Take Control with Torq’s AI Data Transformation

By Torq

July 11, 2025

9 Minute Read

The Role of Data Transformation in Cybersecurity

What is Data Transformation?

Data transformation is the process of converting, cleaning, and manipulating raw data into a structured format that is suitable for analysis or other data processing tasks.

Data transformation is critical for:

Data quality: Removing inconsistencies, duplicates, and errors.
Data compatibility: Ensuring different systems and workflows can use the same data formats.
Data reliability: Maintaining trust in analytics, compliance reporting, and operational decisions.

In a security context, data transformation keeps Hyperautomated workflows running smoothly by ensuring every downstream step receives data in the right format, at the right time. Without it, automation breaks, alerts go unprocessed, and compliance gaps widen.

How Data Transformation Works

Data discovery: Identify and profile raw data sources to understand structure, quality, and required transformations.
Data mapping: Define how fields will be transformed, matched, filtered, joined, and aggregated for the target system.
Data extraction: Move data from source systems (structured or unstructured) to a staging or target environment.
Code generation & execution: Use SQL, Python, or transformation tools to convert raw data into analytics-ready formats, running on a set schedule.
Review: Validate transformation accuracy, completeness, and alignment with business requirements.
Sending: Deliver transformed, structured data to its final destination, such as a data warehouse or analytics platform.

ETL and ELT in Data Transformation

In data engineering, ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are proven methodologies for shaping and preparing information. ETL transforms data before loading it into a data warehouse, while ELT loads raw data first and performs transformation inside the warehouse. Both approaches are designed to ensure clean, structured, and trustworthy data for analytics, reporting, and compliance.

Types of Data Transformation

The main types of data transformation used in security automation include:

Aggregation: Summarizing multiple data points (e.g., calculating the average CVSS score).
Anonymization: Obfuscating personal information to protect sensitive data, essential for compliance with regulations like GDPR and HIPAA.
Filtering: Selecting only the most relevant records (e.g., isolating high-severity vulnerabilities).
Flattening: Converting nested or hierarchical data (such as JSON) into a flat, single-level table format so fields are directly accessible for querying, filtering, aggregation, and joining without complex parsing.
Conditional Logic: Applying predefined rules to determine which data proceeds through the workflow.
Data Cleansing: Removing invalid, duplicate, or incomplete data to improve accuracy.
Data Enrichment: Enhancing records with intelligence from external threat feeds or authoritative databases.

Whether you’re extracting key attributes from JSON logs or merging disparate datasets, these transformation types ensure raw data becomes structured and usable intelligence.

How AI Data Transformation Accelerates Security and Compliance

AI data transformation automates complex processes such as converting formats, improving data integrity, and enhancing data observability. AI significantly speeds up compliance reporting, streamlines incident response, and provides richer, actionable data for security analytics.

Torq’s AI Data Transformation translates natural language into precise data commands, making these sophisticated tasks accessible to all users. Torq’s AI Data Transformation brings automation and intelligence to the process along with:

Customizability: Edit or rewrite any command to suit your needs.
Testability and reproducibility: Test transformations and validate results for precise control.
Flexibility: Easily tweak transformations without disrupting your workflow.
Visibility: See prompts, code, and results at every step — zero guesswork.

While other solutions leave you in the dark, using monolithic parsing that makes it challenging to edit or troubleshoot, Torq keeps you in control through micro-transformations. Every transformation in Torq is testable, customizable, and modifiable with just a click, ensuring your automation runs precisely as intended.

For security teams, this means faster threat enrichment, streamlined compliance reporting, and better data lineage tracking — all with built-in data privacy compliance.

Getting Started with Torq’s AI Data Transformation Operator

The Torq AI Data Transformation Operator is a workflow step that lets you manipulate JSON data inside Torq without needing deep programming skills. It combines AI-powered natural language prompts with deterministic JSON processing using JQ, a high-performance JSON transformation language. AI helps you write transformations in plain language, then Torq converts them into JQ commands that execute consistently.

How It Works

Input your data: Pass JSON from a previous workflow step or paste it directly into the operator.
Describe your transformation in plain language. For example:
- “Extract email, department, and action from each entry.”
- “Remove results where department is equal to Engineering.”
- “Group by department and count actions.”
AI converts your prompt to JQ: The operator generates JQ code from your instructions. The AI step ends here; the deterministic JQ engine handles the actual execution.
Chain multiple instructions: You can stack transformations — extraction, filtering, aggregation, string manipulation — all in one operator, with each step feeding into the next.
Preview and adjust: See the output for each step before finalizing, and tweak the natural language instructions or the generated JQ directly.
Save and reuse: If you create a transformation you’ll need often, you can save it as a custom step and reuse it across workflows or even share it across workspaces.

What You Can Do With It

The Data Transformation Operator supports a wide range of operations:

Mapping and extraction (pull only the fields you care about)
Renaming keys
Converting data types
Filtering and sorting
Conditional logic (if/else rules)
Math functions (averages, sums, etc.)
String manipulation (splitting, regex)
Restructuring JSON formats

Example Prompts

Need ideas? Here are a few natural language prompts and the associated JQ commands the Data Transformation operator could generate.

Natural Language Prompt	AI Translated JQ Command	Security Impact
“Extract all high severity vulnerabilities”	.vulnerabilities[] \| select(.severity == “high”)	Quickly prioritize critical security threats
“Group alerts by source IP”	group_by(.source_ip)	Identify potential attack patterns or compromised assets
“Calculate the average CVSS score”	[.[].cvss_score] \| add / length	Assess the overall vulnerability landscape

In security workflows, raw alerts and logs often come in messy, verbose JSON. The AI Data Transformation Operator lets you clean, normalize, and reformat that data on the fly, so the next steps in your workflow — whether enrichment, correlation, or reporting — get exactly the data they need in the right shape.

Torq Use Cases: Real-World AI Data Transformation in Security Operations

1. Normalizing SIEM Alerts Before AI Analysis

Challenge: SIEM alerts arrive in varied JSON formats depending on the source (cloud, endpoint, identity). Some include deeply nested keys or inconsistent field names.

Transformation:

Extract only relevant fields (timestamp, src_ip, dst_ip, event_type, username).
Rename fields for consistency (dst_ip → Destination IP).
Convert timestamps into ISO 8601 for uniformity.

Outcome: Socrates, the AI SOC Analyst, receives a clean, uniform alert format for faster, more accurate triage.

2. Filtering Out Benign Events in EDR Logs

Challenge: EDR telemetry is high-volume, and not every event is actionable (e.g., routine system updates).

Transformation:

Filter out entries where process_name equals known benign processes (e.g., svchost.exe in a non-suspicious path).
Keep only events matching defined high-risk criteria (e.g., unsigned binaries, rare parent processes).

Outcome: Reduces noise before enrichment, allowing workflows to trigger only on meaningful events.

Challenge: IAM tools generate individual failed login events, making it hard to see patterns.

Transformation:

Group events by username and source_ip.
Count the number of failed attempts per user per IP within a set timeframe.
Output only users exceeding a defined threshold.

Outcome: Aggregated insight triggers an automated account lockout or SOC escalation.

4. Enriching IOC Data Before Threat Hunting

Challenge: Incoming threat intelligence feeds may contain minimal metadata on indicators.

Transformation:

Attach GeoIP data for IP addresses.
Add WHOIS registration details for domains.
Convert lists into an array of {indicator_type, value, source, risk_score} objects.

Outcome: Analysts and automation workflows have full context without additional lookups.

6. Preparing Audit Logs for Compliance Reporting

Challenge: Audit logs contain extra data that auditors don’t need, making reports bulky.

Transformation:

Remove debug and low-value keys.
Sort events chronologically.
Output as a simplified JSON or CSV format matching compliance templates.

Outcome: Audit-ready reports generated instantly without manual editing.

These examples show how Torq’s AI Data Transformation Operator turns messy and inconsistent security data into clean, actionable intelligence that feeds directly into AI analysis, automation workflows, and case management.

Choosing the Right Data Transformation Tools and Software

Selecting the right data transformation software is critical for ensuring your workflows remain efficient, compliant, and adaptable as your organization’s needs evolve. When evaluating options, consider the following criteria:

Ease of use and no-code or AI-driven functionality: Look for platforms offering intuitive interfaces and visual or AI-generated workflow builders so technical and non-technical users can wrangle complex data without writing scripts. This reduces engineering bottlenecks and speeds up deployment.
Integration capabilities: Your data transformation tool should connect seamlessly with your existing security stack (SIEM, SOAR, EDR, threat intelligence feeds) and compliance systems (GRC, audit platforms). Native connectors and API support ensure smooth data integration across multiple environments.
Scalability: As data volumes grow, especially in large enterprise and SOC environments, the platform must handle high-throughput processing without latency issues. Real-time or near-real-time transformation capabilities are essential for automation-driven incident response.
Customizability and flexibility: Every organization has unique data mapping, aggregation, and enrichment needs. A robust platform allows you to tailor transformation logic, apply conditional rules, and reuse transformation templates without disrupting other workflows.
Data governance and compliance support: Choose a solution that offers data lineage tracking, audit logs, and privacy controls to meet data privacy compliance regulations like GDPR, CCPA, and industry-specific standards.

Why Torq Stands Out

Torq’s AI Data Transformation capabilities meet — and exceed — these criteria:

No-code AI workflows: Transform complex JSON or other structured data using plain-language prompts automatically converted into precise JQ commands.
Extensive integrations: 1,000+ prebuilt connectors for security, IT, and compliance tools.
Enterprise-scale performance: Designed to handle large-scale, real-time data transformations without performance degradation.
Full visibility and governance: Every transformation is testable, traceable, and compliant with your data governance policies.

Embrace AI-Driven Data Transformation

In a world where data flows faster and threats evolve by the minute, transforming raw, fragmented information into trusted, actionable intelligence is a competitive advantage. Torq’s AI Data Transformation delivers that capability, combining speed, compliance, and control in a no-code platform that works at enterprise scale. From unifying multi-source security alerts to streamlining compliance reporting, Torq ensures your workflows are reliable, transparent, and ready for whatever comes next.

See the difference for yourself. Request a personalized demo of Torq’s AI Data Transformation today and start turning your data into a decisive asset.

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FAQs

What is data transformation vs. AI data transformation?

Data transformation is the process of converting raw data from one format or structure into another to make it clean, consistent, and usable for analysis, storage, or automation. It typically involves tasks like data cleansing, mapping, aggregation, and enrichment.

AI data transformation uses artificial intelligence — often with natural language processing (NLP) — to automate these steps. Instead of manually writing scripts or queries, users can describe the desired transformation in plain language, and the AI generates the logic, executes it, and allows for easy customization. This speeds up the process, reduces technical barriers, and maintains accuracy and compliance.

What is a data warehouse vs. data lake?

A data warehouse stores structured, processed data in a consistent format, optimized for fast querying, analytics, and compliance reporting.

A data lake stores raw, unprocessed data — including structured, semi-structured, and unstructured formats — for flexible exploration, large-scale storage, and future processing.

Organizations often use both: the data lake for cost-effective retention of all data, and the data warehouse for ready-to-use insights that power day-to-day decision-making.

Why is data transformation important?

Data transformation is essential because it:

Improves data quality by removing errors, duplicates, and inconsistencies.
Ensures compatibility between different tools, systems, and formats.
Supports compliance by enabling privacy controls, audit trails, and data lineage tracking.
Enables better decisions by ensuring analytics, automation, and reporting run on reliable, well-structured data.
Speeds up workflows by making data ready for automation and integration without manual intervention.

In security operations, data transformation ensures that alerts, logs, and intelligence feeds can flow seamlessly into detection, investigation, and response workflows.

What is the difference between ELT and ETL?

ETL (Extract, Transform, Load): Data is extracted from sources, transformed into the desired format, and then loaded into a data warehouse or destination system. This is ideal when you need consistent, cleaned data before it’s stored.

ELT (Extract, Load, Transform): Data is extracted and loaded into the warehouse first, then transformed inside that environment. This approach is useful when storage is cheap and you want flexibility to transform data on demand.

Both approaches have their place, and modern AI data transformation tools like Torq can operate effectively in either ETL or ELT pipelines.

What are examples of data transformation?

Common examples include:

Format conversion: Converting XML to JSON.
Data mapping: Aligning “src_ip” and “source_address” fields into a unified “source_ip” field.
Filtering: Selecting only high-severity vulnerabilities from a dataset.
Aggregation: Grouping alerts by source IP or calculating average CVSS scores.
Enrichment: Adding threat intelligence data (e.g., IP reputation) to security alerts.
Data cleansing: Removing duplicate log entries or fixing malformed timestamps

Tired of Security Alert Fatigue? Stop Burnout with Hyperautomation

By Torq

July 9, 2025

10 Minute Read

Every day, analysts are buried under a mountain of low-value and often meaningless alerts. And they’re expected to triage, investigate, prioritize, and respond to all of them — faster, better, and with fewer people. With this comes cybersecurity alert fatigue, which can lead to missed threats, slower response times, and SOC analyst burnout.

The good news is that SOC analysts don’t have to live like this anymore. Not if you have the right kind of AI working for you. This blog explores what security alert fatigue is, the causes, and how agentic AI can kill your SOC alert fatigue.

What is Alert Fatigue?

Cybersecurity Alert Fatigue

Alert fatigue in cybersecurity refers to the desensitization and exhaustion experienced by security analysts when they are overwhelmed by a high volume of security alerts, many of which are false positives or low-priority events. This can lead to missed or ignored true threats, potentially causing significant security incidents and breaches.

More than half of security teams say false positives are a huge problem, and 62.5% are overwhelmed by sheer data volume. Without effective triage or prioritization, it becomes harder to distinguish real threats from background noise. This leads to slower detection and response, missed incidents, and higher stress on already-stretched SOC teams, which in turn increases risk to the business.

What Causes Cybersecurity Alert Fatigue?

Alert fatigue is the result of too many notifications with too little value. And it’s a problem that only gets worse as security environments become more complex. Here’s what’s driving it.

Excessive False Positives

False positives occur when security systems incorrectly flag benign events as threats. SOC teams inundated with false positives quickly become overwhelmed and stop trusting the alerts altogether. A recent study indicated that more than half of security alerts are false positives, making analysts skeptical about their legitimacy.

Poorly Tuned Detection Rules

Security monitoring tools like SIEM and SOAR platforms rely on detection rules to trigger alerts. When these rules are not properly tuned or regularly updated, they generate an overwhelming volume of irrelevant alerts, contributing significantly to SIEM alert fatigue and SOAR alert fatigue.

Lack of Context in Alerts

Without context, analysts spend valuable time manually investigating alerts to determine their relevance and severity. Contextual information, such as user details, historical activity, and threat intelligence, is essential for quick decision-making — yet many systems fail to provide it.

Manual Triage Processes

Manually sorting through thousands of daily alerts to decide which ones require attention is tedious and error-prone. Human analysts have limits on processing speed and focus, leading to mistakes, missed threats, and inevitable burnout.

Human Limits in Processing Volume and Urgency

Human cognition has inherent limitations. When faced with a high volume of urgent tasks, analysts inevitably experience exhaustion, become less effective, and experience reduced productivity, exacerbating overall security team burnout.

Legacy SOAR

Legacy SOAR is the #1 driver of SOC alert fatigue. It’s a rigid model that treats every alert like a five-alarm fire. It floods analysts with noise, drowns them in contextless data, and racks up costs with every added integration. And because most legacy SOAR platforms are stuck on-prem, they can’t scale or flex with today’s modern security environments.

The Cost of Alert Fatigue in Cybersecurity

Missed vulnerabilities, delayed incident response: When analysts become numb to the constant flood of alerts, critical incidents can slip through unnoticed. Missed threats or delayed responses increase the likelihood of successful cyberattacks, leading to data breaches or significant operational disruptions.

Burned-out analysts and high turnover: Continuous exposure to high stress and repetitive tasks results in analyst burnout. Studies indicate that more than 70% of SOC analysts report burnout, driving skilled talent away and compounding the cybersecurity skills shortage.

Diminished trust in security systems: When false alarms dominate, analysts lose faith in their tools and processes. This lack of trust can lead to negligence or poor decision-making, ultimately undermining your entire cybersecurity posture.

Increased exposure to threats: Ignoring genuine alerts due to fatigue directly translates to higher vulnerability to cyber threats. Attackers exploit this weakness, capitalizing on diminished responsiveness to launch successful attacks.

Wasted resources: Teams overwhelmed by junk alerts often require more headcount. That’s expensive and inefficient.

Reputation damage: When a preventable breach hits the headlines, the fallout can be massive.

Legal and compliance issues: Missed threats can turn into breaches. Breaches mean SEC reporting, fines, investigations, and answering a whole lot of questions.

The average cost of a data breach was $4.9M in 2024, a 10% increase year over year. On the flip side, organizations that fully embraced security AI and automation saved an average of $2.2M compared to those that didn’t, according to IBM.

How Automation Helps You Beat Alert Fatigue

Security automation has become an essential solution for SOC teams to significantly reduce cybersecurity alert fatigue. Here’s how automation addresses the core issues.

Alert enrichment at scale: Automation enriches alerts with relevant context automatically, including threat intelligence data, historical user behavior, and asset criticality, enabling rapid and informed decisions.

Correlation and deduplication: Automation tools correlate related alerts and remove duplicates, drastically reducing noise. Analysts receive fewer but more comprehensive and meaningful incidents, improving efficiency and accuracy.

Routing to the right responder: Automated systems ensure alerts reach the appropriate analyst based on expertise, urgency, or resource availability. This eliminates delays in assignment, balances resource utilization, and improves team responsiveness.

Automated remediation of low-risk threats: Remediating low-risk incidents autonomously significantly reduces repetitive tasks. This allows analysts to prioritize their time and attention on high-severity threats.

Feedback loops for smarter alerting: AI-driven automated systems can learn from past incidents, continuously refining detection rules and processes to reduce false positives and enhance accuracy, minimizing future alert fatigue.

How To Combat Alert Fatigue

While automation is the key solution, here are other best practices your SOC team can implement to reduce alert fatigue further:

Regular optimization: Routinely updating detection rules can somewhat reduce irrelevant alerts.
Prioritization strategies: Clearly define which alerts matter most based on business risk and prioritize accordingly.
Enhanced alert context: Invest in tools providing contextual intelligence so analysts quickly understand the nature of each alert.
Regular training and support: Ensure your team has access to continuous education and training, reinforcing resilience and reducing burnout.
Centralized management: Consolidate alerts into a single case management platform to streamline workflows and reduce duplication.

5 Benefits of Automating Cybersecurity Alert Triage

Automating alert triage doesn’t just address fatigue; it transforms your entire security operation.

80% fewer alerts reaching human analysts: Automation filters out irrelevant alerts, dramatically decreasing the number of notifications analysts need to review, significantly reducing cybersecurity fatigue.
Faster time to detect and respond (MTTD/MTTR): Automation reduces both mean time to detect (MTTD) and mean time to respond (MTTR), allowing analysts to act swiftly and decisively when genuine threats appear.
Reduced analyst burnout and turnover: By offloading repetitive tasks, automation allows analysts to focus on more engaging, complex issues that require critical thinking, significantly reducing burnout and improving job satisfaction.
Higher confidence in escalated alerts: With fewer false positives and enriched context, analysts have more trust in alerts escalated to them, ensuring quick and effective response.
Measurable reduction in false positives: Automated feedback loops continuously improve detection logic, resulting in fewer unnecessary alerts over time, further reducing security alert overload.

How Torq Can Prevent Cybersecurity Alert Fatigue with Automation

Security teams have always relied on automation to streamline repetitive tasks, but traditional automation still requires substantial human oversight and manual intervention. Hyperautomation, however, elevates security operations to an entirely new level by combining advanced deterministic automations with AI-driven non-deterministic automations for real-time adaptive decision-making capabilities.

Unlike basic automation, which crumbles under the pressure of too many complex alerts, Hyperautomation handles volumes that SOAR and other legacy platforms can’t even come close to. It dynamically filters, enriches, correlates, and aggregates alerts at machine speed, ensuring analysts see what actually matters.

Torq HyperSOC™ takes Hyperautomation a step further by integrating agentic AI — an intelligent system capable of autonomous reasoning, decision-making, and iterative planning — to manage security operations at unprecedented speed and scale. Torq HyperSOC dynamically adapts, picking the most appropriate Hyperautomation workflows based on live data and context, enabling autonomous resolution of complex security issues.

Unlike traditional automation, agentic AI iteratively plans and reasons, adjusting actions based on real-time context. It automatically filters noise, enriches data, correlates related alerts, and resolves low-risk incidents without human intervention.

With agentic AI, Torq has replaced repetition with relevance. Our multi-agent system takes on the tasks that drain analysts most — triage, enrichment, correlation, case summaries, even full remediation—and executes them autonomously. Analysts no longer have to sift through countless meaningless alerts because HyperSOC escalates only those that truly require human attention. That means fewer panicked 2 a.m. Slacks and “Why am I still doing this manually?” moments.

“Torq HyperSOC is the first solution we’ve seen that effectively enables SOC professionals to mitigate issues including alert fatigue, false positives, staff burnout, and attrition.”
IDC: Achieving Machine Speed Detection and Response

Torq HyperSOC achieves:

Up to 95% reduction in alert volume: HyperSOC automatically filters, correlates, and prioritizes alerts, drastically reducing noise for analysts.
Real-time incident remediation: Automates end-to-end response, resolving low-risk threats autonomously without human intervention.
Accelerated mean time to detect and respond (MTTD/MTTR): Identifies and addresses critical security incidents in seconds, minimizing potential damage.
Reduced analyst burnout and improved rroductivity: Offloads repetitive tasks, freeing SOC analysts to focus on high-value activities that require human expertise.

With HyperSOC, SOC analysts can finally shift from constantly firefighting false positives to focusing their expertise on high-impact threats that demand human ingenuity.

Legacy SOAR vs. Torq HyperSOC™: Solving Alert Fatigue

Here’s how Torq HyperSOC™ stacks up compared to legacy SOAR systems when it comes to solving cybersecurity alert fatigue.

Legacy SOAR	Torq HyperSOC
SOC alerts are treated like a five-alarm fire, with no intelligent prioritization	Agentic AI triages and prioritizes alerts with semantic, episodic, and procedural memory
Inflexible, SIEM-dependent pipelines for noise reduction and enrichment	Hyperautomation eliminates SIEM dependency and enriches data on the fly
Manual alert triage leads to SOC burnout and delays	AI-driven triage, investigation, and remediation reduce analyst burden
Rigid, on-prem architecture limits scalability and flexibility	Cloud-native architecture scales effortlessly with your environment
Siloed tools and alerts lack unified context	Multi-agent system correlates alerts into unified incidents with full context
Slower response times due to disconnected systems and workflows	End-to-end automation delivers sub-minute response times
High analyst turnover from alert overload and frustration	AI offloads repetitive work, reducing burnout and improving retention

By taking over the repetitive, time-consuming tasks that drive SOC burnout, agentic AI lets analysts do the work that actually matters. You know, the reason they got into security in the first place.

Hyperautomation is the Answer to Cybersecurity Alert Fatigue

The constant flood of alerts compromises response times, erodes analyst trust, causes burnout, and directly increases your organization’s cyber risk. Without addressing cybersecurity alert fatigue, your security strategy is fundamentally flawed.

Hyperautomation, driven by advanced AI, provides a decisive answer to alert fatigue. By automating routine, repetitive tasks and prioritizing real threats, it drastically enhances SOC efficiency and resilience. Torq’s HyperSOC, with its innovative agentic AI, stands at the forefront of this solution, empowering teams to work smarter, not harder.

Ready to take control of your alerts and eliminate SOC burnout once and for all? Learn how to kill your SOAR.

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What is a Cloud-Native Security Automation Framework? Benefits & Use Cases

By Torq

July 3, 2025

9 Minute Read

This blog explores cloud-native security automation, why traditional methods no longer work in modern cloud-native environments, and how teams can transition from reactive security measures to proactive Hyperautomation. Let’s explore the transformative benefits and essential strategies for implementing cloud security automation effectively.

Cloud-Native Security 101

Before you can automate cloud-native security, you need to understand what makes cloud-native security fundamentally different.

In a cloud-native security model, security is integrated from the start, woven directly into both applications and infrastructure, not tacked on later. It relies on automated controls, DevOps alignment, and security teams equipped to navigate complex, fast-moving environments. The objective is to defend against the unique risks of cloud architectures while maintaining continuous compliance with evolving standards and regulations.

What is Cloud-Native Security?

Cloud-native security is an approach that embeds security into every layer of the cloud-native stack, from infrastructure to application code. Rather than adding security as a bolt-on, it’s integrated into the DevOps lifecycle using automated tools and policies. It addresses the unique risks of dynamic, distributed, and containerized environments.

The concept is structured around the 4Cs of cloud-native security:

Cloud: The foundational infrastructure provided by cloud vendors
Clusters: The orchestration layer (e.g. Kubernetes) or other orchestrators managing containers
Containers: The isolated runtime environments housing applications
Code: The actual application logic and configurations deployed across the stack

Key use cases of cloud-native security include identity management, access control, vulnerability scanning, runtime monitoring, and automated response. Together, these create a holistic, resilient security posture that protects organizations at every level of their cloud infrastructure.

A cloud-native security automation framework is a structured set of technologies, workflows, and best practices that automatically detect, prioritize, and respond to threats across cloud-native environments. It integrates security throughout the cloud stack — cloud, clusters, containers, and code.

Manual Cloud Security is Broken: Key Challenges

Traditional security operations can’t keep up with the pace and scale of cloud-native infrastructure. Human analysts struggle to keep pace with rapidly scaling environments and managing cloud-native applications, making manual methods prone to error and inadequate against modern threats. Here are some critical issues:

Bottlenecks: Manual processes delay threat detection and response, increasing exposure to potential breaches. Each delay amplifies the damage from vulnerabilities that attackers can exploit.
Security team burnout: Analysts overwhelmed with alerts face burnout, lowering efficiency and morale, and increasing the likelihood of missed threats. This persistent stress leads to higher staff turnover and reduces overall team productivity.
Inconsistencies: Manual security procedures are difficult to replicate consistently, causing varied security effectiveness across deployments. This inconsistency can leave critical assets exposed and vulnerable to attacks.
Diagnostic difficulties: Manually correlating events across disparate tools and environments leads to slow and often incomplete investigations. Without automated analytics and correlation, incidents are frequently misunderstood or missed entirely.

Automation addresses these manual shortcomings by significantly increasing efficiency, ensuring consistent and accurate enforcement of security policies, and substantially reducing human error, especially critical in complex and rapidly evolving cloud environments.

5 Benefits of Cloud Security Automation

Cloud security automation addresses these challenges, transforming security operations into proactive, efficient, and scalable processes. Here’s why you should automate everything.

1. More Efficient Operations

Automated processes dramatically speed up threat detection, response, and remediation, reducing operational friction and freeing analysts for strategic tasks. With automation, teams can shift focus from repetitive tasks to strategic, value-adding activities. This is especially crucial in Kubernetes-based, serverless environments where threats move fast.

2. Ensure Compliance

Automation helps consistently enforce security policies, compliance standards, and best practices, ensuring your infrastructure continuously meets regulatory requirements like HIPAA, SOC 2, and PCI-DSS. Automated audit trails and compliance checks further simplify adherence to industry standards.

3. More Accuracy

Automation reduces human error, delivering precise, reliable responses to security vulnerabilities every time. Automated security processes significantly decrease false positives and misconfigurations, improving the reliability of your security operations.

4. More Consistency

Automations ensure standardized security responses, reducing variability and improving overall security posture across every container and workload. This ensures a stable security posture — regardless of scale or complexity.

5. Scalability

Security automation scales seamlessly with your infrastructure, ensuring continuous protection as your organization grows. Automation tools effortlessly handle increased workloads, ensuring continuous security coverage even during rapid scaling.

How to Automate Cloud-Native Security: 12 SOC Use Cases

A cloud-native security automation framework doesn’t just respond to threats; it transforms the way SOCs operate. Below are key use cases that demonstrate how automation accelerates security operations across cloud environments.

1. Identity and Access Management (IAM)

Automate user provisioning, access approvals, and credential rotation across cloud-native applications to minimize manual errors, prevent unauthorized access, and maintain compliance at scale.

2. Automated Threat Hunting

Continuously scan cloud workloads, Kubernetes clusters, and logs for indicators of compromise. Enrich findings with threat intelligence and behavioral analytics to detect and respond to advanced threats proactively.

3. Cloud Security Posture Management (CSPM)

Monitor multi-cloud environments for misconfigurations and policy drift. Automatically trigger remediation workflows that maintain a strong security posture and ensure compliance across dynamic cloud-native infrastructure.

4. Email Security

Integrate with cloud-based email and endpoint platforms to instantly detect phishing campaigns, quarantine malicious messages, and update protection rules, without SOC analyst intervention.

5. Self-Service Chatbots

Deploy chatbots in platforms like Slack or Teams to handle common security tasks, such as password resets or access revocations. Reduce SOC workload while improving speed and user experience.

6. Incident Response Automation

Automatically triage alerts, contain threats, execute auto-remediation, and notify stakeholders. Every step — from detection to documentation — is orchestrated for speed and accuracy across cloud-native systems.

7. Application Security Automation

Integrate with CI/CD pipelines to detect vulnerabilities and misconfigurations early. Automate fixes or escalate issues directly in tools developers already use, enabling secure cloud development without delay.

8. Phishing Detection and Response

Correlate email, endpoint, and identity signals to identify phishing attempts. Automate investigation, response, and user notifications to neutralize threats quickly and consistently.

9. Continuous Vulnerability Management

Scan containers, serverless functions, and cloud-native applications for known risks. Prioritize and remediate vulnerabilities using contextual insights, before attackers can exploit them.

10. Threat Intelligence Enrichment

Automatically enrich findings with threat intel: IP geolocation, known malware hashes, adversary infrastructure, and MITRE ATT&CK mappings. Boost detection fidelity and decision-making confidence.

11. Suspicious User Behavior

In real time, detect anomalous user activity — like impossible logins or privilege escalations. Instantly respond with MFA challenges, session termination, or account lockdown.

12. Sensitive Data Access Controls

Enforce zero trust access controls for critical assets by automating policy checks, alerting on anomalies, and verifying user actions across containerized and multi-cloud environments.

Hyperautomation: The Future of Cloud Security Automation

Looking ahead, cloud security automation will increasingly use AI to enhance detection, reduce false positives, and predict potential threats. AI-driven SOC solutions will automate complex decision-making, streamline compliance, and dynamically adapt security measures, ensuring organizations maintain resilient defenses even as threat landscapes rapidly evolve.

The future of cloud security also involves empowering non-technical stakeholders through SOC automation platforms like Torq. This democratization of security allows everyone to contribute to security practices, fostering a broader organizational security culture.

Torq HyperSOC^TM is a cloud-native security automation tool, offering comprehensive, no-code solutions that enable teams to easily automate their security operations, including advanced container security, efficient management of serverless and microservices, and full integration with CNAPP capabilities.

With Torq’s no-code platform, security teams — and even non-technical stakeholders — can define rules, mitigate threats instantly, and ensure consistent security across complex multi-cloud and hybrid cloud environments, significantly reducing vulnerability risks and enhancing overall security posture.

By leveraging Hyperautomation and agentic AI with Torq, security teams can:

Automatically detect, investigate, and remediate threats across all cloud environments
Streamline identity and access management, CSPM, threat intel enrichment, and more
Orchestrate complex workflows across tools like Wiz, Sweet Security, CrowdStrike, SentinelOne, and AWS
Scale effortlessly across cloud-native applications, multi-cloud, or hybrid environments without code or configuration overhead

Cloud-Native Security Automation in Action: Torq + Wiz

To see a cloud-native security automation framework in action, look no further than the powerful partnership between Torq and Wiz. These two platforms combine seamlessly to provide an end-to-end automation solution purpose-built for securing today’s sprawling cloud environments.

Wiz delivers deep visibility into cloud risk — surfacing everything from misconfigurations to toxic combinations of exposure and permissions. Torq turns that insight into instant, intelligent action. Together, they automate everything from detection to remediation, improving cloud security posture, reducing attack surface, and accelerating response without burdening analysts.

With Torq and Wiz, teams can automatically remediate issues like:

Publicly exposed AWS S3 buckets containing sensitive data: Torq receives alerts from Wiz, validates the bucket’s status, and updates access policies automatically, or routes the issue for human-in-the-loop approval via Slack or Jira.
Unencrypted cloud storage: When Wiz detects a storage bucket with encryption disabled, Torq prompts the bucket owner to enable it or does so automatically, ensuring data in the secure cloud stays secure.
Open SSH access on EC2 instances: Torq instantly correlates alerts, confirms owner identity, and applies remediation by removing the risky access rule or prompting the appropriate user to take action.

Together, Wiz Defend and Torq HyperSOC™ form a powerful defense loop for cloud-native security: Wiz delivers deep visibility and precision threat detection across dynamic environments, while Torq transforms those insights into immediate, intelligent, and fully automated response. It’s the fastest path from detection to resolution, built for the demands of modern multi-cloud, serverless, and container-driven architectures.

Ultimately, Torq and Wiz help organizations move beyond traditional security bottlenecks and into a future of truly autonomous, scalable, and resilient cloud-native operations. They are a cornerstone for any organization looking to build or strengthen a modern cloud-native application security automation framework. Watch the demo here.

Don’t Let Manual Security Hold You Back

Cloud-native environments demand cloud-native security. The only way to keep up with the speed of infrastructure — and the speed of attackers — is to automate everything that can be automated.

Go all in with Torq Hyperautomation.

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Chatbot Automation for Security Teams: Streamline Response and Collaboration

By Torq

July 2, 2025

10 Minute Read

AI-powered security chatbots are transforming the SOC into a faster, more collaborative, and more proactive environment. They don’t just chat; they alert, enrich, and execute. From posting high-fidelity threat notifications the instant they occur, to triggering automated remediation workflows, to centralizing every decision in a shared channel, chatbots act as a real-time command interface for your security stack.

When embedded in platforms like Slack or Microsoft Teams (and powered by a robust automation backbone), chatbot automation becomes the front door to your SOC, accelerating response, eliminating silos, and putting the entire security team on the same page in seconds.

What Is Chatbot Automation?

Chatbot automation uses software bots — often enhanced with artificial intelligence (AI) and natural language processing (NLP) — to execute tasks, provide information, or trigger workflows automatically in response to user input or predefined events.

Automated chatbots vary in sophistication and design.

Rule-based chatbots: Operate on predefined rules and decision trees. They’re quick to deploy and easy to maintain, but limited to the scenarios they’ve been programmed to handle.

Artificial intelligence chatbots: Powered by natural language processing (NLP), these bots understand context, adapt to user input, and can autonomously handle complex requests.

Hybrid chatbots: Blend rule-based logic with AI-driven intelligence. They follow decision trees for routine interactions but switch to AI for nuanced queries and can seamlessly escalate to a human analyst when needed.

How Chatbot Automation Works

Receives an alert or request from a connected system (SIEM, EDR, IAM, etc.) or directly from an analyst in chat.
Understands the command or message using rule-based logic or AI/NLP to interpret intent.
Fetches relevant data from integrated tools (e.g., threat intel feeds, asset inventory, HR systems).
Executes pre-defined playbooks or auto-remediation actions, like disabling an account, quarantining a device, or escalating a ticket.
Confirms completion and logs results directly in the chat thread for transparency and collaboration.
Learns and adapts (if AI-powered) to improve accuracy, speed, and relevance over time.

Torq’s Approach to Chatbot Automation

Torq’s chatbot automation furthers this concept by embedding hybrid chatbot capabilities into the Torq Hyperautomation^TM Platform. This means:

Deterministic precision for critical actions: Pre-approved, policy-driven case management runs instantly for high-trust commands like blocking users, isolating endpoints, or enforcing compliance controls.
AI-powered intelligence for dynamic requests: Through Torq Socrates, the AI SOC Analyst, the chatbot can interpret natural language queries, enrich alerts, and guide analysts with context-aware recommendations.
Direct integration with your stack: With 300+ native integrations, Torq’s chatbot can orchestrate actions across SIEM, SOAR, EDR, IAM, SaaS, and cloud environments, all from Slack, Teams, or other chat platforms.
Built for SOC speed: By keeping analysts in the conversation and automating enrichment, remediation, and documentation, Torq’s AI-powered chatbot reduces MTTR and centralizes collaboration without requiring console-hopping.

With Torq, chat platforms like Slack or Teams become real-time SOC command centers, enabling security teams to detect, investigate, and respond in seconds, directly from the tools they already use every day.

How to Set Up a Security Chatbot with Torq

Integrating AI Chatbots into Slack, Teams, or Other Tools

Torq natively integrates with popular chat applications, making setup quick and seamless.

Connect your chat platform: In the Torq console, select your target app (Slack, Microsoft Teams, etc.) and authorize the integration.
Map to security workflows: Choose which workflows you want to make accessible through chat commands, anything from IP lookups to full remediation playbooks.
Define permissions: Restrict sensitive commands to specific channels, users, or groups to ensure only authorized personnel can execute critical actions.
Go live instantly: Once linked, the bot is ready to execute workflows in real time from your chat interface.

Because the integration leverages Torq’s automation engine, any workflow you build is instantly available via chat, no separate scripting or API work needed.

Customizing Chatbot Commands for Security Workflows

Torq’s no-code/low-code visual editor allows you to design security chatbot commands that match your security team’s exact needs. Examples include:

/torq get-ip-reputation 8.8.8.8: Queries integrated threat intel sources and returns reputation scores instantly.
/torq block-user jane.smith: Disables a user account in Okta, Azure AD, or other IAM systems in seconds.
/torq isolate-endpoint host123: Triggers EDR isolation on a compromised asset.
/torq get-case-status 457: Returns the current investigation status from Torq’s case management.

Each command can be as simple or complex as you like. You can chain multiple actions together, like enriching a domain, checking for internal access logs, and disabling an account, all from one chatbot command.

Real-World Example: Torq’s ChatOps in Action

Imagine this scenario:

Your email security system detects a phishing alert.
Torq automatically posts an alert in your SOC Slack channel:
“Potential phishing detected for user [email protected] – Click for details.”
An analyst replies in the same thread:
/torq get-domain-reputation malicious-link.com
The bot instantly responds with:
“Domain is confirmed malicious. Reputation score: High Risk.”
The analyst executes:
/torq disable-user john.doe
Torq triggers the remediation workflow, disabling the account in Okta, notifying the IAM team, and updating the case in Torq’s system.
Total time to containment: Under two minutes, without leaving Slack.

From Alerts to Actions — All Inside Your Chat Window

By integrating Torq into your chat tools, you:

Reduce context switching: Analysts stay in the same interface while taking action.
Accelerate response time: Critical commands are only a message away.
Enforce consistency: Chatbot commands execute the same workflows every time.
Enable collaboration: All actions are visible in-channel, improving transparency and shared awareness.

How Chatbots Enhance Security Operations

AI-powered chatbots aren’t just another channel for alerts; they reshape how SOCs operate. By embedding automation in familiar chat environments, they:

Improve speed: Analysts can take action without leaving the conversation.
Increase visibility: Every stakeholder sees the same updates in real time.
Enhance collaboration: Security, IT, and DevOps can coordinate instantly during incidents.

A chatbot can notify the team of a phishing attempt, fetch related logs when asked, and even block the malicious domain, all within the same thread. That’s ChatOps for security in action.

Top Use Cases for Automated Chatbots in the SOC

Automated threat detection and alerts: Automated chatbots can serve as the first point of contact for high-priority alerts, eliminating delays between detection and analyst awareness. By integrating directly with SIEMs, EDRs, cloud security tools, or Torq workflows, bots can instantly post curated, high-fidelity alerts into a shared SOC channel.

This means analysts get notified in seconds — not minutes — and can immediately start collaborating on next steps. For example, when a suspicious login from an unusual geo-location is detected, the bot posts the alert along with initial context such as user, source IP, and device type.

Collaborative incident response via ChatOps: An AI chatbot turns your chat platform into an incident war room. Instead of jumping between ticketing systems, consoles, and email, analysts and incident responders coordinate directly in a single thread. They can request enrichment from the bot (e.g., “Get file hash reputation”), pivot to related indicators, or even pull up historical case data. Follow-up actions, such as assigning remediation steps or requesting additional context, happen in real time, with everyone on the same page.

Faster access to threat intelligence and logs: In a traditional SOC, pulling a file hash verdict or log snippet can mean logging into multiple systems, navigating dashboards, and running queries. With a chatbot, the process is as simple as asking: /torq get-file-verdict 123abc…

The bot queries integrated threat intelligence platforms (like VirusTotal, Recorded Future, or internal IOC repositories) and posts the result in seconds. This approach removes the friction of multiple logins and speeds up the enrichment phase of an investigation.

Triggering remediation workflows via chat: The real power of ChatOps comes when a bot isn’t just delivering data — it’s taking direct action. With Torq, a chatbot can trigger pre-approved remediation workflows instantly on command, such as:

Quarantine a suspicious endpoint in EDR
Disable a compromised Okta account
Block a malicious IP at the firewall

Because these workflows are automated, they execute in seconds with full consistency, and permissions can be tightly controlled to prevent misuse.

Creating a record of incident response: Everything that happens in a chatbot conversation is automatically logged in the chat history. This provides a searchable, time-stamped record that can be invaluable during audits, compliance reviews, and post-mortems. SOC managers can quickly reconstruct who did what, when, and why, without chasing down fragmented notes or emails.

Key Benefits of Chatbot Automation in Cybersecurity

24/7 availability and communication: Security doesn’t stop when the SOC is offline. AI-powered Chatbots operate around the clock, instantly notifying the right team members no matter the hour.
Centralized security conversations: Everyone sees the same alerts, context, and decisions in one channel.
Improved visibility and traceability: Every action and decision is captured in chat, creating a verifiable audit trail.
Reduced analyst workload: Bots handle routine lookups, enrichment, and workflow triggers — freeing humans for complex analysis.

Chatbots vs. Traditional Security Tools: What’s the Advantage?

While dashboards and consoles are powerful, they require manual navigation, context-switching, and trained operators. Automated chatbots bring:

Real-time interaction in a familiar interface: Analysts can query threat intel, pull logs, or trigger workflows directly in the chat window they already use for coordination. This eliminates the mental and operational overhead of learning and navigating multiple security consoles.
Faster decision-making with instant context: As soon as it posts a notification, a chatbot can enrich it with relevant details — like reputation scores, related incidents, or asset ownership — giving analysts everything they need to act without hunting for additional information.
Fewer workflow breaks: In traditional SOC workflows, collaboration happens in one tool, investigation in another, and remediation in yet another. With ChatOps, all three occur in the same thread. Context isn’t lost; decisions can be executed immediately while everyone involved stays in sync.

Security chatbots aren’t here to replace your SIEM, or EDR. Those platforms provide the detection logic, deep investigation tools, and specialized responses your SOC needs. Intelligent chatbots act as the connective tissue, making these tools more responsive, accessible, and collaborative by bringing their capabilities into a single, shared interface.

The result is faster MTTR, higher SOC throughput, and less analyst fatigue, without sacrificing the depth and power of your existing security stack.

The Case for ChatOps-Driven Security

Automated chatbots are a strategic interface for modern SOC automation. By combining AI-powered decision-making with automated workflows inside your team’s everyday chat tools, security teams gain a real-time, collaborative command center that eliminates silos and accelerates incident response.

With conversational AI and artificial intelligence–driven logic, Torq’s chatbots can interpret text commands, fetch context from threat intelligence, trigger remediations instantly, and keep the conversation flowing across all stakeholders, without requiring console-hopping or manual lookups. Whether it’s powered by machine learning for adaptive insights or rule-based precision for deterministic actions, the result is faster MTTR, reduced analyst fatigue, and higher operational consistency.

As threats accelerate, the speed of your response matters more than ever. See how Torq Hyperautomation brings instant, intelligent, and collaborative security operations to your team.

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How to Respond to a Cybersecurity Incident with Hyperautomation

By Torq

June 26, 2025

10 Minute Read

The question isn’t whether your organization will face a cybersecurity incident — it’s how soon, how often, and how prepared your team is when it happens. From ransomware campaigns to zero-day exploits buried deep in supply chains, today’s most damaging breaches don’t happen in isolation; they build on years of patterns, mistakes, and missed signals.

This blog unpacks the most common cybersecurity incidents, examines real-world attacks that redefined the last decade, and shows how forward-thinking security teams are adapting their response strategies.

Understanding Cybersecurity Incidents

Cybersecurity incidents, as defined by NIST, involve violations of explicit or implied security policies. These events can range from unauthorized system access to the disruption of critical infrastructure.

The terms cyber incident and cyberattack are often used interchangeably, but they have distinct meanings, especially in the context of incident response and security operations.

Cyber Incidents

A cyber incident refers to any event that jeopardizes the confidentiality, integrity, or availability of information or systems, whether malicious or accidental. It includes both confirmed attacks and suspicious activity that could indicate a breach.

Examples of cyber incidents include:

Unauthorized login attempts
Malware detections that were blocked before causing harm
A misconfigured firewall exposing internal systems
Lost or stolen devices containing sensitive data

Not all cyber incidents are attacks. Some may be policy violations, human error, or internal system failures.

Cyber Attacks

A cyberattack is a deliberate, malicious action carried out by cybercriminals to compromise or disrupt digital systems, steal data, or cause damage.

Examples of cyberattacks include:

Ransomware encrypting critical business data
Phishing emails designed to steal credentials
Denial-of-Service (DoS) attacks crashing a public website
Nation-state actors breaching a government network

Every cyberattack is a cyber incident, but not every incident is an attack.

Best Practices for Cybersecurity Incident Readiness

To enhance resilience against cyber threats, organizations in any sector can adopt the following measures.

Threat Prevention

Enforce the principle of least privilege to ensure users have only the access they need.
Implement intelligent multi-factor authentication (MFA) across all critical systems.
Regularly update and patch operating systems, applications, and firmware.
Conduct routine risk assessments to identify and address vulnerabilities proactively.
Perform regular penetration testing to evaluate defenses against real-world attack scenarios.
Apply a Zero Trust architecture for network access, validating users and devices continuously.
Deliver ongoing security awareness training, emphasizing phishing, social engineering, and how to report suspicious behavior.

Threat Detection and Response

Deploy advanced threat detection tools such as endpoint detection and response (EDR) and intrusion detection systems (IDS).
Monitor system and network activity, using analytics and behavioral baselines to spot anomalies.
Use real-time threat intelligence feeds to stay ahead of new attack tactics and tools.
Collaborate with cybersecurity firms, law enforcement, and government entities to exchange intelligence and response strategies.

Incident Recovery

Establish and routinely test a comprehensive incident response plan that defines roles, processes, and escalation paths.
Regularly back up critical data and systems.
Store backups in secure, off-site, and immutable storage locations, and validate recovery processes through routine testing.

By following these cybersecurity best practices, organizations can reduce their exposure to cybersecurity incidents and respond when incidents occur. When paired with security Hyperautomation, these efforts become exponentially more effective.

Torq Hyperautomation™ enables teams to operationalize these best practices at scale — automating access control enforcement, triggering dynamic responses based on threat signals, and ensuring every stage of prevention, detection, and recovery is seamlessly orchestrated. As threats grow more sophisticated, Hyperautomation ensures your defenses evolve just as quickly, turning manual playbooks into intelligent workflows that keep your organization ahead of cyber risk.

How Hyperautomation Prevents Cybersecurity Incidents

Hyperautomation is a critical strategy in preventing cybersecurity incidents before they escalate. By orchestrating and automating security processes end-to-end, Torq Hyperautomation enables security teams to detect, assess, and act on threats at unprecedented speed and scale.

Integrate your stack: Torq Hyperautomation connects disparate tools and data sources to deliver unified, context-rich insights. For example, identity and access management (IAM) alerts can automatically correlate with endpoint and network telemetry to identify anomalous behavior that may signal credential misuse or lateral movement. This level of cross-platform analysis helps detect threats earlier in the kill chain, allowing preventive action before damage occurs.

Enforce policies consistently: Hyperautomation also excels at enforcing consistent security hygiene. It can automatically revoke stale user access, quarantine suspicious endpoints, and enforce policy-based remediation based on dynamic risk scoring. These proactive workflows close common security gaps — such as misconfigured permissions or unpatched vulnerabilities — that often lead to major cyberattacks.

Boost efficiency and scalability: Because Hyperautomation can scale across every phase of the incident lifecycle, from detection to response and resolution, it also reduces alert fatigue and ensures that no critical signals are missed. Routine tasks like enrichment, user verification, or SIEM query execution are handled in real time, allowing analysts to focus on more strategic decision-making.

Improve defenses over time: Additionally, by embedding intelligence into every step, Hyperautomation allows security teams to continuously learn from past incidents. Insights from previous breaches can be operationalized instantly, whether it’s to update playbooks, block known IOCs, or adapt response logic dynamically based on threat actor behavior.

In today’s threat environment, Hyperautomation enables SOCs to stay ahead of adversaries, minimize dwell time, and prevent cybersecurity incidents before they disrupt the business.

How Hyperautomation Helps Respond to Cybersecurity Incidents

Should a cyber incident occur, every second counts. Torq enables security teams to move at machine speed by automatically ingesting alerts from any source — SIEMs, EDRs, CSPM tools, identity platforms, and more — and instantly launching enriched, prioritized investigations. These cases are automatically populated with relevant context: log data, user metadata, threat intelligence, and past activity history. This removes the need for analysts to pivot between platforms or perform repetitive lookups manually.

Streamline case management: AI-driven case management is at the heart of Torq’s incident response capabilities. As soon as a case is created, it’s enriched with dynamic summaries, root cause analysis, and recommended next steps, making it easier for SOC teams to determine what’s happening and how to respond. This reduces dwell time, accelerates triage, and ensures consistency across cases, even when dealing with the most complex attacks.

Automate incident response: Torq’s no-code/low-code interface allows SecOps teams to rapidly design incident response workflows. Whether it’s automatically isolating endpoints, rotating credentials, disabling compromised accounts, or notifying incident response teams via Slack or ServiceNow, Torq makes these responses seamless and repeatable. Organizations can integrate any tool into the workflow and create logic that adapts based on threat severity, incident type, or asset sensitivity.

Move from reactive to proactive defense: Additionally, Torq helps identify patterns across recurring incidents. By aggregating telemetry across tools and standardizing responses through automation, teams can quickly recognize if a new event is part of a broader campaign or an emerging threat vector. This enables proactive defense, rather than reactive firefighting.

Measure and improve security performance: Security leaders also benefit from full auditability and metrics tracking, making demonstrate performance improvements, compliance, and ROI easier. Torq’s dashboards provide visibility into case closure time, response time, alert volume, and automation coverage, empowering CISOs and SOC directors to continuously refine their strategy.

With Torq, teams respond intelligently and at scale, making it a critical partner in any modern SOC’s incident response strategy.

Recent Cybersecurity Incidents & Their Impact

MOVEit Attack (2023)

The MOVEit attack exploited vulnerabilities in the MOVEit Transfer software, compromising sensitive data from hundreds of global organizations, including government and private entities. This incident highlights the risks of third-party software vulnerabilities, prompting increased scrutiny in software supply chains.

Colonial Pipeline Ransomware Attack (2021)

One of the most famous ransomware attacks in recent history, the Colonial Pipeline incident disrupted fuel supplies across the U.S. East Coast. Attackers leveraged ransomware to encrypt critical operational data, demanding millions in ransom. The incident exposed vulnerabilities in critical infrastructure, highlighting the need for robust cybersecurity and incident response planning.

SolarWinds Supply Chain Attack (2020)

The SolarWinds incident involved malicious actors inserting a backdoor into the software update process, affecting thousands of customers, including U.S. government agencies and Fortune 500 companies. This highlighted supply chain risks, significantly changing how organizations vet and monitor third-party vendors.

Facebook Data Breach (2021)

Personal data from approximately 533 million Facebook users was leaked due to security flaws. This breach highlighted data privacy issues and the challenges even the most technologically advanced companies face in securing user information.

Historical List of Major Cybersecurity Incidents

Google (2009): Operation Aurora targeted intellectual property and raised awareness about state-sponsored cyber espionage. The attackers exploited Internet Explorer vulnerabilities and successfully breached multiple U.S. tech and defense companies, prompting a major shift in enterprise threat modeling.
Stuxnet (2010): A highly sophisticated malware attack believed to target Iranian nuclear facilities, demonstrating cyber capabilities in physical damage. It was the first known cyber weapon to cause real-world infrastructure destruction, redefining cyber warfare’s role in international conflict.
Yahoo Breach (2013–2014): Affected approximately 3 billion user accounts, highlighting massive data management vulnerabilities. The breach went undetected for years, raising global scrutiny around breach disclosure practices and user data security.
Sony Pictures (2014): Hackers leaked sensitive internal data, causing operational disruption and highlighting geopolitical cyber threats. The attack was attributed to North Korea and is considered a pivotal moment in the rise of politically motivated cyber aggression.
OPM Breach (2015): Affected 21.5 million individuals, significantly impacting U.S. federal personnel records. Stolen data included detailed background checks, making it one of the most damaging breaches for national security and espionage risk.
Equifax Breach (2017): Compromised sensitive personal data of 147 million consumers, underscoring the importance of patch management. The breach led to congressional hearings, executive resignations, and a $700 million settlement, emphasizing the consequences of poor cyber hygiene.
Marriott Data Breach (2018): Impacted approximately 500 million customers, exposing weaknesses in data security within hospitality services. The breach persisted for years unnoticed and included passport numbers and travel details, sparking global concerns about long-term surveillance and identity theft.

Revolutionizing Incident Response with Hyperautomation

As the volume, velocity, and complexity of cybersecurity incidents continue to grow, organizations must move beyond traditional detection and manual response. The stakes have never been higher, from phishing emails and ransomware to nation-state-backed supply chain compromises.

Understanding the anatomy of past cyber attacks is essential, but building future-proof defenses requires automation, intelligence, and speed. Torq’s Hyperautomation platform empowers modern SOC teams to stay ahead of threats by preventing incidents before they escalate and responding instantly if they do.

By unifying context, automating response, and eliminating manual bottlenecks, Torq helps organizations transform cybersecurity incidents from existential risks into manageable, repeatable operations. Security resilience starts with action, and Torq makes that action faster, smarter, and more effective than ever.

Explore how Torq can enhance your cyber resilience, ensuring you’re not only aware of cybersecurity incidents but equipped to combat them effectively with speed, efficiency, and control.

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How Torq and Wiz Power End-to-End Cloud Threat Detection and Response

By Bob Boyle

June 24, 2025

6 Minute Read

Watch the on demand demo

How Wiz Defend Alerts Flow into Torq

Modern cloud environments are dynamic and often opaque to traditional security tools. Wiz changes that by collecting and correlating rich telemetry across the entire cloud stack, not just from infrastructure and workloads, but from identities, repositories, runtime signals, and more.

What makes this powerful isn’t just the data itself — it’s how Wiz transforms that data into high-fidelity alerts that are seamlessly fed into Torq for immediate action.

How Wiz Finds and Detects Cloud Threats

Wiz begins by ingesting telemetry from multiple sources across your cloud footprint, including:

Cloud-native logs: AWS CloudTrail, S3 data events, Azure Diagnostic Logs, and GCP Audit Logs
Identity activity: Okta, cloud IAM policies, and role assumptions
DevOps and Kubernetes tools: GitHub, container registries, and CI/CD pipelines
Runtime sensors for visibility into container and serverless workload behavior

But rather than alerting on every anomalous signal or potentially malicious indicator, Wiz applies correlation logic that groups related signals into what it calls a Wiz Threat — a complete, narrative alert that reflects an unfolding cloud attack path.

Together, these detections are stitched into one high-confidence alert that captures both the technical indicators and the business risk, allowing SOC teams to move faster with greater certainty.

Prioritized, Correlated, and Automated Cloud Threat Detection

Each Wiz Threat is not just a set of log events — it’s a structured object that includes:

Detection metadata: source, time, cloud account, and service, region
Linked findings: secrets, misconfigurations, and vulnerabilities
Enriched security context: tags, asset owners, MITRE ATT&CK tactics, and runtime behavior
Calculated risk severity based on business impact and adversary activity

This comprehensive data is packaged and passed to Torq HyperSOC via webhook or API integration.

What Gets Sent to Torq

Threat name and summary
Affected cloud assets
Event timeline and sequence
MITRE ATT&CK classification
Associated user identities and network exposure
Recommendations from Wiz’s threat intelligence team

How Socrates Automates and Orchestrates the Cloud Threat Response

Once inside Torq, the Wiz Threat becomes a case, a centralized workspace where Torq’s AI SOC Analyst, Socrates, takes over. Here’s how the end-to-end workflow looks.

Step 1: The Wiz Alert Becomes a Torq Case

When the alert lands in Torq, a new case is created and populated with structured context from Wiz Defend. Analysts are immediately presented with a dynamic AI-generated case summary, which adapts in real-time as new signals, observables, or comments are added.

Step 2: Socrates Begins Enrichment and Investigation

With the case live, Socrates, Torq’s AI SOC Analyst, steps in as the first responder. Socrates parses the detection, extracts IPs, hashes, URLs, and related indicators, and enriches them using your chosen threat intelligence providers (e.g., VirusTotal, AlienVault, Recorded Future). Threat enrichment happens within seconds, and the insights are automatically written back into the case file.

Then, Socrates dynamically identifies asset owners based on tags, CMDB entries, or environment metadata — instantly resolving ownership questions that traditionally slow down response times in cloud environments.

Next, Socrates builds a response plan. Using the MITRE ATT&CK tactics mapped from the Wiz alert and a library of security procedures, it proposes a remediation workflow customized to the threat and environment, whether it’s privilege misuse, misconfigurations, or lateral movement attempts.

Step 3: Autonomous Action and Analyst Escalation (If Needed)

Now the case enters automated execution. Socrates follows a runbook tailored to the case type, executing actions such as:

Collecting additional context from Wiz, AWS, and container workloads
Mapping and enriching security groups and cloud configurations
Identifying blast radius and lateral exposure for potential data exfiltration
Capturing a forensic memory dump of the asset to AWS S3
Notifying asset owners and cloud security teams via Slack or Jira
Removing public IP associations from exposed assets
Tagging the case with relevant MITRE ATT&CK TTPs

For cloud threats meeting certain criteria, Socrates can auto-remediate the incident entirely, containing the issue before a human even sees the alert. For more critical threats, the case is escalated to a human analyst with full context, including recommended next steps and suggested actions.

Step 4: Automatic Post-Incident Reporting

Once the threat has been handled, Socrates generates a full post-incident report that includes:

A summary of the detection and context
Enrichment and investigation details
The full remediation timeline
Root cause analysis of vulnerabilities or misconfigurations
Blast radius insights
Analyst performance scoring (if applicable)
Recommendations for continued improvement of cloud security posture

This report is stored as a PDF attachment to the case and accessible as a structured note, ready for audits, compliance, and continuous SOC training.

As the final touch, Torq automatically tags the case with MITRE ATT&CK TTPs used in the attack. This enables teams to build a MITRE ATT&CK heatmap across Wiz, Torq, and other detection sources, giving CISOs and threat hunters strategic visibility into adversary behavior across cloud and hybrid infrastructure.

Why Torq is the Definitive Automation Tool for Your Wiz Environment

Torq is uniquely built to provide the critical automation layer needed to bridge detection to action with unparalleled efficiency and accuracy. Unlike generic automation tools or manual scripting, Torq understands Wiz alerts natively. As soon as Wiz identifies a high-confidence threat, Torq’s built-in workflows are triggered automatically without extra scripting, manual integrations, or complicated setup.

With Torq, Wiz Defend customers experience immediate threat containment asSocrates enriches alerts, performs investigations, and resolves threats independently. This fully autonomous approach significantly reduces MTTR and frees your analysts to focus on complex scenarios and overall SOC strategy.

Torq doesn’t just enhance Wiz cloud alerts — it completes them.

Wiz and Torq: Your Ultimate Cheat Code for Cloud Security Operations

Cloud threat detection is just half the battle. Together, Wiz and Torq close the loop by coupling high-fidelity detections with instant, automated, and intelligent response. By bridging the gap between detection and action, security teams can finally stay ahead of rapidly evolving cloud threats, reduce alert fatigue, and accelerate remediation.

The integration of Wiz Defend’s rich, correlated telemetry with Torq HyperSOC’s autonomous threat handling isn’t just a solution — it’s your SOC team’s ultimate cheat code.

See Wiz Defend and Torq HyperSOC in action together.

The Best SOC Tools in 2025: Legacy vs Modern Automation

By Torq

June 20, 2025

9 Minute Read

Security Operations Centers (SOCs) are evolving faster than ever. As cybersecurity threats grow more sophisticated and digital infrastructure expands across cloud, hybrid, and on-prem environments, legacy SOC tools like SOAR are falling behind. Static dashboards, siloed point solutions, and human-dependent processes simply can’t keep up.

Traditional SecOps tools are no longer enough. Modern tools must proactively detect suspicious activities using broad data sources (e.g., threat intelligence, vulnerability databases, etc.) and enable seamless collaboration across teams. Automation is the key SOC tool to scale detection and response efficiently.

Modern SOCs require automation-first platforms that enable proactive defense, seamless integrations, and high-scale responsiveness. Platforms like Torq — powered by Hyperautomation — represent the next generation of SOC architecture.

Read on for a breakdown of SOC tools, an exploration of the best tools of 2025, and how automation streamlines security operations.

What is a SOC Tool?

SOC Tools

SOC tools are technologies designed to help security teams detect, investigate, and respond to threats. They support incident resolution by collecting and analyzing data across IT and cloud ecosystems. Each tool plays a vital role in the SOC technology stack.

Today’s cybersecurity environments rely on dozens of integrated systems. While powerful, this complexity can create inefficiencies, increase SOC analyst fatigue, and lead to slower threat response times. This is where SOC automation platforms like Torq shine by orchestrating across all tools, streamlining workflows, and accelerating response.

5 Core Capabilities of Security Operations Center Tools

Modern SOCs demand tools built for the cloud’s dynamic, distributed nature. Here are five must-have capabilities your stack needs.

1. Continuous SOC Monitoring

Tools should provide always-on visibility across cloud, hybrid, and on-prem workloads, dynamically adapting to autoscaling and ephemeral infrastructure. Look for platforms that detect real-time anomalies, monitor traffic flows, flag malicious configurations, and help strengthen your cloud security posture with minimal manual effort.

2. Log Collection and Analysis

Log tools enable deep investigation by aggregating decentralized telemetry across services. They help correlate signals across layers, enhancing intrusion detection, root cause analysis, and threat attribution across sprawling cloud environments.

3. Threat Detection

The best detection tools are plugged into real-time threat intel feeds and vulnerability databases. This allows SOC teams to quickly spot indicators of compromise (IoCs), detect novel tactics, and stay ahead of emerging threats with precision.

4. Incident Response

Incident response platforms have prebuilt playbooks and customizable workflows to stop attacks quickly. They can block malicious IPs, isolate compromised assets, and auto-contain threats without human intervention.

5. Automation

Security automation is essential for modern SOCs to operate efficiently at scale. It streamlines repetitive tasks, accelerates incident response, and allows SOC analysts to focus on complex threats instead of manual workflows.

The Top 10 SOC Tools in 2025

Specific tools have emerged as foundational to operational success as the SOC landscape evolves. Below are ten must-have SOC software tools and technologies for any security team aiming to stay ahead.

1. Log Collection and Management

Log management tools like Splunk and Elasticgather security logs and telemetry from various sources, including endpoints, network devices, and cloud environments. Proper log management is foundational for threat detection, compliance monitoring, and forensic investigations, making it an indispensable part of the SOC infrastructure.

2. Security Information and Event Management (SIEM)

SIEM platforms provide essential SOC monitoring and event correlation capabilities, helping security teams quickly identify and respond to threats. They are the cornerstone for centralized security operations.

Common examples of SIEM tools include IBM QRadar, Microsoft Sentinel, Splunk Enterprise Security, LogRhythm, and ArcSight. This SOC software correlates data across multiple sources, providing comprehensive threat visibility and efficient event management.

3. Vulnerability Management

Vulnerability management platforms continuously scan and assess SOC network assets for vulnerabilities, prioritizing them based on severity and business impact. These platforms help SOC analysts proactively address critical issues before attackers can exploit them.

Rapid7 InsightVM, Nessus, Tenable, and Qualys are leading vulnerability management tools that provide actionable vulnerability data, enabling teams to patch vulnerabilities rapidly and effectively. Effective vulnerability management reduces organizational risk, maintains compliance, and prevents attackers from exploiting known weaknesses.

4. Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR)

EDR tools monitor endpoints, such as laptops and servers, enabling detection of malicious activities and automated response to threats in real time. Extended Detection and Response (XDR) solutions expand this coverage to networks, email, the cloud, and servers, delivering comprehensive security visibility.

EDR solutions like CrowdStrike Falcon and SentinelOne provide forensic capabilities and proactive threat-hunting features. XDR tools like Palo Alto Networks Cortex XDR unify endpoints, SOC networks, and cloud security to offer a holistic view of the threat landscape.

5. Email Security

Email security tools work by performing detection and response across email, endpoints, and identity systems. They can quarantine malicious messages, remove harmful emails post-delivery, and correlate activity across systems to reveal the full scope of an attack.

Solutions like Proofpoint and Microsoft Defender provide real-time URL and attachment sandboxing, threat intelligence integration, and automated remediation of compromised accounts. These capabilities not only strengthen threat response but also support compliance by enforcing encryption, archiving, and access controls.

6. Threat Hunting

Threat hunting tools proactively search for signs of malicious activity that evade traditional detection methods. Platforms like Carbon Black and Cisco empower SOC analysts with advanced investigative capabilities to discover and neutralize threats before they cause significant damage.

7. Threat Intelligence

Threat intelligence tools gather and analyze external threat data, providing actionable insights into potential cyber threats. Platforms such as Recorded Future and Anomali enhance a SOC’s ability to predict, identify, and ensure a proactive response to emerging threats, keeping teams informed of global threat trends and attacker tactics.

8. Cloud Security Posture Management (CSPM)

CSPM tools help identify, assess, and remediate misconfigurations and policy violations in cloud infrastructure. These tools continuously monitor cloud environments like AWS, Microsoft Azure, and Google Cloud Platform to ensure compliance with internal security policies and industry standards.

CSPM solutions automatically detect configuration drift, enforce least privilege access, and reduce the risk of data exposure by alerting teams to insecure storage, open ports, or excessive permissions. By offering centralized visibility and continuous compliance assessment, CSPM enables SOC teams to secure cloud workloads at scale while responding faster to evolving risks.

9. Identity and Access Management (IAM)

IAM tools control and monitor user access to IT resources, ensuring only authorized individuals can reach sensitive systems and data. They encompass technologies like single sign-on (SSO), multi-factor authentication (MFA), privileged access management (PAM), and identity governance.

In a SOC, IAM is essential for investigating incidents, detecting compromised accounts, and preventing unauthorized lateral movement, making it a cornerstone of a strong security posture.

10. Automation

At Torq, we call this Hyperautomation. Hyperautomation represents the next generation of SOC technology, combining advanced automation and artificial intelligence (AI) into a unified approach that fundamentally transforms traditional security operations.

Torq integrates seamlessly with existing SOC tools, orchestrating complex workflows across the entire security stack and significantly reducing repetitive, manual tasks. By leveraging GenAI and agentic AI, Torq Hyperautomation dynamically identifies, analyzes, and responds to threats in real time, delivering faster and more consistent incident responses.

This proactive, autonomous approach enables security teams to scale effectively, enhance operational efficiency, and improve accuracy across their security processes. Hyperautomation accelerates response times, reduces SOC analyst workload, and ensures more precise threat detection and remediation.

How Automation Transforms SOC Tools

Automation transforms traditional SOC operations by connecting disparate tools, streamlining workflows, and enabling rapid, automated responses. Here’s how:

Faster detection and response: Automation drastically reduces the time it takes to identify, investigate, and respond to security incidents. What once took hours or days now happens in seconds, minimizing dwell time and damage.
Increased SOC analyst efficiency: With Tier-1 alerts automatically triaged (and often auto-remediated) and routine tasks offloaded to automated workflows, SOC analysts can handle a higher volume of cases without burnout. Teams get more done with fewer resources, reducing the need to scale headcount just to keep up.
Effortless scalability: As threats grow in number and complexity, automation allows SOC analysts to keep pace without compromising performance. Whether your environment is expanding across clouds or adding new tools, automation scales effortlessly alongside.
Smarter use of human talent: SOC analysts are too valuable to be bogged down by repetitive tasks. Automation frees them to focus on high-impact investigations, strategic decision-making, and threat hunting, where human judgment and creativity matter most.
Reduction in alerts: Automated triage filters out low-priority noise, enriching and escalating only the alerts requiring attention. SOC analysts stay focused on real threats instead of drowning in false positives.

How Torq Hyperautomation Transforms the SOC

Torq HyperSOC™ is the first agentic, AI-powered SOC platform built for autonomous security operations. It transforms your SOC from reactive and overloaded to autonomous and high-performing.

Here’s how Torq makes it happen.

Seamless Integration with Your Entire Security Stack

Torq connects instantly to all your SOC tools — SIEM, EDR, CSPM, IAM, SaaS platforms, ticketing systems, and even homegrown apps — without custom code or complex deployments. Whatever you’re running, Torq plugs in and gets to work.

AI Agents That Work Like SOC Analysts

At the heart of HyperSOC is Socrates, Torq’s AI SOC Analyst and omniagent. Socrates orchestrates a team of specialized AI Agents purpose-built for tasks like enrichment, case management, user verification, and remediation. Together, they coordinate end-to-end case lifecycles with precision and speed.

Natural Language-Driven Automation

Security automation doesn’t have to be complex. With Torq, anyone on your team can trigger powerful workflows using plain English. Want to isolate a user, rotate credentials, or escalate a threat? Just ask — Torq handles the rest.

Hyperautomation at Enterprise Scale

Torq’s performance automatically scales to keep up, whether your environment is cloud-native, hybrid, or on-prem. It runs thousands of workflows in parallel, adapts to evolving threats, and ensures no alert slips through the cracks.

Built to Flex with Your Needs

Torq’s open architecture and robust APIs let you fully customize cases to fit your cybersecurity strategy. Build once, reuse anywhere, and adapt fast to new use cases — all without needing a team of developers.

Hyperautomation is the SOC Tool You Need Today

As cybersecurity challenges mount, traditional tools are no longer enough. Modern security operations centers require intelligent, automated, and scalable solutions that enable security teams to move faster, act smarter, and deliver better outcomes.

AI-driven Hyperautomation is that solution.

Torq brings Hyperautomation to life, enabling SOC analysts to move beyond fragmented processes and manual triage. Whether you’re a lean security team or an enterprise SOC analyst, Torq empowers you to detect, respond, and remediate with unprecedented speed and precision.

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SIEM vs SOAR vs Hyperautomation: What Actually Works for the Modern SOC

By Torq

June 18, 2025

11 Minute Read

Most SOCs standardized on SIEM and SOAR — yet the promise of end-to-end automation never materialized. SIEM gave SOC teams centralized log visibility and correlation. SOAR promised relief from repetitive tasks through orchestration. But as threats scaled in speed and complexity, and security teams faced mounting pressure with fewer resources, these tools started to show their limits.

According to Francis Odum’s AI SOC Market Landscape 2025 survey of 300+ CISOs, organizations now face an average of 960 daily security alerts, and over 3,000 daily alerts at enterprises with 20,000+ employees. The report describes a “tsunami of data” crippling SOCs, compounded by slow triage and limited response capabilities in legacy tools like SIEM and SOAR.

Hyperautomation is a fundamentally new approach built for the modern SOC. It doesn’t just connect tools or run playbooks. It combines real-time integrations, no-code workflow design, and agentic AI to create a fully autonomous, adaptable system for detection, response, and remediation.

The Evolving Landscape of SOC Tools

To understand why Security Hyperautomation is redefining modern SecOps, it helps to look at how we got here.

SIEM was built to tame the flood of security data, ingesting logs, correlating events, and supporting compliance mandates. It gave teams visibility but little action.
SOAR followed, aiming to reduce manual effort by automating response through structured playbooks and tool integrations. It promised efficiency but delivered rigidity.
Security Hyperautomation emerged when both began to crack under modern pressures, soaring alert volumes, hybrid cloud sprawl, analyst burnout, and the demand for real-time, intelligent response.

Legacy tools helped establish the foundation. But they weren’t designed for today’s threat landscape’s speed, scale, or complexity. That’s where Hyperautomation changes everything: bridging gaps, replacing brittle workflows, and enabling fully autonomous, AI-driven security operations.

Next, we’ll break down what each SOC tool delivers — and where they fall short.

SIEM: Built for Logging and Search, Not Speed

A SIEM (Security Information and Event Management) system collects, aggregates, and analyzes log data from across an organization’s IT environment. It centralizes visibility into security events, correlates data to detect potential threats, and supports compliance reporting.

SIEM platforms were built to give SecOps teams visibility. They ingest, normalize, and analyze data from firewalls, endpoints, servers, cloud apps, and more, centralizing logs into one place so teams can detect anomalies and satisfy compliance mandates.

SIEMs deliver:

Centralized log aggregation and historical data analysis
Rule- and pattern-based correlation for threat detection
Dashboards and reports for frameworks like PCI-DSS, HIPAA, and ISO 27001

For a time, this was enough. But, the threat landscape and the SOC have changed. Modern environments are real-time, distributed, and hybrid. Threat actors exploit vulnerabilities in hours, not weeks. Meanwhile, SIEM solutions are built around static detection logic, batch processing, and reactive triage. They’re not designed to orchestrate response or handle fast-moving, multi-vector threats.

And they come with challenges:

Configuration complexity: Fine-tuning SIEM systems requires deep expertise to avoid false positives and missed threats during setup.
Integration hurdles: SIEMs often struggle to seamlessly connect with diverse security tools, limiting visibility and operational efficiency.
Resource constraints: Deploying and managing SIEMs demands significant time, budget, and skilled personnel, often out of reach for lean teams.
Hidden costs: Data ingestion and storage can balloon unexpectedly as log volumes grow, straining budgets and infrastructure.
Data onboarding challenges: Normalizing and standardizing log data from disparate systems adds overhead and impacts detection accuracy.
Scalability limitations: As environments grow, many SIEMs can’t keep pace with increased data volume, causing performance bottlenecks.
Retention and compliance pressures: Meeting regulatory data retention requirements while controlling storage costs is a constant balancing act.

As a result, SIEM solutions often devolve into expensive search engines. They surface problems, but can’t solve them. Analysts still have to swivel-chair between tools, copy/paste IOCs, open tickets, and manually kick off an investigation or remediation. In a world that demands instant response, SIEMs stall at detection.

SOAR: Designed to Orchestrate, but Not Adapt

SOAR (Security Orchestration, Automation, and Response) platforms were designed to help security teams streamline incident response workflows. They integrate across tools like SIEMs, EDRs, and ITSM systems to orchestrate tasks and enforce processes through playbooks.

SOAR platforms were introduced to close the gap between detection and action. They aimed to reduce repetitive work by connecting disparate tools and codifying workflows. With SOAR, SOCs could automate ticket creation, enrich alerts, or trigger containment through predefined playbooks.

SOAR brought value through:

Playbook-driven automation for common incident types (e.g., phishing, malware)
API-based integrations between SIEM, EDR, firewalls, and ITSM platforms
Structured response processes to reduce manual tasks and improve SLAs

However, SOAR platforms often introduce more challenges than they solve, including:

Strategic misalignment: SOAR tools often fail to support broader security maturity or align with long-term operational goals.
Cultural fragmentation: SOAR can reinforce IT silos by overlooking the human workflows and collaboration needed across teams.
Resource diversion: SOAR often pulls skilled analysts away from high-value tasks to maintain, tune, and troubleshoot playbooks.
Overhyped expectations: Many SecOps teams assume SOAR delivers full autonomy, only to face brittle workflows and limited intelligence.
Integration burdens: Connecting SOAR platforms with diverse tools frequently requires custom code and ongoing maintenance.
Vague success metrics: Measuring SOAR effectiveness is difficult without clear KPIs for response speed, coverage, or workflow impact.
Code-heavy and complex: Most SOAR platforms require Python or custom scripting for core functionality.
Fragile integrations: Workflows break easily when APIs shift or tools are updated, creating constant maintenance cycles.
Slow to iterate: Even small changes demand developer time, testing, and deployment, delaying improvements.

This means SOAR becomes a bottleneck instead of an accelerator. Analysts depend on engineers to build or fix automations. Workflows lag behind emerging threats. Rigid architectures can’t adapt to dynamic inputs or decision branches — if something unexpected happens, SOAR stops.

And perhaps most importantly, SOAR lacks contextual intelligence. It can automate known paths but can’t think, reason, or react to the unexpected. This lack of adaptability is a dealbreaker for hybrid and cloud-native SOCs facing high alert volume and constantly shifting attack surfaces. That’s why we believe SOAR is dead.

The SOAR is Dead Manifesto: Why Hyperautomation is What’s Next. Download the Manfesto

Hyperautomation: A New Model for a New Threat Landscape

Security Hyperautomation is the next evolutionary leap in cybersecurity operations. Born out of the limitations of legacy SIEM and SOAR tools, it addresses today’s most pressing SecOps challenges with a radically new approach: connecting every tool, every signal, and every action across the security ecosystem with no-code, intelligent automation.

Torq Hyperautomation^TM is an advanced approach to security operations that combines no-code automation, real-time integrations, and agentic AI to intelligently orchestrate detection, response, and remediation across the entire SOC. Unlike legacy tools, it adapts dynamically to changing threats and environments, eliminating manual effort, reducing response times, and scaling operations without added headcount.

It builds on the promise of SIEM and SOAR but goes further by automating the entire security lifecycle with:

Intelligent case management that anyone can build and adapt in minutes
Real-time integrations with any tool, across cloud and on-prem
Agentic AI that thinks and acts independently, not just executes instructions

Where SIEM and SOAR solutions struggle with flexibility, context, and scale, security Hyperautomation delivers speed, adaptability, and resilience.

What Makes Hyperautomation Different

Hyperautomation enables real-time action, responding as threats emerge rather than after tickets accumulate. It scales elastically across environments and data volumes without manual tuning. It blends no-code with full-code options so every role in the SOC can build and adapt workflows. Agentic AI adds contextual learning, adjustment, and autonomous execution. And it delivers true end-to-end automation.

Hyperautomation’s Strategic Value

Outcome-focused: Reduces MTTR, improves resilience, and protects reputation
Human-centric: Minimizes analyst toil and burnout
System-agnostic: Works with legacy and modern tools alike
Speed to value: Deploy in days, not months

Proven Benefits of Security Hyperautomation

10x faster ROI than traditional SOAR platforms
800% increase in workflow execution speed with less engineering effort
70x faster threat blocking through AI-led real-time response
Up to 30% lower operational costs, according to Gartner
Increased analyst retention, as SecOps teams spend less time on busywork
Self-optimizing systems, powered by continuous learning and feedback

SIEM vs SOAR vs Hyperautomation

Capability	SIEM	SOAR	Hyperautomation
Detection	Log-based correlation and rules	Dependent on SIEM or third-party tools	Real-time + contextual, across multiple data sources
Response	Manual investigation and action	Playbook-based, limited flexibility	Autonomous + adaptive based on live context
Remediation	None	Partial, often manual follow-up needed	End-to-end automation across tools and teams
Integration Complexity	High: Custom parsers and connectors needed	Moderate to High: Scripted connectors required	Low: Plug-and-play, no-code integrations
Analyst Effort	High: Alert triage, tuning, and investigation	Medium to High: Building and maintaining playbooks	Low: Intelligent workflows reduce manual effort
Adaptability	Low: Static rules and searches	Low to Medium: Brittle, slow to update	High: Dynamic workflows adapt in real time
Deployment Time	Months: Setup, tuning, scaling	Months: Playbook development, integrations	Days: Launchable without engineering bottlenecks
Use of AI	Static rules and logic	Scripted logic and decision trees	Agentic AI: Autonomous reasoning and execution

Why Hyperautomation Wins for Modern SOCs

Hyperautomation eliminates the wait time between detection and action. Analysts don’t need developers to build playbooks. No-code platforms mean workflows can be built, tested, and launched in minutes, not weeks.

That speed translates into fewer open incidents, shorter dwell times, and faster remediation. Instead of reactive incident response, teams operate proactively, automatically blocking threats as they emerge.

Agentic AI goes beyond predictive analytics or simple LLM prompts. It doesn’t just assist analysts — it acts on their behalf by:

Planning next steps based on live threat context
Making real-time decisions across toolsets
Executing actions independently and escalating when needed

Hyperautomation is already a proven replacement for SOAR, eliminating rigid playbooks and slow, code-heavy workflows. But it can also serve as a lightweight SIEM — or even a full SIEM alternative — by ingesting, storing, and analyzing raw logs and telemetry in real time. This enables advanced behavioral analytics, long-term visibility, and cost-effective detection and response without the overhead of traditional SIEMs.

How to Transition from SIEM/SOAR to Hyperautomation

Transitioning from a SOAR or SIEM to Torq Hyperautomation doesn’t require a ground-up rebuild; it’s about unlocking more value from the tools you already have. By layering intelligent, no-code automation over your existing stack, you can unify detection, response, and remediation into a seamless, high-speed workflow that eliminates manual lag and scales effortlessly with your environment.

You Don’t Have to Rip and Replace

Hyperautomation isn’t a forklift upgrade. It augments what you already have. Connect your SIEM, SOAR, EDR, and ITSM into the Torq ecosystem to maximize their value without rebuilding from scratch.

Connect What You Have. Automate What You Couldn’t.

With Torq’s plug-and-play architecture, you can quickly unify your environment without custom code or long dev cycles.

Ingest alerts from any major SIEM (Splunk, Sentinel, QRadar, etc.)
Trigger automation across SOAR platforms or manual legacy workflows
Enrich alerts with threat intel, asset data, and CMDB context
Initiate auto-remediation across cloud, endpoint, and identity systems

Building Automated Workflows for Detection → Response → Remediation

Whether your challenge is phishing, malware, or insider threats, Torq automates the entire lifecycle. Example use cases include:

Phishing: From user report to quarantine, user notification, and ticket closure
Malware containment: Auto-isolation via EDR, log enrichment, RCA reporting
Insider threats: Access revocation, HR sync, investigation kick-off

With Hyperautomation, your existing tools become part of an intelligent, adaptive system that moves at the speed of your threats, without adding engineering overhead.

Automate Everything That Matters

Legacy tools are reactive. SIEM and SOAR help you find threats and maybe start to respond. But the workflows are brittle, slow, and reactive. Tickets stack up, analysts burn out, and risk accumulates.

Hyperautomation is proactive. It’s built for the cloud era — fast, modular, and scalable. By replacing manual tasks with intelligent, real-time automation, SOCs reduce MTTR, eliminate noise, and gain control over their environment.

Analysts are empowered. Hyperautomation doesn’t just help you do more with less. It changes what’s possible. Analysts become strategists, platforms become ecosystems, and security becomes faster than attackers.

SIEM and SOAR made progress but can’t keep up with today’s threat volume, speed, and complexity. Alert fatigue, manual overhead, and slow response times cost teams more than just time. Hyperautomation creates a truly autonomous SOC, and the results speak for themselves: faster response, lower cost, less burnout, and security at the speed of your business.

Ready to upgrade your operations? Read the SOC Efficiency Guide to see how leading teams modernize workflows and crush MTTR.

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The Top 3 Hyperautomation Use Cases for Torq POCs

By Torq

June 16, 2025

5 Minute Read

Many organizations come to Torq when they’ve hit a wall with their legacy SOAR platform. The migration to Torq isn’t just a technology upgrade — it’s an operational overhaul. With Torq, enterprises have replaced hundreds of rigid playbooks in weeks, dramatically reduced time-to-value, and unlocked capabilities that legacy SOAR could never support.

The move to Torq is faster and smoother than you think,thanks to our intuitive workflow design, low-code flexibility, and hands-on migration support. If you’re considering a demo or a proof of concept (POC), these are the top three Hyperautomation use cases we’d start with — the ones that deliver instant value and set your implementation up for long-term success.

Hyperautomation: A SOC Must-Have

Hyperautomation is the current era of security operations — where every repetitive task, manual process, and alert-handling bottleneck gets replaced by scalable, intelligent automation. Unlike traditional SOAR, AI-driven Hyperautomation is agile, dynamic, and driven by real-time context.

In the SOC, this means:

Faster threat response: Alerts are triaged, investigated, and remediated automatically across EDR, IAM, email, and cloud systems.
Massive analyst efficiency gains: Your team spends less time on tedious Tier-1 tasks and more time threat hunting and improving security posture.
Lower operational costs: Hyperautomation eliminates tool sprawl, reduces alert fatigue, and streamlines workflows, making the SOC leaner and more effective.
Scalability: Whether it’s 10 alerts or 10,000, Hyperautomation responds at machine speed.
Immediate ROI: The impact is measurable within days: reduced MTTR, faster MTTD, and happier analysts.

Torq’s Hyperautomation platform makes it easy to deploy, customize, and scale automation across your environment without writing a single line of code.

1. Endpoint Detection and Response

EDR is one of the most common Hyperautomation use cases, and for good reason. Endpoints are often the first line of defense when threats bypass preventative controls. But while EDR platforms like SentinelOne, CrowdStrike, and Microsoft Defender continuously surface alerts, they still rely on analysts for response.

That’s where Torq comes in. By integrating your EDR tools with Torq Hyperautomation, you can:

Instantly isolate compromised hosts and cut off lateral movement
Trigger targeted endpoint scans, triage workflows, and auto-remediation actions
Correlate EDR alerts with identity, network, and threat intel context for smarter decision-making
Auto-generate detailed incident reports with full observability into root cause and system impact

EDR Hyperautomation in Action: Torq and SentinelOne

When SentinelOne detects a threat, it sends event data via webhook to Torq, which triggers a predefined workflow. Socrates, Torq’s AI SOC Analyst, evaluates the threat, retrieves asset details from CMDB, checks for correlated user activity, and executes the appropriate response. The compromised host is quarantined, impacted credentials are flagged, and a full report is auto-generated for the analyst.

Automating EDR response is one of the most powerful first moves in any Hyperautomation POC. It delivers instant value, dramatically reduces MTTR, and frees analysts from constantly chasing endpoint alerts across multiple consoles.

2. Email Security

Phishing remains the #1 attack vector — and one of the most common triggers for Tier-1 security alerts. These alerts are high-volume, high-noise, and easy to miss. Automating phishing response with Torq during a POC delivers fast, visible results that eliminate manual overhead.

Torq integrates with various email security platforms, including Microsoft 365, Gmail, Proofpoint, VirusTotal, Mimecast, Abnormal Security, Barracuda, and Cisco.

With Torq, you can:

Auto-quarantine suspicious emails
Lock user inboxes and enforce password resets for potentially compromised accounts
Extract, analyze, and enrich email artifacts like headers, links, and attachments
Launch phishing investigation playbooks

This automation dramatically reduces the mean time to remediate (MTTR) phishing attempts, and it’s one of the clearest, most repeatable use cases for proving the power of Hyperautomation.

Email Security Hyperautomation in Action: Torq and VirusTotal

Torq integrates with VirusTotal to enhance email threat analysis. A Torq workflow can monitor a designated mailbox (such as Outlook or Gmail), extract URLs, attachments, and header IPs from each message, and submit them to VirusTotal for threat scoring. Based on the results, Torq automatically categorizes the message as malicious, suspicious, or clean, updating labels, alerting stakeholders, and kicking off remediation.

What once took hours (or days) is reduced to seconds. Analysts can investigate real threats instead of triaging false positives. And you immediately prove Hyperautomation’s impact on everyday SOC volume.

3. Identity and Access Management (IAM)

Identity is the new perimeter. Many breaches are caused by compromised credentials, whether through phishing, MFA fatigue, or social engineering. Automating IAM workflows early in your POC helps you immediately reduce access-related risk.

Torq integrates with leading IAM providers, including Okta, Microsoft Entra ID, Ping Identity, Duo Security, JumpCloud, CyberArk, and Auth0.

Integrate Torq with your IAM, and you can:

Detect and respond to suspicious login behavior
Auto-disable accounts after anomalous activity
Automate user provisioning and de-provisioning
Trigger MFA resets and log analysis workflows

IAM Hyperautomation in Action: Torq and Okta

Here’s one way Torq and Okta work together: This workflow monitors for new MFA methods added in Okta, a common sign of account takeover. It checks the source IP with VirusTotal, asks the user to confirm the action, and if suspicious, auto-opens a Jira ticket, spins up a Slack message, and suspends the account if needed.

Integrating IAM with Torq at the start of your implementation reduces security risk and enhances operational efficiency by replacing slow, manual processes with scalable automation.

Fast, Scalable Results… In Days

These three use cases — EDR, email, and identity — are high-impact, high-speed proof of what AI-driven Hyperautomation can do for your SOC.

Our customers routinely:

Cut MTTR and MTTD across critical workflows
Eliminate repetitive Tier-1 analyst work
Prove ROI in days, not weeks

Start with what matters most. Let Torq show you how fast modern SOC can move.

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Squish the Phish: 6 Automated Phishing Response Strategies

By Torq

June 12, 2025

11 Minute Read

And rather than getting better at recognizing phishing emails, humans are seemingly getting worse, in part due to the increasing phishing sophistication and customization at scale that GenAI offers. According to Verizon’s 2024 Data Breach Investigations Report, people are falling for phishing attacks at an alarming rate, taking a median of just 21 seconds to click a malicious link and another 28 seconds to enter their personal data.

Of course, part of the solution lies in educating users to recognize and report phishing. But user education only goes so far — on average, only 3% of users report phishing emails. Strong anti-phishing education may increase that number, but you’re still fighting an uphill battle if you rely on end users as your primary means of defense against phishing.

Instead, modern security teams are turning to automated phishing response. By using security automation to detect and respond to phishing attempts, security teams can stop the majority of phishing messages before they ever reach end users.

Manual Phishing Triage: A Losing Battle for SOC Teams

Manual phishing investigation and response is a relentless, high-volume drain on SOCs. When a potentially malicious email is flagged — either by a security tool or a user — the clock starts ticking.

The analyst must first deconstruct the suspicious email: digging into email headers, verifying sender addresses, analyzing the message body for suspicious language, and identifying any potential Indicators of Compromise (IOCs), such as embedded links or file attachments.
Each potential IOC must then be manually validated. This initiates a tedious cycle of “swivel-chair” analysis, where the analyst copies and pastes information — IP addresses, domains, file hashes, etc. — out of the email and into various threat intelligence platforms and security tools. Juggling these multiple browser tabs and windows is essential to determine if an artifact is truly malicious, but each copy-paste and window hop wastes time while the risk of human error increases.
And this is all before remediation even starts. Once the threat is confirmed, the analyst must then take action to block the sender, initiate a search to delete the email from all other inboxes, and respond to the user who reported it.

This monotonous, repetitive process is not just slow — it’s dangerously error-prone. A single missed detail or misinterpretation can be the difference between a blocked threat and a full-blown incident.

Manual phishing triage and response workflows can take tens of minutes to over an hour for a single case. Multiply that by hundreds of daily alerts, and the challenge of keeping up becomes too big to ignore. However, with anti-phishing automation, all of the grind of phishing triage, investigation, and remediation disappears.

What is Automated Phishing Response?

Anti-phishing automation refers to technology that autonomously investigates, triages, and neutralizes suspected phishing emails. It is designed to replace the slow, repetitive, error-prone grind of manual phishing defense with a consistent machine-speed response that immediately isolates compromised inboxes, revokes access to malicious emails, blocks phishing URLs, and notifies users.

6 Hyperautomated Phishing Response Strategies and Tactics

Torq Hyperautomation™ integrates with several key partners to help organizations prevent and mitigate phishing attacks and avoid costly data breaches — which cost organizations an estimated $4.88 million in 2024. Below are six strategies for leveraging Hyperautomation to fight phishing across your entire security environment.

1. Perimeter Defense: Hardening the Email Gateway

Your first line of automated defense is securing the primary phishing entry point: the email inbox. The goal is to identify and block as many malicious emails as possible before they ever reach a user.

Torq partners with Secure Email Gateway (SEG) providers to enhance their detection accuracy and response by correlating data across leading SEG solutions like Abnormal Security, Microsoft, Proofpoint, Mimecast, and more. Torq then autonomously initiates remediation actions, such as removing malicious emails or adjusting email security controls.

Key tactics:

Filter messages based on multiple attributes: The days are long gone when simply scanning email for strings like “Nigerian prince” guaranteed that you’d catch the phishers. Simple keyword or domain name scanning won’t cut it. Effective anti-phishing automation evaluates every email based on multiple attributes — its content, the domain from which it originated, whether it contains an attachment, the type of attachment, and so on — to build a far more informed assessment than content analysis alone can provide.
Detonate attachments in sandboxes: For suspicious but unconfirmed email threats, automation can instantly “detonate” (i.e. download and open) attachments or links in a secure, isolated sandbox. By evaluating the content’s behavior in a safe environment, the system can detect anomalies or attack signatures that confirm the content is indeed malicious. At the same time, the original email remains quarantined from the user. Pending the results, the workflow can either safely release the back content to the user or block it definitively.
Block sender names and domains automatically: When a phishing attempt is confirmed, automation can instantly block the sender’s name and entire domain across the organization. This prevents subsequent waves of the attack from different accounts on the same infrastructure, disrupting the phisher’s campaign.

2. Identity and Access Control: Protecting Your People

Since credentials are the primary target of most phishing attacks, proactively protecting user identities is paramount. Torq does this by analyzing cloud-based user and entity behaviors to detect anomalies that could be indicative of phishing. And if a phishing attack does occur, Torq integrates with solutions, including Okta, Active Directory, JumpCloud, OneLogin, Ping, and Wiz, to prevent account takeover and limit an attacker’s access.

Key tactic:

Reset credentials automatically: Upon detecting a potential phishing compromise, automation should immediately trigger a security workflow to reset login credentials for impacted users. This includes logging the user out of all active sessions and forcing a password reset to instantly invalidate any stolen credentials.

3. Endpoint Security: Containing the Impact

If a malicious email makes it through and a user clicks a link or opens an attachment, the battle shifts to the endpoint (e.g. the user’s laptop or phone). Working with EDR providers like Crowdstrike, SentinelOne, Microsoft, and others, Torq can correlate endpoint data for a holistic view of a phishing attack’s scope and impact, then rapidly take action to contain and remediate any compromise on the device itself.

Key tactic:

Scan and quarantine affected endpoints automatically: The moment a user is linked to a confirmed phishing attack, automation should trigger the EDR solution to perform an immediate scan of their devices. If malware is found, the endpoint can be automatically quarantined from the network to prevent lateral movement while the threat is removed.

4. The Human Element: Empowering Users as a Line of Defense

Your employees are both a target and a potential ally. Torq’s chatbot integrations with communication tools like Slack, Microsoft Teams, Discord, and email make it quick and easy for users to report threats, providing them with instant feedback and education, and turning users into an active part of your security posture.

Key tactics:

Use chatbots for phishing reporting: Integrating chatbots into communication tools like Slack or Microsoft Teams gives users a simple, immediate way to report suspicious emails. These bots can then kick off automated security workflows based on the report, such as resetting passwords, revoking access, or initiating scans for malware. Chatbots can also provide educational resources and coaching to users on how to avoid phishing and improve their cybersecurity awareness.
Triage user-reported emails automatically: When a user reports a suspected phishing email, automation takes over. It can instantly extract key indicators (URLs, file hashes, headers), analyze them against threat intelligence, and provide the user with immediate feedback, confirming if the email was malicious and has been handled, or if it was safe.

5. Data Protection & Incident Response: Minimizing the Damage

When a breach occurs from a phishing email, the strategy shifts to understanding and minimizing the damage. Automation is critical for rapidly assessing the scope and scale of data loss and ensuring compliance with regulatory requirements for notifications and reporting. Torq partners with providers like Microsoft, Crowdstrike, Varonis, and Symantec to automate these two important pieces of the phishing puzzle.

6. Continuous Improvement: Learning from Every Attack

A strong defense is one that constantly learns and adapts. Understanding the metrics after the fact can help prevent a phishing attack in the future. Torq partners with SIEM, SEG, and EDR providers to use data from past incidents to refine and improve your automated workflows and overall security posture.

Key tactic:

Quantify improvements with automated metrics: Use automation to analyze response times, workflow effectiveness, and incident severity. By leveraging AI in the SOC to automatically categorize incidents and create cases, you can ensure critical threats receive priority and gather insights to continually harden your defenses against future attacks.

Example Automated Phishing Alert Analysis Workflow in Torq

This Torq Hyperautomation workflow automates the initial triage of a reported phishing email. It instantly extracts and aggregates key artifacts like URLs, file hashes, and headers from Outlook messages and attaches to create a structured data set for deeper analysis, following these steps:

Alert trigger: The process begins the moment a potential phishing alert is received from a source like Microsoft 365.
Parallel data extraction: Torq immediately executes multiple tasks in parallel to deconstruct the email:
- URLs: It extracts all unique URLs from the email’s body and within any attachments.
- Attachments: It processes all file attachments to retrieve their details and corresponding file hashes.
- Headers: It retrieves the full message headers using the Microsoft Graph API.
Threat Validation: Torq then leverages integrations with various threat intelligence feeds, such as VirusTotal, to determine if the URLs, attachments, or information pulled from the email headers are flagged as malicious or benign. This helps quickly weed out false positives, or confirms the alert as a true malicious threat before a security case is even created.
Data consolidation and output: All extracted artifacts (URLs, file hashes, and headers) are automatically collected, combined, and formatted into a single, structured output, ensuring all necessary data is ready for the next step.
Initiate case management: If the alert is confirmed as malicious through third-party validation (or reaches a designated suspicious threshold), the structured output is then used to automatically create a new security case or escalate an existing incident with similar IOCs, often triggering a nested workflow for full case management and remediation.

Case Study: Lennar Cuts Phishing Resolution from Hours to Minutes

The security team at Lennar, one of the nation’s leading homebuilders, was swamped by phishing. They spent “hours and hours” remediating phishing attacks due to manual processes and the lack of flexibility and integrations in their existing XSOAR solution.

After switching to Torq Hyperautomation, the time it took Lennar to resolve a phishing attack dropped from hours to just minutes. This freed up their security experts to focus on more important work, like hunting for major threats.

Before we had Torq, we would do a lot of manual phishing remediation, which was a big time-taker. We would spend hours and hours. With Torq, we’ve significantly reduced the amount of time spent on phishing, which allowed us to further refine our other tools and alerts.
Daniel Gross, Senior Operations Analyst, Lennar

Read the full case study >

Win the Phishing War with Automated Phishing Response

Phishers are only going to get better at what they do, especially as they become more sophisticated in their use of AI. The only way for today’s stretched-thin security teams to keep up is with automated phishing response.

Anti-phishing automation eliminates the noise from low-level phishing alerts and frees up analysts to focus on more critical threats. It also enables immediate, consistent, and accurate phishing incident response, reducing human error and minimizing the potential impact of a breach.

A truly effective automated phishing defense relies on the ability to connect and orchestrate every tool in your security stack. With Torq’s limitless integrations, you can automate any phishing tool and process, creating a unified and automated response to neutralize phishing threats across your entire environment.

Want to make your SOC more efficient across the board? Get Torq’s Field CISO’s guide covering practical advice to overcome rising threats, lean teams, and budget scrutiny.

Get the SOC Efficiency Guide

Contents

The Role of Data Transformation in Cybersecurity

What is Data Transformation?

How Data Transformation Works

ETL and ELT in Data Transformation

Types of Data Transformation

How AI Data Transformation Accelerates Security and Compliance

Getting Started with Torq’s AI Data Transformation Operator

How It Works

What You Can Do With It

Example Prompts

Torq Use Cases: Real-World AI Data Transformation in Security Operations

1. Normalizing SIEM Alerts Before AI Analysis

2. Filtering Out Benign Events in EDR Logs

3. Aggregating Failed Login Attempts for Brute Force Detection

4. Enriching IOC Data Before Threat Hunting

6. Preparing Audit Logs for Compliance Reporting

Choosing the Right Data Transformation Tools and Software

Why Torq Stands Out

Embrace AI-Driven Data Transformation

FAQs

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Contents

What is Alert Fatigue?

Cybersecurity Alert Fatigue

What Causes Cybersecurity Alert Fatigue?

Excessive False Positives

Poorly Tuned Detection Rules

Lack of Context in Alerts

Manual Triage Processes

Human Limits in Processing Volume and Urgency

Legacy SOAR

The Cost of Alert Fatigue in Cybersecurity

How Automation Helps You Beat Alert Fatigue

How To Combat Alert Fatigue

5 Benefits of Automating Cybersecurity Alert Triage

How Torq Can Prevent Cybersecurity Alert Fatigue with Automation

Legacy SOAR vs. Torq HyperSOC™: Solving Alert Fatigue

Hyperautomation is the Answer to Cybersecurity Alert Fatigue

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Contents

Cloud-Native Security 101

What is Cloud-Native Security?

Manual Cloud Security is Broken: Key Challenges

5 Benefits of Cloud Security Automation

1. More Efficient Operations

2. Ensure Compliance

3. More Accuracy

4. More Consistency

5. Scalability

How to Automate Cloud-Native Security: 12 SOC Use Cases

1. Identity and Access Management (IAM)

2. Automated Threat Hunting

3. Cloud Security Posture Management (CSPM)

4. Email Security

5. Self-Service Chatbots

6. Incident Response Automation

7. Application Security Automation

8. Phishing Detection and Response

9. Continuous Vulnerability Management

10. Threat Intelligence Enrichment

11. Suspicious User Behavior

12. Sensitive Data Access Controls

Hyperautomation: The Future of Cloud Security Automation

Cloud-Native Security Automation in Action: Torq + Wiz

Don’t Let Manual Security Hold You Back

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Contents

What Is Chatbot Automation?

How Chatbot Automation Works

Torq’s Approach to Chatbot Automation