AI Autonomous SOC Hyperautomation

The Multi-Agent System: A New Era for SecOps

By Torq

May 28, 2025

8 Minute Read

90 Days to SOC Autonomy: How Torq Customers Get There

See how Torq customers reach SOC autonomy in 90 days or less.

Learn how Torq helps MDRs automate workflows, increase efficiency, and scale managed detection and response services.

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Cybersecurity lifecycle automated by Torq Hyperautomation.

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Automate the cybersecurity lifecycle end-to-end to cut MTTR and strengthen resilience.

Autonomous SOC Hyperautomation Use Cases

3 Ways Torq HyperSOC Reduces MTTR with AI and Automation

By Bob Boyle

May 22, 2025

10 Minute Read

Your SOC exists for one core reason: to rapidly reduce the mean time to detect, investigate, and respond to threats. The more efficiently your team operates, the faster you reduce essential KPIs like MTTR, MTTD, MTTI, and what we call ‘MTTx’ (mean time to anything).

Ask our Field CISO, Patrick Orzechowski (PO), and he’ll tell you straight: If your SOC isn’t relentlessly focused on reducing risk through speed, you’re falling behind.

Talking about efficiency is easy. Actually achieving it, especially when your SOC is drowning in alerts and your analysts are burning out, is another story entirely.

The solution lies in combining Hyperautomation, agentic AI, and intelligent case management. Below, we break down three use cases where Torq HyperSOC™ and Socrates, the AI SOC Analyst, reduce MTTR to just minutes.

The SOC Efficiency Challenge

Reducing MTTR is a top priority for SOCs, yet many struggle to make meaningful progress. The root of the problem lies in legacy SOC environments’ outdated, manual, and disconnected nature.

If you’ve spent time in a SOC, these pain points are familiar:

Manual investigations slow everything down: Over half of security teams struggle with false positives and data overload. Analysts spend valuable time pivoting between tools, manually gathering context from logs, threat intel feeds, and asset databases. This “swivel-chair” approach introduces friction at every stage of the investigation.
Siloed tools don’t talk to each other: Most SOCs operate across dozens of disconnected platforms — EDR, SIEM, IAM, CMDB, ticketing, and more — without unified visibility or shared context. This makes correlating events and making informed decisions harder and slower.
High alert volume leads to fatigue: Teams receive thousands of alerts daily, many of which are false positives. Sifting through the noise to find true threats overwhelms even the most seasoned analysts, increasing the time it takes to detect and resolve incidents.
Disjointed shift handoffs cause delays: Without standardized processes or automated case management, investigations are often paused or reset between analyst shifts. Critical details get lost, increasing downtime and dragging out resolution timelines.
Inconsistent processes and tribal knowledge: The lack of documented workflows and reliance on individual expertise mean response varies from one analyst to the next. This inconsistency increases mean time to detect (MTTD), mean time to investigate (MTTI), and ultimately mean time to resolve (MTTR).
Delayed escalation and decision-making: Analysts often wait for senior approval before containing threats, primarily when procedures aren’t codified. This slows the response and allows attackers to move laterally or escalate privileges.

These pain points slow your team’s reaction times and increase risk. But these barriers disappear when Hyperautomation, AI, and smart case management are unified.

Why Reducing MTTR Is the Key to SOC Efficiency

What is MTTR in Cybersecurity?

MTTR (Mean Time to Resolution) measures the average time it takes to detect, investigate, contain, and fully resolve a security incident — from when it’s identified to when it’s no longer a threat. It’s one of the most critical KPIs for security operations because it directly reflects how quickly a SOC can respond to threats and minimize damage.

Related metrics include:

MTTD (Mean Time to Detect): How long it takes to identify that an incident has occurred.
MTTI (Mean Time to Investigate): The time required to assess and understand the scope and severity of an incident.
MTTR (Mean Time to Resolution): The full incident lifecycle — detection through response and resolution.
MTTx: A flexible term for any “mean time to X” metric, such as mean time to contain, recover, or respond.

High MTTR leads to longer dwell times, greater risk exposure, and higher operational costs. Reducing MTTR means:

Stopping attackers before lateral movement or data exfiltration
Limiting downtime and business disruption
Giving analysts time back to focus on proactive defense

Reducing MTTR is a direct path to stronger security, happier analysts, and a more efficient SOC.

How AI, Hyperautomation, and Case Management Can Reduce MTTR

Torq HyperSOC is an autonomous, cloud-native security operations platform designed to reduce MTTR by eliminating manual bottlenecks across the incident lifecycle. Built on the Torq Hyperautomation platform, HyperSOC combines:

Agentic AI (Socrates) to autonomously triage, investigate, and resolve threats
No-code/low-code orchestration for rapid integration with existing tools across SIEM, EDR, IAM, and SaaS environments
Natural language processing (NLP)-powered automation for dynamic workflows, smart case management, and intuitive analyst interaction

How Automation Speeds Detection, Investigation, and Response

Every minute matters in security. HyperSOC uses automation to minimize time spent on repetitive and manual tasks, which directly reduces MTTR.

Automated threat detection eliminates wait time for analyst triage.

Instant data correlation reduces downtime spent stitching logs, alerts, and asset context.

Hands-free auto-remediation triggers the correct response playbooks based on the threat type.Audit-ready documentation is generated in real time, ensuring compliance and traceability.

Use Case #1: Neutralize a Reverse Shell Command & Control (C2) Attack

This example shows how Torq HyperSOC reduced MTTR from hours to under two minutes by automating detection, investigation, and containment, without human intervention.

Threat detection and autonomous response: When a Ruby-powered reverse shell (courtesy of njRAT) targeted an EC2 Linux instance, Socrates got to work. As Torq’s AI SOC Analyst, Socrates detected anomalous process behaviors and network connections, flagging the reverse shell command within seconds.

Real-time enrichment: Without waiting for analyst input, Socrates quarantined the EC2 host. The platform harvested file hashes, process trees, and destination IPs, then enriched them via threat intel feeds and internal CMDB lookups.

AI-generated reporting: Through a deep understanding of the environment and analysis of the remediation runbook associated with the detected use case, Socrates autonomously killed the malicious process in its tracks before the bad actor was able to spread laterally, exfiltrate sensitive data, or cause any further damage. In under two minutes, the HyperSOC dashboard included an AI-generated incident report with prioritized next steps and detailed documentation of every AI-driven action taken.

Result: The threat was detected and neutralized without manual intervention, reducing MTTR and allowing analysts to move on to higher-priority tasks.

The threat was detected and neutralized without manual intervention, allowing analysts to move swiftly to higher-priority tasks. — Torq HyperSOC™ detected and neutralized a Ruby-based njRAT attack on an EC2 Linux instance in under two minutes.

Use Case #2: Reduce MTTR with Automated MITRE ATT&CK Tagging

Manually identifying and tagging MITRE ATT&CK tactics, techniques, and procedures is time-consuming.

Automatic TTP mapping: Socrates can streamline this process by automatically linking and tagging threats with relevant MITRE ATT&CK tactics, techniques, and procedures (TTPs).

Runbook recommendations: The AI Agent parses case data, file hashes, process names, network connections, and behavior patterns, and distills them into discrete observables. Socrates cross-references each observable against the latest MITRE ATT&CK framework — pinpointing the primary tactic and related sub-techniques and procedures. For each matched TTP, Socrates auto-tags the case, links to relevant playbooks, and correlates with past incidents that used the same methods.

Automated scoring: Finally, the AI generates a concise report section that shows:

Tactic: TA0011 – Command and Control
Technique: T1219 – Remote Access Software
Procedure: njRAT reverse shell delivered via Ruby script on EC2 instance.
Confidence: 92%
Potential Impact: Successful execution of these TTPs can lead to unauthorized access and control of critical systems, leading to data breaches or disruptions.
Next Steps: Trigger the containment playbook, notify the Tier-2 SOC analyst team, and run a full asset sweep.

Result: Analysts no longer spend time manually tagging or correlating cases, which helps reduce MTTR and increase consistency across investigations.

Analysts no longer spend time manually tagging or correlating cases, which helps reduce MTTR and increase consistency across investigations. — Socrates auto-tagged MITRE ATT&CK TTPs for a reverse shell incident, cutting MTTR and surfacing next steps in seconds.

Use Case #3: Investigate and Close an Impossible Travel Alert in Minutes

Use Case #3: Investigate and Close an Impossible Travel Alert in Minutes

This case shows how Socrates cut MTTR from 20+ minutes to under three, replacing a manual investigation across multiple tools with a fully automated workflow.

Cross-platform checks: Okta flagged suspicious logins from Austria, Singapore, and Brazil for a single user within a 30-minute window, an impossible travel scenario indicating potential compromise.

Anomaly resolution: Socrates autonomously checked the user’s leave status in Workday and calendar systems. Next, Socrates messaged the employee on Slack, capturing their response directly into the case notes. Simultaneously, it enriched each login IP against external threat intelligence feeds, scoring them for risk and historical malicious activity.

Automated case closure: Socrates then compared the session details against the user’s normal behavior baseline to spot anomalies. Finally, because the user had confirmed the unusual travel and all IP reputations returned legitimate, Socrates marked the alert as a benign true positive, documented the reasoning, and closed the case.

Result: MTTR was reduced to three minutes, false positives were resolved autonomously, and analysts stayed focused on real threats.

This workflow took under three minutes, reducing MTTR and giving analysts hours back by eliminating manual checks and unnecessary escalations. — Socrates investigated suspicious Okta logins, cross-checked HR systems, messaged the user, and closed the alert autonomously.

What These Results Mean for Your SOC

The use cases above aren’t isolated wins — they represent a repeatable, scalable model for transforming your security operations. When you reduce MTTR through AI, Hyperautomation, and intelligent case management, your SOC becomes faster, more resilient, and dramatically more cost-effective.

Proving the ROI of MTTR Reduction

Reducing mean time to resolution doesn’t just make your SOC more efficient — it delivers measurable business value:

Faster resolution = less dwell time and downtime: The longer a threat lingers, the more damage it can do. By shortening the incident lifecycle, your team minimizes business disruption, data loss, and risk exposure.
Fewer escalations = less analyst fatigue: Automating repetitive tasks and low-risk decisions reduces the volume of escalations sent to senior analysts. That frees them up to focus on high-value investigations — and helps reduce burnout.
Higher accuracy = better threat outcomes: With real-time enrichment, contextual tagging, and autonomous decision-making, your SOC can respond more precisely, even under pressure. This leads to faster containment, fewer false positives, and stronger compliance reporting.

Operational resilience = higher ROI: SOCs that reduce MTTR gain more value from their existing tools and staff. You’re not just solving problems faster — you’re using fewer resources.

How to Start Automating Your SOC the Right Way

To reduce MTTR, you don’t need to rip and replace your entire tech stack. The best approach is incremental and targeted, focusing first on areas with high volume, low complexity, and high analyst fatigue.

Start by automating:

High-volume alert triage: Automatically enrich, correlate, and suppress low-risk alerts based on historical context and threat intelligence.
Repetitive enrichment tasks: Automated gathering of user context, asset data, geolocation, IP reputation, and vulnerability information can be done in seconds, not hours.
Access investigations and policy violations: Build workflows that verify unusual access events across IAM, HR, calendar, and communication platforms, then take action based on policy.

These aren’t theoretical benefits; they’re proof points from the frontlines of modern AI-powered SOCs. When the powers of Hyperautomation, AI, and intelligent case management are combined in Torq HyperSOC, your team moves smarter and faster.

Instead of being bogged down, analysts are empowered to lead, strategize, and scale across complex environments. That’s how you reduce risk, retain talent, and prove real value.

Want to see HyperSOC in action? Book a demo now — and don’t miss our Field CISO’s guide full of practical advice for building a more efficient SOC.

Get the Guide

90 Days to SOC Autonomy: How Torq Customers Get There

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Hyperautomation Security Automation 101 Use Cases

The Best Threat Intelligence Tools & How to Automate Alert Enrichment with Torq

By Torq

May 21, 2025

11 Minute Read

Threat intelligence is the cornerstone of proactive security. By collecting and analyzing indicators of compromise (IOCs), tactics, techniques, and procedures (TTPs), and adversary infrastructure, threat intelligence tools help cybersecurity teams spot attacks before they escalate.

But here’s the catch: Most tools stop at surfacing raw intel. They hand you the data but don’t help you operationalize it. This results in analysts drowning in noise, alert fatigue, and slow incident response times.

Explore the top categories of threat intelligence tools and see how Torq Hyperautomation bridges the gap between intel and action, delivering real-time enrichment and autonomous response at scale.

What Are Threat Intelligence Tools?

Threat Intelligence (TI) tools are software and services that collect, enrich, analyze, and distribute information about cyber threats so security teams can detect, prioritize, and respond faster.

What Threat Intelligence Tools Do

Collect data: Ingests signals from OSINT, dark web sources, malware sandboxes, DNS/WHOIS, product telemetry, ISACs, and commercial vendor feeds to build a comprehensive threat picture.

Normalize and enrich: Standardizes formats, deduplicates indicators, and adds context — actor, campaign, TTPs, confidence, and sightings — so data is usable and trustworthy.

Correlate and score: Links indicators to behaviors using frameworks like MITRE ATT&CK and assign risk and confidence to drive prioritization.

Distribute intel: Pushes curated intelligence to SIEM, EDR, or SOAR via APIs and STIX/TAXII, often triggering automated playbooks.

Search and investigate: Lets analysts pivot across IPs, domains, and hashes, build campaign timelines, and track adversary infrastructure.

Report and measure: Provides dashboards, alerts, and takedown and mitigation guidance while tracking coverage and efficacy.

Threat Intelligence Tooling Categories

Feeds (Raw indicators): Continuous streams of IPs, domains, hashes, phishing kits, and C2 infrastructure.
Threat Intelligence Platforms (TIPs): Central hubs that aggregate sources, dedupe and score indicators, enable sharing, and orchestrate automation.
Vertical/Community intel: ISAC/ISAO groups that facilitate trusted, sector-specific sharing of timely threats and mitigations.
Managed TI services: Provider-run offerings where human analysts deliver curated, finished intelligence and advisory support.

4 Types of Threat Intelligence

Strategic (Board/CISO): High-level trends, risks, and business impact to inform investment and policy.
Operational (SOC/IR): Campaign-level insights — adversaries, infrastructure, and TTPs — translated into detections and response actions.
Tactical (Detections): Short-lived IOCs with confidence and expiry to feed blocklists and detection rules.
Technical (Artifacts): Low-level signatures and artifacts — YARA/Sigma rules, decoders, and malware I/O — used to research and codify detections.

While threat intelligence is vital for shifting from reactive to proactive security, most tools stop short of execution. They provide intel but don’t automate triage or incident response, leaving a critical gap in the security kill chain.

Why Threat Intelligence Alone Isn’t Enough

“Threat intelligence — while abundant — is frequently underutilized due to inconsistent application and a lack of objective analysis, keeping teams stuck in reactive mode.”

– SANS 2025 SOC Survey

High-quality threat intelligence is essential for modern security operations, but even the best intel feeds can only take you so far. Many SOC teams still struggle to operationalize that intelligence effectively, facing challenges such as:

Siloed data sources: Threat intel often lives in separate tools and feeds, requiring analysts to manually pivot between consoles to correlate indicators with events in their environment. This not only slows investigations but also risks missing connections entirely.
Alert fatigue from unverified IOCs: Raw intelligence feeds can produce an overwhelming volume of indicators of compromise (IOCs). Without automated context and verification, analysts are forced to triage a flood of alerts, many of which turn out to be irrelevant or false positives.
Slow MTTR due to manual processes: Even when malicious activity is identified, enrichment, prioritization, and incident response often rely on a series of manual steps. This delays containment, gives adversaries more time to act, and increases the likelihood of impact.

The missing link is security Hyperautomation: The ability to take incoming threat intelligence and enrich it in real time, validate it against your environment, prioritize based on risk, and execute the right response automatically.

With Hyperautomation in place, security teams can:

Instantly correlate threat intel with live telemetry from SIEM, EDR, IAM, and cloud security tools.
Automatically filter out low-confidence or irrelevant IOCs before they reach analysts.
Trigger pre-approved auto-remediation workflows such as blocking a domain, isolating an endpoint, or disabling a compromised account in seconds.

Threat intelligence is powerful, but it becomes truly operational when paired with automation. That’s how teams turn static data into actionable, measurable defense at machine speed.

The Power of Automated Alert Enrichment

Threat intelligence enrichment is the critical bridge between raw threat data and meaningful, actionable threat intelligence. It transforms a bare IOC or alert into a fully contextualized security event, giving analysts the information they need to make faster, more confident decisions.

Without enrichment, a malicious IP alert is just a red flag without a story. You know something might be wrong, but you don’t know:

Who controls the IP
When it was first reported as malicious
Whether it has been active in other attacks
If it’s currently interacting with your environment

With threat enrichment, those questions are answered instantly. You can see ownership, reputation scores, historical abuse records, and whether the threat currently targets your assets. This drastically reduces false positives, helps prioritize real threats, and accelerates triage, especially in high-volume SOC environments.

Real-Time Enrichment with Torq

Torq automates this process end-to-end, ingesting IOCs from virtually any source:

Open-source feeds like AbuseIPDB or AlienVault OTX
Commercial CTI platforms such as Recorded Future or CrowdStrike Falcon Intelligence
Internal telemetry from SIEM, EDR, IAM, and CSPM systems

Once ingested, Torq automatically enriches each IOC or alert with:

Threat intelligence lookups for risk scoring and category classification
WHOIS data to identify domain or IP ownership
GeoIP mapping for geographic attribution
Historical incident correlation to see if this IOC has appeared in past investigations

All of this happens without writing a single line of code, using Torq’s no-code/low-code visual builder.

Connecting Enrichment to Automated Response

Enrichment is all about enabling faster, more precise action. With Torq, once an alert is enriched, it can immediately trigger targeted, pre-approved response runbooks, such as:

Block malicious IPs or domains at the firewall or secure web gateway
Disable compromised accounts in IAM systems like Okta or Azure AD
Quarantine infected endpoints via EDR tools like CrowdStrike or SentinelOne
Notify analysts in Slack or Microsoft Teams with full, structured context for review

Because enrichment and incident response are linked in the same Hyperautomation workflow, there’s no waiting for an analyst to manually look up data before taking action — vulnerabilities are validated, prioritized, and remediated in near real time.

Real-World Use Cases: How Torq Elevates Your Threat Intelligence Stack

IOC-Triggered Triage

Scenario: A new malicious IP is published by Abuse.ch’s SSL Blacklist feed.

How Torq Handles It:

The IOC enters Torq through a scheduled or webhook-based integration with Abuse.ch.
Torq automatically enriches it with:
- Recorded Future for risk scoring and threat actor attribution.
- VirusTotal for file and domain associations.
- WHOIS and GeoIP for ownership and location details.
The enriched IOC is compared against SIEM and EDR telemetry to see if it’s active in your environment.
Based on the risk score and internal matches, Torq either:
- Auto-blocks the IP in your firewall and secure web gateway.
- Escalates the IOC to a case in Torq for analyst review.

Result: Threats are validated and acted on within seconds, without manual lookups or context switching.

Autonomous Response to High-Risk Alerts

Scenario: Correlated threat intel and internal detections reveal an active phishing campaign targeting corporate users.

How Torq Handles It:

The IOC feed from a commercial CTI provider flags multiple domains tied to a phishing kit.
Torq cross-references internal email gateway logs to confirm delivery attempts to specific users.
Upon confirmation, Torq executes automated actions:
- Revokes credentials in Okta or Azure AD for targeted accounts.
- Sends a Slack or Teams alert to affected users with security guidance.
- Updates the SIEM with an incident record for correlation and compliance.

Result: Compromised accounts are secured, and users are alerted before threat actors can exploit access.

Threat Intel + Phishing Detection

Scenario: A user reports a suspicious email via the company’s phishing reporting button.

How Torq Handles It:

The reported email is sent to Torq via Microsoft 365 Security or Proofpoint TAP integration.
Torq extracts sender domains, IPs, and embedded URLs.
Those indicators are checked against:
- External threat intel feeds like AlienVault OTX and Abuse.ch.
- Internal blocklists and historical case data in Torq.
If confirmed malicious, Torq:
- Quarantines the email for all recipients at the email gateway.
- Blocks the domain in the web proxy.
- Notifies the reporting user with a “verified malicious” confirmation.

Result: A single user report becomes a fully automated, organization-wide protection action.

Scalable Enrichment Without Developer Overhead

Scenario: The SOC wants to enrich all IOC feeds with cross-platform intelligence but lacks developer bandwidth.

How Torq Handles It:

An analyst drags and drops connectors for Recorded Future, VirusTotal, AbuseIPDB, and MISP into the workflow canvas.
Using Torq’s no-code visual editor, the analyst chains enrichment steps, scoring logic, and conditional response rules.
New threat intel feeds can be added in minutes, and workflows update automatically without engineering intervention.

Result: The SOC scales enrichment capabilities rapidly, integrating multiple TI sources and incident response actions without waiting on dev cycles.

Threat Intelligence Is Only as Good as the Action It Enables

Threat intelligence is the spark that ignites detection, but it’s the action you take with that intelligence that determines whether it prevents an attack or becomes just another line in a report. Without automation, even the most curated and timely feeds leave SOC teams drowning in manual triage, correlation, and remediation steps.

The challenge is operationalizing threat intelligence at machine speed, ingesting, validating, enriching, and acting on it in seconds, not hours. That requires an automation platform that connects intelligence sources directly to your detection, investigation, and response layers.

What to Look for in an Automated Threat Intelligence Stack

To fully realize the value of your threat intel, your automation stack should deliver:

Interoperability: Native integrations with SIEM, SOAR, EDR, firewall, email security, and CTI feeds so threat data flows seamlessly across tools.
Real-time enrichment: The ability to instantly enhance IOCs with reputation scores, geo-location, WHOIS data, historical activity, and related incidents, and feed that context back into detection and response systems.
Scalability: Capacity to process thousands (or millions) of IOCs per day without slowing down, whether from burst attack campaigns or ongoing intelligence streams.
No-code flexibility: The option for analysts to adapt, expand, or fine-tune workflows without relying on developer resources, so you can pivot quickly to new threats.

Why Torq Is Built for Modern Threat Detection

Torq’s Hyperautomation Platform turns raw threat intel into orchestrated action across your SOC. It’s designed to:

Automate at scale with autonomous runbooks that can process and act on high IOC volumes without analyst intervention.
Integrate instantly using agentless, native connectors to 1,000+ tools — from threat intel platforms like Recorded Future, VirusTotal, and MISP to your SIEM, EDR, and firewall stack.
Enable SOC agility through a visual no-code/low-code editor and AI workflow building, so analysts can build or modify enrichment and incident response workflows in minutes.
Drive immediate outcomes — blocking malicious IPs, quarantining emails, disabling compromised accounts, or alerting security analysts— all triggered by enriched intel in real time.

With Torq, threat intelligence isn’t just data; it’s a live signal that moves seamlessly from detection to decision to remediation, without manual processing delays.

Categories of Threat Intelligence Tools Cybersecurity Teams Rely On

Category	Workflow Stage	Purpose	Where Torq Fits	Example Tools
Threat Data Aggregators & Feeds	Collect → Normalize	Centralize raw intel from OSINT, dark web, vendor feeds	Ingests IOCs, auto-dedupes, normalizes to STIX/TAXII, applies TTL, routes to SIEM/EDR with guardrails	AlienVault OTX, Abuse.ch, Recorded Future
Threat Analysis & Correlation	Enrich → Analyze → Hunt	Link IOCs to malware families, campaigns, actors	Automates enrichment and correlation, captures analyst pivots as runbooks, pushes TTPs back to detection	ThreatConnect, Anomali, VirusTotal
Alert Prioritization & Risk Scoring	Triage → Prioritize	Rank alerts by risk and asset criticality	Auto-escalates high-risk alerts, auto-suppresses noise, learns from analyst feedback	Splunk ES, Cisco SecureX, Exabeam
Threat Intelligence Sharing & Collaboration	Share → Collaborate → Govern	Distribute intel across teams & communities	Auto-ingests shared intel, validates, enriches, deploys, feeds outcomes back to community	MISP, OpenCTI, ISAC Portals

Operationalize Threat Intelligence Tools with Torq

Great threat intelligence tools surface what’s out there; Torq turns that signal into outcomes. By ingesting feeds and TIPs, normalizing to common schemas, enriching with WHOIS/GeoIP/reputation, and correlating against your SIEM/EDR/IAM telemetry, Torq’s no-code Hyperautomation moves from detect to resolve in seconds — automatically.

Pre-approved playbooks block domains and IPs, isolate endpoints, revoke access, and notify stakeholders in chat, all with full audit trails and role-based control. The result: lower MTTR, less downtime, fewer manual escalations, a stronger security posture, and a calmer on-call.

If you’re investing in threat intelligence tools but still triaging by hand, you’re leaving value on the table. Pair your intel with automation that’s interoperable, explainable, and scalable so every high-confidence indicator translates into immediate, governed action.

Ready to turn intel into impact? See how Torq can help make your SOC more efficient.

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FAQs

What are examples of threat intelligence?

Examples of threat intelligence include malicious IP addresses, suspicious domain names, file hashes associated with malware, phishing email indicators, and known threat actor infrastructure. More advanced threat intelligence also includes TTPs (tactics, techniques, and procedures) tied to specific threat actors.

What are the four types of threat intelligence?

Strategic: High-level trends and risks for executive decision-making.
Tactical: Information on adversary TTPs for defensive planning.
Operational: Intel on active campaigns and imminent threats.
Technical: Raw indicators like IOCs for detection and blocking.

What are six major sources of cyber threat intelligence?

Open-source threat feeds (e.g., AlienVault OTX, Abuse.ch)
Commercial CTI platforms (e.g., Recorded Future, Mandiant Advantage)
Security product telemetry (SIEM, EDR, XDR)
Dark web monitoring
Industry sharing groups (ISACs/ISAOs)
Government or law enforcement alerts (e.g., CISA, FBI)

What are the best free cyber threat intelligence feeds?

Popular free feeds include AlienVault OTX, Abuse.ch, MalwareBazaar, URLhaus, and various ISAC community feeds. While valuable, they should be supplemented with commercial feeds and automated enrichment for best results.

What does threat intel do?

Threat intelligence helps security teams understand, anticipate, and respond to cyber threats by providing context, patterns, and IOCs that inform detection and incident response workflows.

What are feeds in cybersecurity?

A threat feed is a continuously updated stream of IOCs and threat data that can be ingested into cybersecurity tools like SIEMs and SOAR platforms to enhance detection.

What are examples of threat feeds?

Examples of threat feeds include IP blocklists, malicious domain lists, malware hash databases, and phishing URL repositories.

What is threat feed vs threat intelligence?

Threat feed: A raw data stream containing IOCs.

Threat intelligence: Enriched, analyzed, and contextualized data derived from one or more feeds, ready to be used in decision-making and automated workflows.

90 Days to SOC Autonomy: How Torq Customers Get There

See how Torq customers reach SOC autonomy in 90 days or less.

MSSP

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MDRs are turning to Hyperautomation to overcome the limitations of legacy SOAR solutions.

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The Cybersecurity Lifecycle: How Torq Automates Detection, Response, and Recovery

Automate the cybersecurity lifecycle end-to-end to cut MTTR and strengthen resilience.

AI Autonomous SOC Research

CISOs’ Unconventional Criteria for Evaluating AI SOC Analysts

By Noam Cohen

May 19, 2025

10 Minute Read

Noam Cohen is a serial entrepreneur building seriously cool data and AI companies since 2018. Noam’s insights are informed by a unique combination of data, product, and AI expertise — with a background that includes winning the Israel Defense Prize for his work in leveraging data to predict terror attacks. As the Head of Artificial Intelligence at Torq, Noam is helping build truly next-gen AI capabilities into Torq’s autonomous SOC platform.

Still obsessing over compliance certifications and data volumes when choosing your AI SOC analyst? You might as well be that guy at the dealership kicking tires and demanding V8 specs while ignoring the self-driving capabilities.

Today’s CISO battlefield isn’t won with yesterday’s metrics. While AI security vendors sell you on training corpus size and customization options, you should be demanding zero-day detection without signatures and unified threat visibility.

Let’s be brutally honest: the blistering pace of AI innovation means your current AI SOC evaluation checklist is obsolete. GenAI marked an inflection point; now, agentic AI is completely disrupting SecOps. This means the real competitive edge lies in capabilities your procurement team isn’t even asking about.

So, what should CISOs look for in an AI SOC analyst? Below, we break down 8 key capabilities that you might not have considered but are crucial to ensure AI trust and effectiveness in your SOC.

What to Look for in an AI SOC Analyst Evaluation

1. AI That Simplifies and Communicates Context

Look for: Next-gen AI for the SOC that shows sophistication beyond query-response models, demonstrating a nuanced understanding and delightful communication of organizational context, ongoing security incidents, and specific scenarios.

Rather than summarizing in a generic “TL;DR” format, the AI should communicate about logs, case artifacts, and indicators of compromise (IOCs) through a cybersecurity-oriented UI that highlights key information for the specific security context.

Ask:

Can the AI maintain contextual continuity across analyst shifts and SOC handoffs?
How does the chat UI maintain context for the user when referencing information-heavy items like logs and cases?
Does the AI have different user views for summarizing actions, IOCs, and alerts?
Where can I embed our knowledge and policies to guide the AI’s interactions?

General example:

AI SOC Evaluation example: Example: simplified context communication — General example showing how a smart reference summarization popup from Arc (The Browser Company) helps users quickly understand selected text or an entire webpage without leaving their current browser.

2. AI for the Entire Team

Look for: Practical AI capabilities mapped explicitly to real-world SOC workflows and use cases.

The AI SOC analyst should do the actual, gritty tasks your SOC team performs daily — from initial triage to investigating alerts, hunting for threats, and remediating problems. This isn’t about general intelligence; it’s about directly supporting actual analyst workflows from end to end. If you use a multi-agent system (MAS), the AI SOC analyst should act as an OmniAgent to coordinate and collaborate with multiple specialized AI agents to accomplish these complex security goals.

Ask:

What analyst-level jobs does the AI accelerate (e.g. query writing, unstructured enrichment, and response recommendations)?
How does the AI SOC agent accelerate threat hunting and detection engineering through intelligent hypothesis generation?
Is the system capable of auto-healing errors in security workflows the way a good security engineer can?

General example:

Example of AI for cross-functional teams — *General example showing how Gemini’s Gem store features different chatbots for Marketing, Sales, and Developers.*

3. AI That Explains What It’s Doing

Look for: AI that grounds its findings and recommendations in clear, structured explanations showing its sources.

CISOs increasingly prioritize “explainability” in AI decisions as a pragmatic imperative for achieving cognitive alignment between the AI SOC analyst and the human security team. To foster trust, adoption, and effective action, your security team must have a line of sight into the AI’s reasoning, not just its conclusions.

Ask:

Does the AI SOC analyst clearly explain why particular security events are flagged or escalated?
How easily can human analysts validate or challenge the AI’s recommendations? For instance, can they request source links, exact quotes, or highlighting?
Do we have visibility into the AI agent’s self-critique step?
What validation guardrails does the AI implement?

General examples:

Example of AI that explains what it's doing — *General examples showing how two AI models show the data it relies on. Perplexity shows a snippet of the source while NotebookLM highlights the exact sentence it used from the source.*

4. AI That’s Easy to Interact With — Without Training

Look for: A SOC-specific user interface that is genuinely intuitive, innovative, and frictionless and that directly enhances analyst productivity, retention, and job satisfaction.

Even the most powerful AI can be hampered by a clunky or difficult interface, undermining your team’s effectiveness and morale and discouraging AI adoption. A truly innovative interface should feel natural to use and streamline workflows, not add complexity or friction to processes. An intuitive design enables analysts of any level to quickly access insights and take action without specialized skills or knowledge.

Ask:

How much do our human analysts need to be familiar with AI hacks and general prompt engineering, such as knowing when to use deep search options, ask for a specific data format, or open a new conversation thread?
Does the AI SOC analyst support conversational SIEM queries and natural-language threat exploration?
How does the AI communicate its planning and thinking process?
In autopiloting, can I interrupt the investigation before the AI is done?

General example:

AI SOC Evaluation: example of AI that is intuitive to use — *General example showing how Perplexity creates a simpler user experience by auto-choosing the model according to its research, rather than making the user choose a model by task/prompt.*

5. AI That Helps You Get Ahead

Look for: An AI SOC analyst that doesn’t only react to known threats but proactively guides SOC teams towards improving security posture and operational effectiveness.

Think of your top analysts — the ones who are always one step ahead, anticipating your team’s needs and suggesting improvements without being asked. Agentic AI that performs at this advanced level can act as a virtual extension of your team, identifying weaknesses and suggesting optimizations to elevate your security operations.

Ask:

Can the AI SOC analyst proactively detect and suggest SOC operational improvements, such as recommending repetitive manual processes that are ripe for automation?
Can it automatically correlate cases with incident history and recommend improvements?
Has your AI ever caught a missing step in its instructions and fixed it (or asked about it) before executing?
Can the AI automatically tag and store important information from your interactions that can help in future cases?
Will the AI suggest changes to the detection rules, workflows, or playbooks? How often does your AI flag inefficiencies in workflows?

General example:

Example of AI that proactively recommends optimizations — General example of ChatGPT maintaining context after you’ve told it that you are an AI product manager in San Francisco. When asking it to brainstorm messaging for a social post celebrating an achievement, ChatGPT already knows where to start.

6. AI That Understands What You Really Want (and Can Figure Out How to Do It)

Look for: Deterministic, agentic AI that understands how to break a user intent into multiple tasks, which may require different execution plans.

Good AI gets a task and starts working. Great AI first looks for communication gaps, understands the goal, and asks for more instructions when needed. Ideally, the user shouldn’t have to think like the AI to ensure the AI grasps their intent — the AI should understand how the user thinks and ask clarifying questions when needed.

A structured execution scheme reduces ambiguity and improves the accuracy of the AI’s planning and orchestration, eliminating the likelihood of the AI agent skipping steps, going out of order, selecting incorrect tools, or misinterpreting instructions.

Ask:

When I give the AI a vague or complex instruction, does it ask clarifying questions — or just charge ahead?
How does it use screens, user information, and past sessions to better understand the user’s specific intent?
Can your AI break down a high-level goal (‘Investigate this alert’) into a sequence of logically ordered tasks — and tell you why?
Can your AI explain its execution plan in plain language before it starts and adjust if you push back?

General example:

AI SOC Evaluation: Example of AI that asks clarification questions — *General example showing how ChatGPT asks clarification questions before building a report in Deep Research.*

7. An AI Assistant That You Don’t Need to Babysit

Look for: Agentic AI capable of autonomously chaining together multiple actions without constant human prompts.

Your human analysts don’t want to click through 10 steps every time they need the AI to take action. While human oversight of critical decisions is important, to efficiently investigate an alert end-to-end and even initiate containment, an AI SOC analyst must be capable of independently stringing together a sequence of relevant subtasks — like log collection, enrichment, reverse engineering, and containment suggestions — in pursuit of a high-level goal.

Ask:

Can the AI SOC analyst complete a multi-step investigation with one high-level instruction?
Can the AI write and execute deterministic workflows when needed?
Does it pause and check with human analysts before executing sensitive tasks (e.g., blocking users or IPs)?
When given a high-level goal or non-playbook scenario, does the AI independently decide which steps to take and in what order?
How does the AI identify when not to act — and escalate to a human when it hits a confidence or authority threshold?

General example:

AI SOC Evaluation: Example of AI that defines when it needs to loop humans in — *General example of how Intercom’s Fin interface defines the moments where a human needs to be looped into the convo.*

8. AI That Gets More Helpful Through Human Feedback

Look for: An AI SOC analyst that continuously learns and improves by observing and incorporating feedback from human analyst behavior.

The best AI SOC analysts learn from human analyst behavior to become more effective and accurate over time. Think of it as shaping the ideal analyst that shadows your team, watches how they triage alerts, write queries, and handle false positives — and gets smarter with every interaction.

Human analysts should be able to fine-tune and correct AI as threats evolve rather than treating it as a black box. In practice, features like thumbs-up/down ratings, interactive retraining, or the ability to override AI decisions make the human–AI loop tighter and more effective.

Ask:

How does the AI SOC analyst adapt based on human analysts’ corrections or preferences over time?
Can I adjust the AI’s prioritization or response style via feedback?
How can the user flag a successful conversation with the AI to make future sessions easier and more effective?
Can you review and audit what the AI has learned from your team?

General example:

AI SOC Evaluation: Example of AI that continuously improves — *General example showing how Cursor’s Coding Rules feature helps developers continuously improve and adapt their preferences using natural language.*

Next-Gen AI for the SOC is Here — Are You Ready?

Don’t be the security leader who marvels at a shiny paint job while ignoring the revolutionary engine. When evaluating AI SOC analysts, focus on explainable intelligence, seamless integration into your team’s workflow, and deterministic AI that can independently plan and orchestrate all of the actions required to complete a high-level goal from end to end.

Finding an AI SOC analyst that truly understands context, empowers your analysts, and acts with proactive autonomy will ensure you’re not just keeping up with the latest tech but investing in a force multiplier for your security team.

Get the AI or Die Manifesto to learn strategic considerations, get insights from a CISO, and learn red flags and more questions to ask for an AI SOC evaluation.

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Research Security Automation 101 Use Cases

What is Cyber Threat Hunting? How to Stay Ahead of Attacks

By Torq

May 15, 2025

10 Minute Read

Cyberattacks are becoming more frequent and sophisticated as threat actors continually sharpen their tactics and upgrade their tools. Defending against these evolving threats is increasingly complex, especially in a landscape where cybersecurity ROI is measured in loss prevention rather than revenue generation.

Cyber threat hunting offers a proactive way to secure your environment by actively seeking out threats that evade traditional defenses. However, manual threat hunting is time-consuming, resource-intensive, and complicated by a growing shortage of skilled professionals.

In this blog, we’ll unpack everything you need to know about cyber threat hunting and show how Hyperautomation can help your team stay ahead of attackers by streamlining detection, investigation, and response without requiring massive overhead.

What is Threat Hunting in Cybersecurity?

Cyber Threat Hunting

Cyber threat hunting is a proactive cybersecurity process where trained individuals, called threat hunters, actively search for malicious cyber activities and potential threats that might have evaded traditional security measures. It involves going beyond automated security tools and alerts to investigate and identify hidden threats within a network or system.

The value of cyber threat hunting lies in these key properties:

Proactive approach: Unlike traditional security measures that react to alerts, threat hunting is a proactive process. Threat hunters actively seek out potential threats rather than waiting for them to be detected or, worse, erupt into a critical incident.
Augmenting automated systems: Threat hunting complements automated security tools by identifying threats that may have slipped past those systems.
Human expertise: It relies on the knowledge and skills of threat hunters who use their expertise, tools, and methodologies to identify malicious activities.
Targeted searches: Threat hunters develop hypotheses about potential threats based on threat intelligence, known attack techniques, and other factors, then they search for evidence to validate those hypotheses.
Focus on advanced threats: Threat hunting is beneficial for identifying advanced persistent threats (APTs) and other sophisticated attacks that can evade traditional security measures.

Why is Cyber Threat Hunting Important?

Most SOC tools operate reactively — they wait for indicators of compromise (IOCs) or known attack signatures to trigger alerts. However, today’s adversaries are stealthy, often residing in networks undetected for weeks or months. Cyber threat hunting flips the script.

Threat hunting proactively searches for unknown, suspicious behavior and zero-day threats that traditional detection tools miss. The benefits include:

Early threat detection and response: Threat hunters spot anomalies before damage occurs, enabling rapid, contained responses to reduce breach impact. Early detection and response can significantly reduce the potential damage and costs associated with cyberattacks.
Identification of persistent and complex threats: Advanced persistent threats (APTs) often evade SIEMs or endpoint detection and response (EDR). Threat hunting reveals long-dwelling attackers using subtle tactics.
Improved incident response efficiency: Hunting improves context and decision-making for incident response (IR) teams, reducing mean time to investigate (MTTI) and resolve (MTTR). By identifying and mitigating threats proactively, threat hunting strengthens an organization’s overall security posture.
Enhanced threat intelligence: The insights gained from threat hunting can also improve an organization’s threat intelligence and help them better understand their adversaries.

How Cyber Threat Hunting Works: 6 Methods

Cyber threat hunting isn’t a single technique — it’s a flexible, proactive approach that combines human expertise with data, context, and tooling. Depending on your team’s goals, tools, and maturity level, different methodologies can be used to uncover hidden threats and eliminate adversaries before they cause damage. Here are six of the most effective threat hunting methods in use today.

1. Hypothesis-Driven Hunting

This method begins with a well-formed theory about how an adversary might be operating within your environment. Hunters often base these hypotheses on current threat intelligence, past incidents, or a known threat actor’s tactics.

For example, a threat hunting team may ask, “Is an attacker using PowerShell for lateral movement across endpoints?” They then query logs, examine user activity, and look for anomalies that might validate or disprove that theory. This structured, scientific approach allows analysts to pursue purposeful leads and systematically uncover sophisticated threats.

2. Indicator of Attack (IoA)-Based Hunting

Rather than reacting to alerts, IoA-based threat hunting proactively searches for signs of attacker behavior that signal malicious intent — even if no breach has occurred. Analysts look for behavioral patterns and tactics often used by adversaries, such as a sudden surge in failed login attempts, suspicious registry modifications, or abnormal user behavior during off-hours.

By focusing on indicators of attack (IoAs) instead of indicators of compromise (IoCs), teams can identify active intrusion attempts earlier in the kill chain, often before data exfiltration or lateral movement occurs.

3. Advanced Analytics and Machine Learning

Threat hunting at scale benefits significantly from security automation, particularly through advanced analytics and machine learning (ML). These AI models are trained on historical attack data and behavioral baselines, helping analysts identify statistical anomalies and outliers across massive datasets.

For example, suppose a user suddenly begins downloading gigabytes of data from an unfamiliar endpoint. ML-driven tools can flag the deviation from normal behavior in that case, even if no specific IoA has been defined. This method increases speed and coverage, especially in cloud or hybrid environments.

4. Structured Hunting

Structured threat hunting leverages formal models and frameworks like MITRE ATT&CK to organize and guide investigations. By using well-defined tactics, techniques, and procedures (TTPs), analysts can systematically scan for known threat behaviors across endpoints, identities, and networks.

This method is beneficial for standardizing team processes, ensuring knowledge sharing, and aligning with compliance or threat modeling requirements. It also enables better documentation and repeatability of hunts, making it a valuable tool for maturing a cybersecurity program.

5. Unstructured Hunting

Unstructured hunting relies more on analyst intuition and real-world experience than on formal rules or frameworks. In this method, seasoned hunters follow their instincts, identifying suspicious patterns, log entries, or correlations that don’t match any known indicators — but still “feel off.”

This open-ended approach can surface novel attacks, zero-day behaviors, or insider threats that evade automated detection. While more time-consuming, unstructured hunting is crucial in developing hypotheses for future structured hunts and refining detection rules.

6. Situational or Entity-Driven Hunting

This method prioritizes hunting based on specific contexts — such as critical assets, high-risk users, or sensitive business functions. For example, threat hunters may target systems housing personally identifiable information (PII) or monitor executive accounts likely to be targeted in phishing or business email compromise (BEC) attacks.

Situational or entity-driven hunting ensures security teams protect what matters most by focusing on high-value targets and contextual threat intelligence. It can also quickly act on suspicious activity that might otherwise get lost in the noise.

Cyber Threat Hunting Process

Effective threat hunting follows a straightforward process. Here’s how top-performing teams approach it.

Trigger: A hunt often starts with a clue — a suspicious login, a new TTP from a threat intel feed, or a hunch. Triggers inform what to investigate.
Investigation: Hunters use SIEM, EDR, network traffic, and log data to dig deeper. Enrichment, correlation, and historical context help determine risk.
Resolution: If a threat is confirmed, it’s escalated for response, and hunting insights are used to improve detection rules and workflows in the future.

Cyber Threat Hunting Tools & Technologies

SIEM (Security Information and Event Management): Aggregates logs across systems to provide a centralized view and real-time alerts.
EDR (Endpoint Detection and Response): Monitors and records activity on endpoints to uncover suspicious behavior.
MDR (Managed Detection and Response): Outsources threat detection and response to external experts, ideal for resource-limited teams.
Hyperautomation: Goes beyond basic automation. Combines AI, orchestration, and no-code/low-code tools to scale SOC threat detection and response with minimal manual effort. Hyperautomation platforms like Torq can enrich alerts, orchestrate threat investigations, and even automate entire cyber threat hunting workflows.

4 Cyber Threat Hunting Challenges & How to Navigate Them with Torq

Cyber threat hunting is an essential pillar of modern cybersecurity strategy, but it’s not without its obstacles. Today’s SOC teams face increasing complexity, resource constraints, and alert overload, which can hinder their ability to detect and respond to threats proactively.

Below are four of the most common challenges security teams encounter in threat hunting, along with how Torq’s Hyperautomation platform directly addresses them with AI-driven precision and scale.

1. Integrating Disparate Data Sources

The Challenge: Threat hunters rely on data from SIEM, EDR, firewalls, and cloud environments, which are often siloed.

How Torq Helps: Torq Hyperautomation breaks down these silos by integrating your entire security stack into a unified, low-code automation engine. With hundreds of pre-built integrations, Torq enables real-time data normalization, enrichment, and orchestration across all sources. Threat intel from platforms like VirusTotal or Recorded Future can be automatically enriched into alert streams, providing analysts with actionable context — fast. This consolidated view eliminates blind spots and empowers threat hunters to act confidently and quickly.

2. Alert Fatigue

The Challenge: Analysts drown in noisy, low-value alerts, making it difficult to spot real threats.

How Torq Helps: Torq uses agentic AI to combat alert fatigue. Torq ensures that only high-confidence, context-rich alerts reach analysts by filtering out noise, deduplicating alerts, and applying real-time prioritization logic. Low-risk or redundant alerts are automatically suppressed, and high-severity incidents are escalated to the right person or team through customized workflows. This triage process reduces alert volume by up to 95%, allowing teams to focus on what truly matters — critical threats that require human judgment.

3. False Positives

The Challenge: Traditional tools generate too many “maybe” threats — wasting time and delaying response. In fact, more than half of security teams say that false positives are a huge problem.

How Torq Helps: Torq uses intelligent case automation and prioritization to differentiate between real threats and false alarms intelligently. By analyzing historical resolution data, Torq can fine-tune playbooks to automatically suppress known false positives while continuously learning and adapting to your unique environment. This self-optimizing capability reduces alert fatigue and improves detection, cutting through the noise to surface high-priority incidents faster.

4. Limited Resources

The Challenge: Skilled threat hunters are in short supply — and expensive.

How Torq Helps: Torq HyperSOC empowers teams of all skill levels to participate in advanced threat hunting. Its intuitive low-code interface allows junior analysts to build and execute workflows without needing deep coding experience. Meanwhile, Torq’s AI agents led by Socrates, automatically handle routine triage, enrichment, and correlation, freeing up senior analysts to focus on deep-dive threat analysis and strategic improvements. The result is an autonomous SOC that can scale without scaling headcount.

The Bottom Line

Cyber threat hunting is too important to be slowed down by fragmented tools, noisy alerts, or stretched resources. Torq Hyperautomation modernizes the threat hunting process by combining unified data integration, real-time alert intelligence, and agentic AI, enabling any SOC team to hunt smarter, faster, and more efficiently.

Ready to eliminate your threat hunting roadblocks? See Torq Hyperautomation in action and learn how to evolve from reactive to proactive security today.

Get a Demo

Security Orchestration, Automation, and Response (SOAR) promised streamlined workflows, rapid incident responses, and reduced security analyst workloads. But as cybersecurity threats grow more sophisticated, legacy SOAR solutions revealed their critical limitations. Static, rigid workflows and cumbersome integration processes have left many SOCs overwhelmed, struggling with slow response times, high security alert fatigue, and fragmented security toolsets.

Today, traditional SOAR platforms are becoming obsolete, unable to keep pace with rapidly evolving cyber threats. Legacy SOAR solutions typically rely on static playbooks and manual script updates, which quickly become outdated, failing to adapt dynamically to new threats or changing environments. Additionally, traditional SOAR platforms often come with steep learning curves, extensive deployment timelines, and hidden cost, which limit their practicality and reduce their overall ROI.

Hyperautomation and advanced agentic AI tools like Torq offer a powerful alternative, transforming security operations by automating dynamically, intelligently, and at scale. Unlike legacy SOAR, Hyperautomation provides flexibility with no-code workflows, real-time contextual enrichment, and seamless integrations, eliminating the need for extensive manual intervention and continuous maintenance. By leveraging advanced AI-driven tools, SOC teams can proactively manage threats, dramatically reduce analyst fatigue, and significantly improve response times.

What is SOAR in Cybersecurity?

SOAR

Legacy Security Orchestration, Automation, and Response (SOAR) was built to stitch together security tools — but relies on brittle scripts and static playbooks. It promised efficiency but delivered complexity, blind spots, and burnout. It’s dead because the modern SOC demands dynamic, AI-driven action, not legacy code and lag time. See why SOAR is dead.

SOAR is composed of three components:

Orchestration: Orchestration connects disparate security tools into a cohesive ecosystem. SOAR tools coordinate actions and share data across multiple platforms by integrating various security solutions..
Automation: Automation enables SOC teams to execute repetitive security tasks without human intervention. Common automated actions include blocking IP addresses, isolating infected endpoints, or generating reports..
Response: Security orchestration and automation provide the foundation for response. Response is where detection turns into action.

How Does SOAR Work?

Data collection: SOAR aggregates alerts and telemetry from SIEMs, firewalls, cloud environments, endpoints, and threat intelligence sources to provide centralized visibility.

Data analysis: It applies correlation rules or basic machine learning to identify indicators of compromise (IOCs), anomalies, or attack patterns.

Enrichment: Alerts are enriched with contextual data like user behavior, asset value, or known threat intelligence to support investigation.

Triage and investigation: Automated playbooks classify incidents by type or severity. Analysts manually investigate with supporting evidence and logs.

Response: Once verified, predefined playbooks carry out static actions like isolating devices, disabling accounts, or opening IT tickets.

By orchestrating and automating these stages, SOAR platforms aimed to improve incident response times, reduce human error, and standardize security operations. However, traditional SOAR often falls short due to rigid playbooks, brittle integrations, and high maintenance requirements.

Why SOAR Fell Short — and How Hyperautomation Delivers

SOAR was supposed to be the silver bullet for overloaded SOCs, promising faster response, streamlined workflows, and fewer manual tasks. But, in practice, legacy SOAR platforms introduced new complexity, slowed response times, and failed to adapt to real-world threats.

Torq Hyperautomation™ was purpose-built to fix what SOAR broke. It eliminates the inflexible playbooks, easy-to-break integrations, and alert overload that plague traditional platforms, replacing them with intelligent, adaptable workflows that actually deliver on the promise of automation. Here’s how they compare.

Response Time to Incidents

Reality: SOAR workflows are code-heavy, slow to implement, and difficult to adapt, significantly limiting response speed.

Torq Advantage: Torq uses real-time, no-code/low-code workflows that adapt instantly, enabling immediate response without extensive engineering or programming expertise. Security teams can respond to threats the moment they’re detected, without delays.

Analyst Fatigue

Reality: SOAR solutions require extensive manual setup, continuous maintenance, and scripting, further burdening analysts.

Torq Advantage: Torq’s AI-assisted automation is ready out-of-the-box and requires minimal upkeep, significantly alleviating SOC analyst fatigue by automatically handling repetitive tasks.

Fewer False Positives

Reality: Static correlation rules in legacy SOAR platforms often lack necessary context, resulting in a high volume of false positives that inundate analysts.

Torq Advantage: Torq dynamically enriches alerts with real-time, contextual intelligence, automatically prioritizing legitimate threats and dramatically reducing false positives.

Centralized Visibility and Control

Reality: Legacy SOAR platforms typically require cumbersome custom integrations, causing data silos and fragmented visibility.

Torq Advantage: Torq integrates seamlessly with hundreds of security tools, delivering immediate unified visibility and actionable insights from the start.

Collaboration Across Teams

Reality: SOAR isolates SOC teams with dashboards that don’t effectively bridge departmental gaps or workflow handoffs.

Torq Advantage: Torq proactively shares enriched alerts and contextual data directly via collaboration tools like Slack, Jira, and Teams, enabling cross-departmental efficiency and accelerated decision-making.

Efficiency and ROI on Existing Security Tools

Reality: Complex SOAR deployments often result in shelfware due to their slow implementation, limited scalability, and difficulty in maintenance, severely restricting efficiency and ultimately ROI.

Torq Advantage: Torq provides immediate deployment, effortless scalability, increased SOC efficiency, and continuous enhancement of existing security tools, resulting in quick, measurable ROI improvements.

SIEM Integration

Reality: Legacy SOAR systems were meant to complement SIEM by responding to alerts faster. Instead, they add friction, slowing down triage and overwhelming analysts with manually tuned workflows that can’t scale with modern SIEM telemetry.

Torq Advantage: Torq seamlessly ingests SIEM alerts and enriches them with real-time context from across the security stack, automatically prioritizing, triaging, and triggering response workflows without manual effort. It transforms SIEM data from noise into action, accelerating time-to-response and eliminating the bottlenecks SOAR was supposed to solve.

Repeatable, Scalable Response Workflows

Reality: Static SOAR playbooks become outdated and ineffective as threats evolve and environments shift.

Torq Advantage: Torq’s dynamic workflows adapt automatically, staying continuously effective in combating evolving threats and environmental changes, ensuring resilience and scalability for any size organization.

Threat Intelligence Automation and Utilization

Reality: Traditional SOAR tools struggle to utilize threat intelligence effectively, resulting in missed opportunities for proactive measures and a reactive security posture

Torq Advantage: Our platform automatically correlates threat feeds with real-time alerts and events, instantly enriching cases with context that would otherwise take hours to collect. Analysts get a full picture of the threat landscape without leaving their workflow, enabling faster, smarter decisions and more successful threat hunting.

Integrated Vulnerability Management

Reality: SOAR platforms keep vulnerability management in a silo, disconnected from the broader incident response cycle.

Torq Advantage: Torq bakes vulnerability management directly into incident response. Our platform continuously pulls in vulnerability data, prioritizes it based on live threat intelligence, and automates the next best action — whether that’s patching, escalating, or isolating impacted systems. That means zero delay between discovering a weakness and neutralizing it.

Optimized Threat Hunting Capabilities

Reality: Threat hunting with SOAR often means toggling between tools, manually stitching together clues, and hoping nothing slips through the cracks. It’s slow, disjointed, and easy to get wrong.

Torq Advantage: Torq brings everything together, from data sources to actions, in a single, Hyperautomated workflow. Analysts can launch cyber threat hunts with one click, rely on Torq to handle enrichment and correlation, and focus their time on analysis and response.

Keep Up With Threats You Haven’t Seen Yet

Reality: As cyber threats continue to evolve, traditional SOAR solutions are unable to keep pace, leaving SOC teams at a disadvantage.

Torq Advantage: Torq HyperSOC^TM is built for change. With a no-code interface, AI architecture, and agentic AI, SOC teams can adapt to new threats in minutes. Whether onboarding a new tool, facing a new TTP, or launching an entirely new use case, Torq gives the agility to do it at machine speed.

The Pitfalls and Shortcomings of Traditional SOAR Platforms

So, where did SOAR go wrong? Despite its early promise, legacy SOAR platforms are buckling under the weight of today’s security demands, plagued by technical debt, operational friction, and outdated architecture. Here’s where they fall short:

Steep learning curve and complexity: SOAR solutions often require specialized knowledge, making them difficult and time-consuming to deploy and manage.
Static playbooks: Playbooks built in traditional SOAR tools lack flexibility, quickly becoming outdated and ineffective.
Poor integrations and limited interoperability: Integration complexities frequently result in limited interoperability, leaving critical data fragmented across isolated tools.
Disconnected tools, fragmented data: Despite promises of centralization, many SOAR platforms leave vital security tools disconnected, exacerbating inefficiencies.
Alert overload: Without dynamic context, traditional SOAR platforms struggle to differentiate legitimate threats from noise, overwhelming security analysts.
Long implementation timelines: Implementing SOAR solutions can take months, significantly delaying any potential benefits.
High cost with limited ROI: Legacy SOAR investments often fail to deliver sufficient value due to high upfront costs, ongoing maintenance expenses, and poor usability.

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

SOAR is Dead, Thanks to Hyperautomation

As cybersecurity threats grow more advanced and SOC teams face escalating pressure, legacy SOAR simply can’t keep up. Torq’s Hyperautomation platform replaces outdated SOAR with a smarter, faster, and far more adaptive solution. Built for the modern SOC, it combines AI-native automation, limitless integrations, and scalable cloud architecture to solve problems SOAR was never designed to address.

Torq Hyperautomation transcends traditional SOAR capabilities by introducing:

Hyperautomation and dynamic workflows: Unlike traditional SOAR platforms with rigid, linear playbooks, Torq’s Hyperautomation workflows are built to support complex logic. This enables security teams to design multiple response paths within a single workflow. This allows teams to easily look for exceptions, outliers, and conditional scenarios without rewriting or reconfiguring playbooks each time a threat or environment changes.
No-code/low-code integrations: Security teams can integrate any tool or data source in minutes, eliminating the development bottlenecks and vendor lock-in associated with traditional SOAR.
AI-assisted decision-making: Torq’s multi-agent system, led by Socrates the AI SOC Analyst, doesn’t just follow rules — it plans, adapts, and makes autonomous decisions based on contextual awareness. It handles most Tier-1 tasks without human input and elevates complex cases with intelligent summaries and prioritization.
Context-aware playbooks: Legacy SOAR relies on static if/then logic. Torq replaces that with workflows that adjust actions based on threat intelligence, user identity, behavioral context, and risk level.
Cloud-native, scalable architecture: SOAR’s monolithic architecture creates scaling headaches and performance ceilings. Torq’s elastic, event-driven architecture scales horizontally with guaranteed SLAs, real-time API sync, and zero performance degradation, whether you’re processing 10 events per hour or 10,000 per second.

The result is a complete transformation of security operations. Hyperautomation doesn’t just automate response; it enables continuous detection, intelligent triage, enriched case management, and full-lifecycle resolution.

Where SOAR added layers of complexity, Torq removes them. Where SOAR overwhelmed security analysts, Torq augments them. And where SOAR promised outcomes it couldn’t deliver, Torq is delivering those outcomes.

Move Beyond SOAR to Hyperautomation

While SOAR was a significant step forward in security automation, its limitations are evident. Modern SOC teams require dynamic, adaptive, and intelligent tools that can scale effortlessly and deliver immediate value.

Hyperautomation, as delivered by Torq, empowers SOCs to achieve true operational agility, dramatically faster response times, and improved overall security posture, without the complexity and rigidity of traditional SOAR.

Luckily, if you’re already using a SOAR platform, Torq makes migration effortless. Torq Hyperautomation can ingest your existing workflows, integrate with your current tools, replicate, and radically improve your existing use cases.

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Contents

What Is a Multi-Agent System?

Multi-Agent System Defined

Why Multi-Agent AI Outperforms Single AI Agents

How Do Multi-Agent AI Systems Work in the SOC?

Multi-Agent System Use Cases In the SOC

Alert Triage at Scale

Runbook Orchestration

Incident Response

Threat Hunting

The World’s First Multi-Agent System for The SOC

Socrates, the AI SOC Analyst

Runbook Agent

Investigation Agent

Remediation Agent

Case Management Agent

The Future of SecOps: The Autonomous SOC Powered by Multi-Agent AI

Quiz: Which Torq AI SOC Agent Has Your Back?

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Torq for MDRs: Increase Margin and Onboard Customers Faster

The Cybersecurity Lifecycle: How Torq Automates Detection, Response, and Recovery

Contents

The SOC Efficiency Challenge

Why Reducing MTTR Is the Key to SOC Efficiency

What is MTTR in Cybersecurity?

How AI, Hyperautomation, and Case Management Can Reduce MTTR

How Automation Speeds Detection, Investigation, and Response

Use Case #1: Neutralize a Reverse Shell Command & Control (C2) Attack

Use Case #2: Reduce MTTR with Automated MITRE ATT&CK Tagging

Use Case #3: Investigate and Close an Impossible Travel Alert in Minutes

What These Results Mean for Your SOC

Proving the ROI of MTTR Reduction

How to Start Automating Your SOC the Right Way

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Torq for MDRs: Increase Margin and Onboard Customers Faster

The Cybersecurity Lifecycle: How Torq Automates Detection, Response, and Recovery

Contents

What Are Threat Intelligence Tools?

What Threat Intelligence Tools Do

Threat Intelligence Tooling Categories

4 Types of Threat Intelligence

Why Threat Intelligence Alone Isn’t Enough

The Power of Automated Alert Enrichment

Real-Time Enrichment with Torq

Connecting Enrichment to Automated Response

Real-World Use Cases: How Torq Elevates Your Threat Intelligence Stack

IOC-Triggered Triage

Autonomous Response to High-Risk Alerts

Threat Intel + Phishing Detection

Scalable Enrichment Without Developer Overhead

Threat Intelligence Is Only as Good as the Action It Enables

What to Look for in an Automated Threat Intelligence Stack

Why Torq Is Built for Modern Threat Detection

Categories of Threat Intelligence Tools Cybersecurity Teams Rely On

Operationalize Threat Intelligence Tools with Torq

FAQs

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90 Days to SOC Autonomy: How Torq Customers Get There

Torq for MDRs: Increase Margin and Onboard Customers Faster

The Cybersecurity Lifecycle: How Torq Automates Detection, Response, and Recovery

Contents

What to Look for in an AI SOC Analyst Evaluation

1. AI That Simplifies and Communicates Context

2. AI for the Entire Team

3. AI That Explains What It’s Doing

4. AI That’s Easy to Interact With — Without Training

5. AI That Helps You Get Ahead

6. AI That Understands What You Really Want (and Can Figure Out How to Do It)

7. An AI Assistant That You Don’t Need to Babysit

8. AI That Gets More Helpful Through Human Feedback

Next-Gen AI for the SOC is Here — Are You Ready?

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