Most organizations automate pieces of their Security Operations Center (SOC), but true enterprise automation remains out of reach. Across IT, compliance, HR, and business operations, manual processes still dominate. All of it drains time, slows teams, and keeps skilled people locked in low-impact work.
The truth is, automation shouldn’t live in one department. The same intelligence that speeds incident response can just as easily simplify IT workflows, accelerate business processes, and connect systems across the enterprise. That’s the future companies like Bloomreach are building — where enterprise automation is not a security initiative, but an operational foundation.
The Modern SOC Challenge
Even mature SOCs face the same blockers that limit broader enterprise automation:
Too many tools, too few connectors: Disjointed systems slow response and duplicate effort.
Developer dependency:Traditional SOAR tools demand scripting skills, leaving automation siloed with a few experts.
Adoption barriers: Teams outside security rarely touch these tools, limiting ROI and innovation.
Those challenges were clear for Bloomreach, a global technology company known for its AI-driven digital experience platform. Their SOC ran 24×7 — but legacy SOAR tooling kept automation confined to a small group of developers. Other teams saw its potential but couldn’t use it.
To scale automation beyond the SOC, Bloomreach needed an intuitive, flexible, and AI-powered platform anyone could adopt.
Enter Hyperautomation: One Platform for Enterprise Automation
When Bloomreach adopted Torq HyperSOC™, their goal was to modernize the SOC — but it soon became so much more than that. Torq’s no-code, low-code environment meant every analyst could build, test, and launch workflows without a heavy technical lift.
“We wanted everybody on the team, including junior analysts, to be able to build automations — not just developers. With traditional SOAR, that wasn’t possible.”
– Chris Talevi, Deputy CISO at Bloomreach
Within weeks, Bloomreach’s analysts had automated key SOC workflows like phishing triage and user authentication validation. The success sparked something bigger: adoption across departments.
Torq quickly became more than a SOC tool. Its adaptability allowed Bloomreach to connect workflows across security, IT, and business systems, driving consistency and scale throughout operations.
SOC automation: Phishing triage, identity checks, and threat enrichment now run automatically. With AI assistance from Socrates, Torq’s AI SOC Analyst, alerts are enriched, verified, and prioritized, freeing human analysts to focus on deeper investigation.
IT and help desk workflows: The IT team extended automation to account management — automatically verifying users, resetting credentials, and validating HR data through chat-based workflows. What used to take hours is now resolved in minutes, cutting ticket volume and reducing repetitive support work.
Threat intelligence summaries: Instead of manually parsing reports, Torq aggregates and summarizes global threat feeds using large language models (LLMs), publishing concise updates into Slack for real-time action.
Business intelligence automation: The Business Intelligence team automated Salesforce renewals and order updates, reducing manual follow-up and ensuring smoother handoffs between revenue and operations teams.
“We didn’t want automation to be just for the SOC — we wanted something adaptable across teams. Torq made that possible.”
– Chris Talevi, Bloomreach
The Results: Enterprise Adoption, Time Savings, and Scale
100% of Tier-1 and Tier-2 tasks handled autonomously by AI
Three departments (SOC, IT, BI) using Torq with near-total adoption
Analysts at every level empowered to build and maintain workflows
What began as SOC automation became a blueprint for company-wide efficiency. Every team now operates more efficiently, with AI handling repetitive tasks and humans focusing on strategic outcomes.
“Torq levels up the type of work analysts can perform. It removes repetitive tasks and gives them time to focus on higher-value work.”
– Chris Talevi, Bloomreach
Enterprise Automation Without Boundaries
Enterprise automation shouldn’t stop at the edge of the SOC. The same platform that powers detection and response can power IT operations, business processes, and data workflows across an entire organization.
Bloomreach’s journey shows what’s possible when automation is democratized. By expanding beyond security, they built a connected operational ecosystem — one that is faster, smarter, and more resilient.
With Hyperautomation, enterprises aren’t just defending the business — they’re transforming how it runs.
Security operations are evolving — because they have to. The old model of human-dependent monitoring, manual ticket creation, and siloed tools is breaking under the weight of cloud complexity and relentless attack volume.
Today’s enterprise requires a new kind of agility. It demands security operations that are context-aware, Hyperautomated, and capable of responding at machine speed. But for many organizations, the reality is still reactive busywork. Teams are drowning in noise, switching between a dozen dashboards, and struggling to scale.
Torq changes that. By serving as the connective tissue for your entire security stack, Torq Hyperautomation enables smart, automated, and cloud-scalable operations that transform your SOC from a cost center into a resilient, always-on defense engine.
What Are Security Operations?
Security operations (SecOps) is the discipline responsible for monitoring, detecting, analyzing, and responding to cyber threats across an organization. It’s the day-to-day engine that keeps your defenses running.
These functions typically live within the Security Operations Center (SOC), a centralized hub of people, processes, and technology dedicated to protecting the organization’s information assets.
A security operations program manages critical functions, including:
Continuous monitoring: Real-time surveillance of networks, endpoints, clouds, and applications
Incident response (IR): The structured approach to addressing and managing the aftermath of a security breach or cyberattack
Vulnerability management: Identifying, evaluating, treating, and reporting on security vulnerabilities
Log analysis and SIEM/XDR management: Collecting, normalizing, and analyzing telemetry to detect suspicious behaviors and patterns
The team behind these functions typically includes:
Tier 1 analysts (alert triage and initial investigation)
Tier 2/3 analysts and Incident Responders
Threat Hunters and Security Engineers
SecOps / Detection Engineers
A SOC Manager overseeing the day-to-day operations
The CISO aligning operations with business risk, compliance, and continuity goals
The Challenges of Traditional Security Operations
Despite massive investment, many SOCs are failing to keep pace. They are hindered by legacy processes that simply cannot scale to meet modern threat volumes.
Alert Fatigue and Triage Overload
Alert fatigue is the single biggest killer of SOC morale and efficiency. Analysts are flooded with thousands of alerts daily from SIEMs, EDRs, and cloud monitors. A large portion of alerts goes uninvestigated, is of low fidelity, or turns out to be a false positive. This forces highly skilled analysts to spend their days manually clicking ‘dismiss’ or chasing ghosts, leading to missed genuine threats amidst the noise.
Siloed Tools and Data Sources
The average enterprise security stack has dozens of disconnected tools — endpoint protection here, identity management there, cloud security somewhere else. This fragmentation makes it nearly impossible to correlate threats or automate workflows effectively. Analysts waste valuable time manually piecing together data from disparate systems to get a coherent picture of an attack.
Staff Shortages and Burnout
The cybersecurity talent gap is real, but burnout is the bigger issue. High-pressure environments, repetitive manual tasks, and the feeling of never being “caught up” drive high turnover rates. Scaling response capacity by simply hiring more bodies is expensive and increasingly ineffective.
Manual Response Processes
In many SOCs, common workflows still look like this:
Alert arrives in one tool
Analyst copies details into another
Analyst opens a ticket in ITSM
Analyst pings someone on Slack or email
Analyst waits for action
Analyst updates the ticket by hand
These manual steps introduce significant latency in both detection and response (MTTD/MTTR), giving attackers more time to move laterally, escalate privileges, or exfiltrate data.
What Does a Modern Security Operations Center Look Like?
To survive in the modern threat landscape, the SOC must evolve. It can no longer be a reactive ticket-taking factory. It must become a proactive, automated nerve center.
Cloud-Native and Tool-Agnostic
Modern SOCs protect hybrid and multi-cloud environments, plus SaaS systems and distributed workforces — not just on-prem networks. They must be:
Cloud-native: Able to ingest and act on telemetry from AWS, Azure, GCP, and SaaS platforms
Tool-agnostic: Able to integrate with whichever SIEM, EDR, IAM, CSPM, and ITSM tools you already use
Flexible: Able to swap or add tools without re-architecting security operations from scratch
Driven by Automation and Orchestration
In a modern SOC, workflows replace manual playbooks. Automation isn’t an afterthought; it is the foundation. Security operations workflows handle the heavy lifting of data ingestion, enrichment, and initial triage, ensuring that human analysts only engage when their expertise is truly required. This moves response from “whenever someone can get to it” to real-time or near real-time.
Continuous Detection and Response
Rather than periodic scans or ad hoc investigations, modern SOCs aim for continuous detection and response in which:
New alerts and signals are evaluated immediately
Identity, endpoint, cloud, and network context are applied automatically
Follow-up actions are orchestrated as soon as risk is confirmed
This isn’t a formal cybersecurity standard like NIST CSF, but a practical operating mode: continuous risk evaluation, continuous enforcement, continuous improvement.
Unified Dashboards and Metrics
You can’t optimize what you don’t measure. SOC leaders need visibility into:
Automation coverage (what % of workflows are automated)
False positive rates and escalation volumes
Modern security operations utilize unified dashboards to track these metrics and drive continuous improvement — and to show to the board and leadership how investments translate into reduced risk.
How Security Operations Automation Works
Torq acts as the orchestration layer that brings this modern vision to life. But how does SecOps automation actually function under the hood?
Connects to Your Full Stack
Automation starts with connectivity. Torq integrates with virtually everything in your ecosystem, including SIEMs, EDRs, ticketing systems (such as Jira and ServiceNow), identity providers (like Okta and Azure AD), cloud platforms (like AWS, Azure, and GCP), and communication tools (like Slack and Teams). This connectivity eliminates silos and allows data to flow freely between tools.
Ingests and Enriches Events
Instead of dumping raw logs onto an analyst, the Torq platform ingests alerts and immediately enriches them. It automatically queries threat intelligence feeds, checks user directories, and pulls asset information. By the time a human looks at the case, it is already populated with the who, what, where, and when.
Orchestrates Workflows from Alert to Remediation
This is the core of SOC automation. Using no-code visual workflows, Torq can:
Automate triage: Classify alerts, suppress known noise, group related events
Kick off root-cause and follow-up work: Create tickets for IT or DevOps, trigger patching or configuration changes
Complex, multi-step processes that previously took hours of manual coordination can execute in seconds.
Provides Full Auditability and Reporting
Every automated action is logged. The system tracks exactly what logic was applied, what actions were taken, and the outcome. This provides full auditability for compliance purposes and rich reporting data to measure automation ROI.
6 Benefits of Automating Security Operations
Why make the shift? The impact of automation on security operations is measurable and transformative.
10x faster incident response: By removing manual latency, automation allows you to respond to threats at machine speed. Containment actions that used to take 30 minutes can now happen in seconds.
Major reduction in false positives: Automated triage filters out the noise before it ever reaches the queue. Logic-based filtering ensures that known false positives are dismissed automatically, clearing the deck for real work.
Analysts focused on real threats: When you automate the repetitive busywork like password resets and IP lookups, you free up your most valuable resource: your people. Analysts can focus on threat hunting, strategic planning, and complex investigations.
Consistent playbook execution: Automation doesn’t get tired, and it doesn’t skip steps. It ensures that every incident is handled according to your defined security operations best practices, regardless of whether it happens at 2pm on a Tuesday or 3am on a Saturday.
Measurable improvement in MTTD/MTTR: These are the metrics that matter most to the board. Automation directly compresses both detection and response times, shrinking the window of exposure and reducing risk.
Seamless collaboration across IR, IT, and DevOps: Security doesn’t happen in a vacuum. Automation bridges the gap between teams, automatically routing tasks to IT for patching or Engineering for code fixes, fostering true collaboration without the friction of email chains.
How Torq Transforms Security Operations
Torq isn’t just another tool in the stack; it is the automation nerve center for the modern enterprise.
Visual workflow builder: Torq offers a powerful, no-code and AI-driven visual builder that makes automation accessible. Anyone on the team — from junior analysts to engineers — can build and maintain workflows without writing complex code.
300+ integrations: With hundreds of out-of-the-box integrations, Torq connects your SIEM, XDR, cloud, IAM, ticketing, and threat intel tools instantly.
Real-time execution: Torq enforces security policies and executes playbooks live, reacting to events as they happen, not after the fact.
Smart routing: The platform intelligently assigns incidents based on severity, time of day, or analyst skillset, ensuring the right eyes are always on the right problem.
Audit trails: Torq monitors all workflows, actions, and outcomes in real time with immutable logs that satisfy even the strictest compliance auditors.
Security Operations Don’t Have to Be Manual or Reactive
Security operations don’t have to be manual, slow, or reactive. The choice is no longer between secure and fast — you can have both. With automation and orchestration, security teams can do more with less — responding faster, reducing burnout, and operating with vastly higher confidence.
Reimagine your SOC. See how Torq modernizes security operations from the inside out.
Security operations (SecOps) encompass the processes, technology, and personnel responsible for continuously monitoring, detecting, investigating, and responding to cyber threats across an organization. It is the operational layer of enterprise security — combining threat intelligence, incident response, vulnerability management, and system monitoring into a coordinated defense function.
What happens in a SOC?
A Security Operations Center (SOC) is the command center for SecOps. Analysts triage alerts, investigate suspicious activity, hunt for threats that bypass detection tools, coordinate incident response, and ensure security controls are working as intended. Modern SOCs also manage cloud telemetry, identity signals, and automation workflows that drive containment and remediation across the environment.
Why is automation important in SecOps?
Automation eliminates the manual, repetitive tasks that slow down detection and response. It filters noise, enriches alerts, executes containment steps, and enforces security policies in real time, reducing MTTR, cutting false positives, and freeing analysts to focus on high-value investigation and threat hunting. In high-volume environments, automation is the only way to maintain 24/7 coverage without scaling headcount linearly.
What is the difference between SecOps and DevSecOps?
SecOps focuses on defending enterprise infrastructure — cloud, identity, endpoints, and networks — through continuous monitoring and response. DevSecOps embeds security into the software development lifecycle, ensuring that code, pipelines, and deployments are secure from build to production. SecOps protects operations; DevSecOps secures development. Both disciplines intersect in cloud-native, API-driven environments, but their missions and workflows differ.
How can I modernize my security operations center?
A modern SOC prioritizes automation, cloud-native telemetry, unified case management, and AI-assisted investigation. Start by consolidating tooling, eliminating manual triage, and automating routine containment steps. Introduce no-code or low-code workflows to standardize response. Deploy AI-driven enrichment and prioritization to reduce analyst load. Finally, build continuous detection and response capabilities that operate across identity, cloud, and endpoint, giving your team real-time visibility and control.
If you ask ten security architects to draw their incident response stack on a whiteboard, you will get ten different diagrams that all share one common feature: chaos.
The modern SOC is a museum of standalone best-of-breed tools. Endpoint tools excel at process behavior, SIEMs aggregate vast log volumes, cloud security platforms surface exposure and misconfigurations, and identity systems track user activity, each operating in its own domain and language. The challenge isn’t the tools themselves, but the operational sprawl that emerges when these systems run independently, forcing analysts to manually stitch together partial views of the same incident.
Effective incident response isn’t just about having the right tools; it’s about making them talk to each other. The traditional approach of buying more dashboards to solve the problem of too many dashboards is over.
This blog breaks down the essential incident response tools you actually need and, more importantly, how to use Torq to turn that disconnected jumble of software into a coordinated, autonomous defense system.
What Are Incident Response Tools?
Incident response tools are the specialized software and platforms security teams use to detect, investigate, contain, and recover from cyber incidents. They sit across the incident response lifecycle — supporting detection, analysis, containment, eradication, and recovery.
Detect when something is wrong (suspicious activity, malware, policy violations).
Investigate quickly (who, what, where, when, and how)
Respond and recover (contain the threat, remediate, and restore normal operations)
Without them, you’re flying blind. With them, you have visibility — but often so much data and so many consoles that you struggle to turn information into action.
Incident Response Lifecycle Placement
Different tools own different parts of the NIST or SANS frameworks. Typical incident response tools map to them like this:
Containment, eradication & recovery: Firewalls and gateways, IAM tools, EDR isolation, sandboxing, patch and configuration management, ticketing/ITSM systems
Post-incident activity: Case management, reporting and dashboards, evidence archiving, and analytics on incident response procedures (MTTR, first-pass resolution, automation coverage)
Gaps in Traditional Tooling
The industry secret: most incident response tools were designed to be operated manually, one at a time, by humans.
Manual handoffs: An alert in the EDR doesn’t automatically trigger a firewall block. A human has to read the alert, log into the firewall, and type the rule. This latency is where attackers live.
Alert overload: Tools are incentivized to be noisy. A SIEM that generates zero alerts looks broken, so it generates thousands. This creates alert fatigue, where analysts miss the signal because of the noise.
Siloed context: Your Identity provider knows who the user is. Your EDR knows what the process is. But neither tool talks to the other to ask, “Should this user be running that process?”
That’s why modern SOCs are moving beyond tools alone toward security Hyperautomation — using automation and orchestration to stitch all of this together.
5 Types of Incident Response Tools Used by Security Teams
To build a functional stack, you need coverage across four distinct categories. Here is the breakdown of the tools typically found in a mature SOC.
1. Detection and Alerting Tools
These platforms collect telemetry and generate alerts when something suspicious occurs.
SIEM (Security Information and Event Management): The central aggregation and correlation layer for logs and events.
Splunk, Microsoft Sentinel, Datadog
EDR (Endpoint Detection and Response): Agents on endpoints and workloads that monitor process execution, file changes, and behavioral indicators.
CrowdStrike Falcon, SentinelOne, Microsoft Defender for Endpoint
NDR (Network Detection and Response): Observes network traffic to detect anomalies and threats missed at the endpoint.
Corelight, Darktrace
Cloud Monitoring Platforms: Cloud security posture and runtime monitoring for public cloud environments.
Access Controls (IAM): Revoke sessions, enforce MFA, reset credentials, and adjust group memberships.
Okta, Azure AD (Entra ID), Duo
Endpoint Isolation: Network-isolate a compromised host, kill malicious processes, and remove persistence.
EDRs like Crowdstrike Falcon and Microsoft Defender
4. Communication and Reporting Tools
Incident response is a team sport. You need to talk to IT, Legal, and HR.
Collaboration Platforms: Real-time “war room” coordination across SecOps, IT, Legal, and leadership.
Slack, Microsoft Teams, Zoom
Dashboards: Visualization tools that show the CISO the current threat status.
Documentation: Store runbooks, incident response steps, and post-incident reports.
Wikis or knowledge bases like Confluence
5. Hyperautomation
These platforms orchestrate the entire incident response lifecycle end to end. Instead of analysts stitching tools together manually, Hyperautomation connects detection, enrichment, containment, and communication into one cohesive flow.
Hyperautomation Platforms: Automates triage, enrichment, and decision-making the moment an alert fires.
How Automation Transforms Incident Response Workflows
Traditional incident response is linear and human-dependent. An alert fires, a human looks at it, a human investigates, and a human remediates. This model fails at scale.
Security Hyperautomation transforms this process from a relay race into a unified, autonomous machine.
From Reactive to Autonomous
The shift is from static playbooks to dynamic, automated workflows.
Static: “If malware is detected, analyst logs into Okta and suspends user.”
Dynamic: “If malware is detected, Torq immediately suspends the user via API, creates a Jira ticket, messages the manager on Slack, and isolates the endpoint — all in less than a minute.”
Torq workflows can also adapt based on context. For example:
Check the user’s role (is this a privileged admin or an executive?)
Check asset criticality (is this a production database or a test VM?)
Adjust the incident response steps based on risk (e.g., require approval for high-impact actions)
Role of Security Hyperautomation
Hyperautomation is the concept of automating everything that can be automated. Torq’s platform serves as the connective tissue. It uses API-first integrations to ingest alerts from any detection tool and orchestrate actions in any response tool. It’s no-code, meaning security architects can build these complex flows visually without waiting for software engineering resources.
Key Benefits for Security Teams
Faster response times: We are talking about reducing MTTR from days or hours to seconds. Automation moves at machine speed.
Reduced manual work: By automating the Tier-1 triage and containment tasks (the boring stuff), you free up your analysts to do actual threat hunting and critical thinking.
Improved consistency and scalability: A workflow never gets tired, never forgets a step, and never calls in sick. Whether you have 10 alerts or 10,000, the process execution is identical.
Orchestrating Incident Response Tools with Torq: Real-World Use Cases
Let’s look at how this works in practice. Here are three common scenarios where Torq turns disconnected tools into a unified response capability.
Automated Phishing Response
Phishing is a high-volume, low-fidelity problem that drowns SOC teams.
With Torq:
User reports a suspicious email (via phishing button or ticket).
Torq ingests the event from email security or the mailbox.
Torq automatically:
Extracts URLs, attachments, and headers.
Checks them against Recorded Future, VirusTotal, and other threat intel tools.
If malicious, deletes messages across all affected inboxes (via M365 or Google Workspace API).
Triggers IAM actions like forcing a password reset or revoking sessions.
Posts a full summary and evidence to a dedicated Slack or Teams channel.
What used to take many minutes per email now completes in seconds, and analysts only step in for edge cases.
Coordinated Ransomware Containment
Ransomware moves laterally in minutes. Human response is too slow.
With Torq:
Torq receives the detection alert via webhook or SIEM. It Immediately:
Commands the EDR to isolate the host from the network.
Adds temporary firewall rules to block traffic from the affected IP or subnet.
Revokes the user’s active sessions via IAM.
Opens a high-severity incident in ServiceNow or Jira
Spins up a “war room” channel in Slack or Teams and notifies the on-call IR team.
By the time an analyst joins the call, initial containment is done and they can focus on deeper investigation and recovery instead of scrambling through manual steps.
Enrichment and Triage at Scale
Alert fatigue comes from a lack of context. SIEM alerts like impossible travel or suspicious login are common — but without context, they’re hard to triage.
With Torq:
Torq receives a “suspicious login” alert. It automatically:
Checks the user’s recent login history in the IdP.
Pulls device posture from EDR.
Looks up IP reputation in threat intelligence.
Optionally messages the user via Slack, Teams, or email: “Was this you?”
If the user confirms, Torq records the outcome and closes the case. If they deny or don’t respond, Torq escalates the incident, applies containment actions, and routes it to the right analyst with full context.
Choosing the Right Approach: Tools Alone Aren’t Enough
There’s a common trap in cybersecurity: assuming that buying one more “next-gen” tool will fix structural problems in incident response.
It won’t.
What to Look for in a Modern IR Ecosystem
When evaluating incident response tools and platforms, prioritize:
Open, well-documented APIs for ingesting alerts and triggering actions
Interoperability with your existing stack (SIEM, EDR, IAM, cloud, email security, ITSM)
Automation readiness, not just dashboards
Flexible deployment that works across hybrid and multi-cloud environments
Don’t Just Buy More Tools, Orchestrate Them
Instead of adding another dashboard to the pile, invest in the layer that sits above them. A Hyperautomation platform like Torq acts as a force multiplier for every other investment you have made. It makes your EDR faster. It makes your threat intel more actionable. It makes your analysts smarter.
Why Torq Is Built for Modern IR Challenges
Torq was built because legacy SOAR (Security Orchestration, Automation, and Response) tools failed. They were too complex, too rigid, and too hard to maintain. In comparison, Torq has:
Agentless automation: Deploy in minutes, not months.
AI workflows: Use Socrates, Torq’s AI SOC Analyst, to reason through alerts and make decisions, not just follow scripts.
No-code customization: Drag-and-drop workflow building that allows you to adapt to new threats instantly.
Enterprise scale: Built to handle the millions of events that modern cloud environments generate.
Plug-and-Play with Any IR Stack
Torq is agentless and tool-agnostic:
It connects via APIs to your existing incident response tools, including SIEM, EDR/XDR, IAM, firewalls, cloud platforms, ticketing systems, and threat intelligence.
It doesn’t require agents on endpoints or rip-and-replace projects.
If you swap tools (e.g., move from Splunk to Sentinel), you update integrations in Torq and keep your incident response workflows intact.
That makes your incident response architecture future-proof: your automation logic lives above any single vendor.
Turn Your Incident Response Tools into an Autonomous Defense System
The bad guys are using automation. They are using scripts to scan your network, AI to write phishing emails, and bots to brute-force your accounts. You cannot fight them with manual processes and spreadsheets.
Incident response is no longer about who has the best tools; it’s about who has the fastest, most integrated workflows. Empower your security team by orchestrating your stack with Torq.
Transform your incident response tools from a collection of noisy, disconnected boxes into a fast, intelligent, and autonomous defense system with Torq. Get the Don’t Die, Get Torq manifesto to learn more.
What are the essential incident response tools for a modern SOC?
The essential incident response tools for a modern SOC include Detection tools (SIEM, EDR/XDR, NDR), Investigation tools (Threat Intelligence, Log Analysis), Containment tools (Firewalls, IAM, Endpoint Isolation), and Communication tools (Slack/Teams, Ticketing Systems). Leading the stack is a Hyperautomation platform like Torq, which connects these disjointed tools into a unified, autonomous workflow.
How can I automate incident response workflows effectively?
To automate incident response workflows effectively, implement a Hyperautomation platform that orchestrates actions across your security stack via APIs. Start by automating high-volume, repetitive tasks like phishing triage, user verification, and IOC enrichment. This allows your tools to autonomously detect threats, enrich alerts with context, and execute containment actions (like blocking IPs or suspending users) without manual intervention.
Why do legacy SOAR tools fail at incident response?
Legacy SOAR tools fail because they are often rigid, complex, and reliant on static playbooks that cannot adapt to dynamic threats. They struggle with high alert volumes, lack intuitive integration capabilities, and require significant maintenance overhead. Modern Hyperautomation platforms replace legacy SOAR by offering flexible, AI-driven workflows that scale effortlessly and empower analysts with no-code/low-code building.
What is the difference between automated and manual incident response?
Manual incident response relies on human analysts to detect alerts, switch between multiple dashboards for investigation, and manually execute remediation steps, which is slow and prone to error. Automated incident response uses software to instantly detect anomalies, enrich data, and execute pre-defined containment actions at machine speed, significantly reducing Mean Time to Respond (MTTR) and analyst burnout.
How does Torq integrate with existing incident response tools?
Torq integrates with existing incident response tools through an agentless, API-first architecture. It connects seamlessly with SIEMs (like Splunk), EDRs (like CrowdStrike), Identity providers (like Okta), and communication platforms (like Slack) without requiring custom code. This allows security teams to orchestrate complex workflows across their entire stack and swap tools easily without breaking their automation logic.
The modern enterprise is built on a foundation of trust. You trust your cloud provider to secure the hypervisor. You trust your software vendors to secure their build pipelines. You trust your open-source libraries to be free of backdoors. But in the current threat landscape, trust is your biggest vulnerability.
Supply chain attacks have evolved from niche, nation-state anomalies into a commoditised attack vector used by ransomware gangs and opportunists alike. They bypass your perimeter, your firewall, and your endpoint protection because they ride in on the trusted highways you built for business efficiency.
For the strategic CISO, supply chain attack prevention is no longer just about third-party risk management questionnaires or annual audits. It is an operational challenge that demands real-time visibility, automated governance, and the ability to sever connections with compromised vendors at machine speed.
This guide explores the realities of supply chain risks, the necessity of security automation, and how Torq enables enterprises to defend their ecosystem without slowing down innovation.
What Is A Supply Chain Attack?
A supply chain attack occurs when an adversary infiltrates your system through an outside partner or provider with access to your systems and data. This dramatically changes the attack surface. Instead of attacking you directly, the adversary compromises:
A build system
An upstream open-source dependency
Firmware on a critical device
A vendor or MSP with network or identity access
From there, they can move laterally into downstream customer environments. These attacks are particularly dangerous because they exploit trust:
Signed binaries from known vendors may be whitelisted
Updates are assumed to be safe
Vendor access paths are often less tightly monitored than internal accounts
A single malicious update or compromised vendor account can deploy malware deep inside an environment before traditional detection fires, if it fires at all.
The 3 Primary Vectors of Supply Chain Compromise
To understand the scope of supply chain compromise, we must look beyond just software.
1. Software Supply Chain Attacks
This is the most visible and well-publicized vector. Attackers:
Inject malicious code into an upstream application or dependency
Compromise build systems or CI/CD pipelines
Exploit widely used open-source components
When targets consume the compromised artifact (via update, container image, dependency, etc.), they unwittingly deploy attacker-controlled code.
Examples:
SolarWinds Orion:Attackers compromised SolarWinds’ build environment and injected a backdoor into legitimate, digitally signed Orion updates. Once customers installed them, the malware gained privileged access inside federal agencies, enterprises, and critical infrastructure.
Log4j (Log4Shell):Not a malicious backdoor, but a critical vulnerability in a ubiquitous Java logging library, embedded into thousands of products. It showed how a flaw in a single upstream dependency can trigger an internet-wide scramble to identify and patch exposure.
XZ Utils:A near-miss in 2024 where a long-term effort to compromise a critical compression library’s maintainer led to a backdoored version of xz/liblzma. Several major Linux distributions were close to shipping it before the issue was discovered — highlighting how attacker focus is shifting toward open-source maintainers and infrastructure.
2. Hardware and Firmware Attacks
Hardware and firmware compromise is less common but extremely high impact. Attacks can involve:
Tampering with components during manufacturing or distribution
Modifying firmware on devices such as network gear, baseboard controllers, or storage devices
Because these operate below the OS, traditional endpoint and application security tools often can’t see them. Successful firmware or hardware compromise can provide long-term, stealthy access.
3. Vendor and Service Provider Compromise
This is often called island hopping. Attackers compromise a Managed Service Provider (MSP) or a smaller vendor with access to your network and use their credentials to pivot into your environment.
Examples:
Kaseya VSA: Attackers exploited vulnerabilities in Kaseya’s remote monitoring and management platform, using its privileged channel to deploy ransomware through MSPs to hundreds of downstream organizations.
Target HVAC Vendor Breach: An attacker compromised credentials from a third-party HVAC vendor with network access into Target’s environment. That foothold was used to pivot into payment systems and exfiltrate tens of millions of card numbers.
5 Supply Chain Security Best Practices (Where Automation Becomes Essential)
Effective prevention requires a layered defense that spans the software development lifecycle (SDLC), hardware procurement, and organizational governance. Automation is the only way to apply these controls at the scale of a modern enterprise.
1. Software and Open-Source Controls
Securing the software supply chain requires a shift left — integrating security into the development process rather than applying it as an afterthought.
Harden the CI/CD pipeline: Your build server is a prime target. Ensure that access to build tools is strictly controlled and monitored. Use ephemeral build environments that are spun up for a job and destroyed immediately after, preventing persistence.
Enforce provenance: Implement standards such as SLSA (Supply Chain Levels for Software Artifacts). You must verify that the code running in production is the exact same code that was committed to the repository and built by the trusted pipeline. Code signing is non-negotiable.
Curate dependencies: Developers should not pull libraries directly from the public internet. Use an internal artifact repository that acts as a proxy. Scan every package for known vulnerabilities and malware before it is added to the internal repository.
2. Hardware and Firmware Security
Hardware risks are challenging to detect but crucial to mitigate, particularly in critical infrastructure and high-security environments.
Verify root of trust: Utilize Trusted Platform Modules (TPM) and hardware roots of trust to ensure that the system has not been tampered with before the OS even boots.
Secure firmware updates: Firmware updates should be digitally signed by the vendor and verified by the hardware before installation. Disable the ability to downgrade firmware to prevent attackers from rolling back to vulnerable versions.
Physical tamper evidence: For critical hardware shipments, use tamper-evident packaging and separate shipping channels for the hardware and the authentication keys required to activate it.
3. Governance and Vendor Management
Governance must evolve from a static contract to a continuous operational state.
Contractual security SLAs: Contracts must mandate notification timelines for breaches. If a vendor is breached, you need to know within hours, not days.
Right to audit: Include clauses that allow you to review the vendor’s security posture or receive independent audit reports (SOC 2 Type II) regularly.
Continuous monitoring: Use third-party risk management platforms to monitor the external security posture of your vendors.
4. Zero Trust Network Access (ZTNA)
The days of the trusted site-to-site VPN for vendors are over. A vendor should never have broad network access.
Least privilege access: Vendors should only access the specific applications they need to service.
Identity verification: Enforce strict Multi-Factor Authentication (MFA) for all external access.
Session recording: For high-risk access, record the session. If a vendor creates a backdoor, you need the forensic tape.
5. Automated Asset Discovery
You cannot patch what you do not know you have. Shadow IT and forgotten assets are fertile ground for supply chain attackers. Automated asset discovery tools must run continuously to identify unknown software and hardware on the network, reconciling them against the authorized inventory.
Detection, Response, and Resilience Beyond Prevention
Prevention is the goal, but resilience is the requirement. A determined nation-state actor may eventually find a way into your supply chain. Therefore, your strategy must include capabilities to detect the compromise and minimize the damage.
Anomaly Detection
When prevention fails, behavior is the only tell. If a trusted software update process suddenly starts beaconing to an unknown IP address in a hostile nation, that is a supply chain attack in progress.
Enterprises need runtime security that monitors the behavior of applications and vendor accounts. Establish a baseline of normal activity. Any deviation — such as a printer trying to access a domain controller or a payroll software spawning a command shell — should trigger an immediate, high-severity alert.
Forensic Readiness
In the event of a suspected supply chain breach, time is critical. Incident response teams need immediate access to logs, artifacts, and memory dumps. Forensic readiness means having the telemetry enabled and the retention policies set before the incident occurs.
Kill Switches
You need the ability to sever the connection to a compromised vendor instantly. This isn’t about sending an email to the firewall team. It means having an automated playbook that can block a vendor’s IP range, revoke their certificates, and disable their accounts across the entire enterprise with a single authorization.
How to Detect Supply Chain Attacks with Torq
Traditional SOAR platforms and generic risk management tools struggle with supply chain attacks because they are siloed. They see the alert, but they cannot see the context, and they certainly cannot touch the infrastructure to fix it.
Torq HyperSOC serves as the connective tissue between your governance, development, and security operations.
Automating Intake and Triage for New Supply Chain Risks
When a new zero-day vulnerability in a common library (like Log4j) is announced, the first question every CISO asks is: Where are we vulnerable?
Manual discovery takes weeks. Responding to an incident with Hyperautomation is faster.
Torq automates this in minutes:
Ingestion: Torq ingests vulnerability data from threat intel feeds.
Correlation: It automatically queries your CMDB, cloud security posture management (CSPM) tools, and code repositories to identify every asset running the vulnerable version.
Context: It enriches this data with business context. A vulnerable server exposed to the internet is prioritized over a vulnerable air-gapped test machine.
Orchestrating Response Across the Stack
Torq integrates with over 300 enterprise tools, allowing it to take action across the entire stack.
Vendor isolation: If a vendor is compromised, Torq can trigger workflows to revoke their IAM access, block their IPs at the firewall, and suspend their VPN sessions instantly.
Automated patching: For software vulnerabilities, Torq can trigger patching workflows via your endpoint management systems or open tickets in Jira for developers with the specific upgrade instructions attached.
Communication: Torq creates a dedicated war room channel in Slack or Teams, inviting the relevant stakeholders and posting real-time updates from the investigation.
Applying Agentic AI for Vendor Risk
Torq Socrates — the AI SOC Analyst — takes vendor management to the next level. It can parse incoming vendor security emails, identifying notifications of breaches or updates. It can autonomously reach out to vendors to request updated compliance documents or status on vulnerability remediation, parsing their responses and updating the risk register without human intervention.
By automating the tedious work of verification and the critical work of isolation, Torq allows security teams to move faster than the supply chain contagion.
From Blind Trust to Automated Verification
The era of trusting the ecosystem is over. Verification is the new standard. Supply chain attack prevention is not a box to check; it is a continuous operational discipline that requires deep visibility, rigorous governance, and the ability to act instantly.
Checklists and questionnaires are artifacts of the past. The future of supply chain security belongs to SOC automation. You need a platform that can map your risks, monitor your vendors, and enforce your controls at the speed of code.
Stop relying on trust. Start relying on verification and automation.
Reimagine your defenses. Explore Torq for SOC resilience in our Don’t Die, Get Torq manifesto.
What is a supply chain attack, and why are enterprises so vulnerable to them?
A supply chain attack occurs when an adversary compromises a trusted vendor, service provider, or upstream software component to infiltrate downstream environments. Because these pathways rely on trust, they bypass traditional controls — making supply chain attack prevention a core requirement for modern enterprises.
What are the main types of supply chain attacks organizations should be prepared for?
The most common types of supply chain attacks include software supply chain compromise, hardware or firmware tampering, and vendor access breaches. Each requires different controls, from provenance enforcement to continuous vendor monitoring.
What are the best supply chain security best practices for enterprises in 2026?
Effective supply chain security best practices include hardening CI/CD pipelines, enforcing code provenance, verifying hardware integrity, continuously monitoring vendor risk, enforcing least privilege access, and automating asset discovery. Automation ensures these controls operate at scale.
How do you mitigate risk in the supply chain when attackers target upstream software and vendors?
Enterprises can mitigate risk in the supply chain by combining automated vulnerability correlation, real-time vendor access governance, anomaly detection, and rapid isolation playbooks. Platforms like Torq automate discovery, prioritization, and containment across the entire stack.
What are some real-world software supply chain attack examples, and what can we learn from them?
High-impact software supply chain attacks — such as SolarWinds, Log4j, and the XZ Utils backdoor — show how a compromise in a single upstream dependency can cascade across thousands of organizations. These supply chain attack examples underscore the need for automated detection, provenance validation, and fast response mechanisms.
Are there any industry standards for supply chain attack prevention?
Yes, several frameworks provide industry standards for supply chain attack prevention. Key standards include NIST SP 800-161 (Cybersecurity Supply Chain Risk Management), ISO/IEC 27036 (Information Security for Supplier Relationships), and SLSA (Supply-chain Levels for Software Artifacts), which focuses specifically on securing software build pipelines. Adopting these standards helps organizations establish a baseline for vendor governance and software integrity.
Can you explain the main warning signs of a possible supply chain attack?
The main warning signs of a possible supply chain attack often appear as anomalies in trusted channels. Indicators include unauthorized configuration changes by service accounts, unexpected outbound traffic from updated software to unknown IP addresses, sudden spikes in resource usage after a vendor patch, or login attempts from vendor accounts at unusual times. Detecting these signs requires continuous behavioral monitoring and automated anomaly detection tools.
Alert volumes are climbing, tool sprawl is paralyzing investigations, and the attack surface — spanning identity, SaaS, and cloud — expands daily. 47% of SOCs face alerting issues, and a majority of SOCs spend more time maintaining tools than defending threats, according to a recent Splunk study. Security teams aren’t just overwhelmed; they’re outmatched by scale.
AI has arrived as the promised solution, supporting almost every phase of detection and response. But the real question facing CISOs and SOC leaders is this: How do you adopt AI in a way that is fast, safe, transparent, and trusted?
The answer isn’t humans alone, and it certainly isn’t AI alone. The future of the SOC lies in human-AI collaboration — a coordinated model where agentic AI executes high-volume, repetitive reasoning tasks, and humans apply judgment where it matters most.
This guide outlines a practical framework for building collaboration within modern SOCs, ensuring you achieve machine speed without sacrificing human control.
What Agentic AI Means in Cybersecurity (and Why It Matters)
To understand how humans and AI collaborate, we must first distinguish agentic AI from the chatbots and scripts of the past (Generative AI). Traditional automation follows a rigid track: If X happens, do Y. If the data format changes or the API hangs, the script fails.
Agentic AI is different. It has agency. Agentic AI describes autonomous systems that possess a cognitive architecture capable of “thinking” through a workflow. Instead of just following a script, an agentic system:
Perceives: It ingests raw telemetry and recognizes anomalies (“This user behavior deviates from the baseline”).
Plans: It breaks a high-level goal (“Investigate phishing”) into a sequence of logical steps.
Reasons: It makes decisions based on context. If a tool fails, it doesn’t crash; it attempts an alternative route or query.
Acts: It uses “hands”— integrations and APIs — to execute changes in the environment, such as blocking an IP or isolating a host.
Reflects: It evaluates the output of its actions to ensure the goal was met.
This shifts the way a SOC works. AI is no longer just a tool you click; it is a digital teammate that handles mechanical work — enrichment, correlation, evidence gathering, and repetitive decision-making — so humans can focus on oversight, interpretation, and policy refinement.
Understanding Human-AI Collaboration in the SOC
A functional human-AI collaborative model depends on a clear division of labor.
Where AI Leads:
Alert triage: Eliminating noise, enriching identity context, and grouping related alerts into coherent cases.
Deep investigation: Retrieving user login history, mapping device posture, and correlating signals across the stack (SIEM, EDR, IAM).
SaaS governance: Discovering shadow AI tools and validating risky OAuth scopes instantly.
Cloud assessment: Checking severity, exposure, and potential blast radius across AWS, Azure, and GCP in near real time.
Where Humans Lead:
Risk interpretation: Making calls when business impact is ambiguous or context is offline.
Exception handling: Approving high-risk access requests or sensitive identity changes.
Strategic decisions: Refining detection logic, setting policy guardrails, and managing data privacy.
This division only works when humans trust the AI system’s reasoning. That trust has to be earned.
A Framework for Trust Calibration in AI-Driven SOCs
The biggest barrier to AI adoption isn’t capability; it’s confidence. Trust is earned when AI behaves predictably and transparently. This Trust Calibration Framework can help organizations evaluate and strengthen this relationship.
1. Transparency
An AI Agent must show its work. It is not enough to present a verdict; the agent must display the chain of thought.
In practice, Torq Socrates includes step-by-step rationale, evidence, and source logs in every case summary. Analysts don’t just see “Blocked IP” — they see the specific threat intel matches and user behavior anomalies that led to that decision.
2. Consistency
AI should act predictably across environments, identities, and tenants.
This requires agentic AI systems that can reason through adaptive tasks while strictly adhering to defined rules and logic flows.
3. Guardrails
Humans define the boundaries; AI operates within them. Examples include identity policy limits, restricted actions for sensitive roles (like the C-Suite), and mandatory approval flows for high-risk changes.
Torq builds these guardrails into the core of HyperSOC™, ensuring that speed never comes at the expense of governance.
4. Escalation
An intelligent agent knows what it doesn’t know. It must be programmed to recognize ambiguity and hand the case to a human.
Typical triggers include legal/regulatory implications, conflicting signals across tools, or access attempts involving sensitive data. This keeps automation aligned with business context.
5. Measurement
Trust grows through data, not intuition.
Key metrics include: false positive reduction, percentage of autonomously resolved cases, and importantly, the rate of human overrides. If humans are constantly reversing AI decisions, calibration is off.
AI Trust Calibration Framework
Pillar
Goal
How Torq Delivers This
Key Metrics
Transparency
Actions must be visible and auditable
Torq provides workflow execution logs and case updates showing each step taken and all data passed between systems.
Ability to trace every workflow action in logs
Consistency
Workflows should run the same way every time
Torq workflows execute deterministically based on triggers, steps, and conditions defined by the user.
Workflow execution success/failure rate
Guardrails
Sensitive actions require controls
Torq supports RBAC and workflow approval steps to restrict changes and require human sign-off.
Number of workflows requiring approval; compliance with approval paths
Escalation
Complex or sensitive events route to humans
Conditional logic determines when to assign or escalate a case to an analyst.
Percentage of cases escalated by workflow conditions
Measurement
Performance and outcomes must be trackable
Torq Reporting dashboards show workflow metrics, case metrics, and execution history.
Borrowing from academic research, AI in the SOC should operate on a tiered autonomy scale.
Level 1: AI Assists
AI recommends. Humans decide.
Example: AI enriches an Okta impossible-travel alert with geo-velocity data, past login history, device posture, and recent MFA failures. It suggests: High-risk login. Recommend MFA reset. The analyst reviews the evidence and performs the action manually.
Level 2: AI Acts With Approval (Human-in-the-Loop)
AI can take action, but only after a human signs off.
Example: A phishing alert enters the SOC. AI pulls message headers, checks the attachment and URL reputation, and proposes: Remove this email from all inboxes and block the sender. The analyst clicks “Approve,” and the automation executes the full remediation workflow.
Level 3: AI Acts With Supervision (Human-on-the-Loop)
AI handles the task end-to-end but alerts a human if something looks unusual.
Example: A cloud alert reports a public S3 bucket containing sensitive files. AI validates exposure, removes the public ACL, notifies the bucket owner, and updates the case. If conflicting metadata appears (e.g., bucket belongs to a high-risk business unit), it escalates to an analyst for review.
Level 4: AI Acts Autonomously in Routine Scenarios
AI handles predictable, low-risk tasks with no human touch unless something breaks.
Example: AI detects a known malicious IP scanning the perimeter across multiple tenants. It automatically blocks the IP across firewalls, updates indicators in the SIEM, logs the action with evidence, and closes the case. No analyst is involved unless the block fails or impacts a critical system.
High-risk tasks stay at lower autonomy. Routine tasks move up the scale. This adaptive model ensures the right balance between speed and oversight.
How to Build This Model With Torq Today
You don’t need to rip and replace your stack to move toward an agentic AI security model. With Torq HyperSOC™, you can layer AI and automation on top of what you already have — starting small, proving value fast, and expanding from there.
1. Start With Tier-1 Autonomy
Begin where the pain is highest: Tier-1 triage. Use Torq workflows to automate the grunt work like enrichment, correlation, and initial routing. In practice, that means:
Triggering workflows from SIEM, EDR, email security, or webhook alerts
Enriching observables automatically (IPs, URLs, hashes, users) across your tools
Creating and updating Torq cases as part of the workflow, instead of forcing analysts to swivel between consoles
You can even use Torq’s AI-powered features to generate the first version of these workflows from a plain-language description, then refine them with your own logic. Once Tier-1 noise is under control, analysts immediately feel the difference: fewer repetitive clicks, more time for real investigations.
2. Use AI Inside Workflows for Decisions
Next, infuse intelligence into those workflows. Torq’s AI Task operator lets you call large language models directly from any stage of a workflow to summarize evidence, extract observables, or propose next steps — without leaving the automation.
Instead of a chatbot on the side, AI becomes part of the decision path to:
Summarize multi-tool telemetry into a readable case note
Draft Slack or email messages to users for verification
Propose a severity level or recommended action based on the collected context
Humans still own the final call, but AI does the heavy lifting — exactly what Human–AI collaboration should look like in an AI SOC.
3. Build Human-in-the-Loop Guardrails Where Needed
Not every action should be fully autonomous, and Torq’s AI governance features reflect that. Use workflow approval patterns and access-control templates to hard-code where humans must step in:
Add explicit approval steps before sensitive actions like account lockouts, high-risk group changes, or production firewall changes
Use Slack or Teams approval flows for identity and access workflows (for example, just-in-time access or group membership changes)
Leverage Torq roles so only specific users can publish or modify high-impact workflows
This lets you keep routine automation fast while enforcing strong human guardrails around identity, data movement, and privileged operations.
4. Unify Case Management and Measurement
Finally, stop scattering decisions across five tools. Use case management as the single place where alerts, context, AI outputs, and actions come together. Workflows can automatically:
Create cases when certain alerts arrive
Attach enrichment results and AI-generated summaries
Update status, severity, and assignees as the investigation progresses
From there, Torq Reporting gives you the dashboards to measure what actually changed: how many cases are auto-resolved, how MTTR is trending, and where humans are still overriding automation. Those metrics are your calibration loop; the data that tells you when to increase, decrease, or reshape autonomy across your security operations workflows.
Why This Approach Works
What you get with Torq is:
Reliability: Automation always operates in the same manner
Transparency: Every decision is logged and visible
Scalability: Workflows can automate thousands of alerts or remediation tasks
Flexibility: Easy to edit, iterate, and improve workflows without code
Control and governance: RBAC, approvals, and auditability keep humans in charge where it matters
Over time, this human-AI collaboration model delivers significant SOC uplift — fewer alerts, faster response, less toil, more focus on true threats.
The Future of the SOC is Human-AI Collaboration
Human-AI collaboration is transforming SOCs across industries. Leading organizations like Carvana and Valvoline are already proving this autonomous SOC model works, using Torq to pair agentic AI with human expertise to drive faster, safer outcomes.
Torq HyperSOC™ is built on this philosophy. We combine the speed of agentic AI with the transparency, guardrails, and governance required for enterprise security. And you don’t need to replace your stack or commit to “full autonomy.” You can start small — automate Tier-1 triage, add AI decisions inside workflows, and scale gradually using the Trust Calibration Framework.
This is how you reduce MTTR, increase resilience, and eliminate the operational drag that cripples most SOCs. And this is how you turn AI from a black box into a trusted teammate.
The future of the SOC is Torq. See how Torq’s Human-AI collaboration model eliminates Tier-1 overload, restores analyst bandwidth, and delivers resilience. Get the Don’t Die, Get Torq manifesto.
What is human-AI collaboration in security operations?
Human-AI collaboration is a security operating model where AI Agents handle high-volume, repetitive tasks — such as alert triage, data enrichment, and initial correlation — while human analysts focus on high-value tasks requiring strategic judgment, risk interpretation, and policy refinement.
How do you build trust in AI for the SOC?
Building trust requires a Trust Calibration Framework focused on transparency and consistency. AI Agents must display their “chain of thought” (rationale and evidence) for every decision. Additionally, organizations should implement strict guardrails, such as mandatory human approvals for high-risk actions, and predefined escalation paths when the AI encounters ambiguity or sensitive contexts.
What is the difference between AI assistance and agentic AI?
AI assistance (like a standard chatbot) is passive; it waits for a human prompt to summarize data or write code. Agentic AI is active and goal-oriented. It can autonomously reason through a workflow, retrieve context, decide on next steps, and execute remediation actions within defined guardrails, functioning more like a digital teammate than a simple tool.
What are the levels of autonomy in an AI-driven SOC?
Academic research defines four key levels of autonomy for the SOC:
Level 1 (Assist): AI recommends actions; humans decide.
Level 2 (Approval): AI prepares the action; humans must approve execution (human-in-the-loop).
Level 3 (Supervision): AI acts end-to-end but alerts humans for unusual outliers (human-on-the-loop).
Level 4 (Autonomous): AI handles routine, predictable tasks entirely without human intervention.
How can legacy SOCs implement human-AI collaboration?
You do not need to replace your entire security stack. Platforms like Torq HyperSOC™ layer over existing tools (SIEM, EDR, IAM) to introduce autonomous capabilities. SOCs can start by automating Tier-1 triage to clear noise, then gradually introduce human-in-the-loop checkpoints for remediation, allowing the organization to scale autonomy as trust in the system grows.
2025 marked a significant shift in the security operations landscape. The industry focus moved beyond simple task automation to full-scale autonomy, driven by the adoption of agentic AI and Hyperautomation.
Throughout the year, we documented this transition through technical research, strategic frameworks, and real-world implementation stories. We have compiled our most impactful resources into this single library to help security leaders and practitioners benchmark their progress and plan for the year ahead.
Here is a comprehensive roundup of the case studies, strategic guides, and technical sessions that defined 2025.
Top Blogs of 2025: The Concepts Fueling the Next-Gen SOC
From defining new tech categories to debunking legacy metrics, these were Torq’s hottest reads of the year.
The Year of Agentic AI and The AI SOC
Agentic AI in the SOC:Everyone talked about AI in SecOps in 2025, but we defined it. This post cuts through the chatbot hype to explain what agentic AI actually is: autonomous, goal-oriented, and capable of reasoning through threats without a human babysitter.
The AI SOC:The legacy SOC model is broken. This foundational piece lays out the architecture of the future, where data ingestion, analysis, and response happen at machine speed, and humans stop acting like glue for broken tools.
The Multi-Agent System: Why hire one AI Agent when you can have a coordinated team? We break down why a multi-agent system (MAS) is the only architecture robust enough to handle the complexity of the modern enterprise.
Meet Socrates, the AI SOC Analyst: Tired of Tier-1 burnout? This blog introduces Socrates, Torq’s AI SOC Analyst — a digital teammate that investigates, documents, and remediates alerts 24/7/365.
Torq HyperAgents: HyperAgents were a breakout highlight in 2025 — fully goal-driven AI operators that plan, reason, and execute end-to-end security workflows. This post breaks down how they outperform playbooks and why early adopters use them to wipe out Tier-1 workload.
Product and Innovation: What Torq Shipped in 2025
HyperSOC 2.0: We took HyperSOC and made it faster, smarter, and more intuitive. If you missed the launch details of HyperSOC-2o, catch up on the specs that are redefining speed.
gRPC-Web in Front-End Applications: For the builders and the engineers, this blog draws back the curtain on the tech stack that powers Torq’s blazing-fast interface. A must-read for anyone who loves engineering excellence.
The Model Context Protocol (MCP): Connectivity is everything. We explore the Model Context Protocol and how standardizing AI context exchanges is the key to unlocking truly interoperable security tools.
AI Security Operations Workflows:Static playbooks are dead. This post dives into how dynamic, AI-driven workflows adapt in real-time to the threat context, ensuring you’re never stuck following a rigid script when the situation changes.
Torq Case Management: Unlike ticket-based systems retrofitted with automation, Torq Case Management is AI-native from the ground up — built to ingest millions of events, correlate signals across your entire stack, and drive end-to-end investigation and response without human busywork. This is the future of case management for autonomous SOCs.
Strategy & Best Practices: Modern Frameworks for Modern SOCs
The Pyramid of Pain: We explain how Hyperautomation allows you to automate the top of the pyramid, making life miserable for attackers and easier for your team.
MTTD vs. MTTR:Are you measuring activity or impact? Let’s settle the debate on detection vs. response metrics and show you which numbers actually prove ROI to the board.
10 Best SOC Tools: Your stack is probably bloated. This blog breaks down the essential tools for a modern defense and helps you identify which legacy anchors might be dragging you down.
2025 Cybersecurity Best Practices:The fundamentals, modernized. From Zero Trust to automated governance, this is the checklist for staying resilient in a threat landscape that never sleeps.
Executive Playbooks: Strategic Guides for CISOs in 2025
This year, we released four major resources designed to give you the blueprint for the Autonomous SOC.
Don’t Die. Get Torq. A blunt, data-backed manifesto showing why the legacy SOC model is collapsing and how Agentic AI + Hyperautomation give teams the only viable path to survive rising alert volume, burnout, and budget pressure.
The Tomorrow SOC:You can’t fight tomorrow’s threats with yesterday’s architecture. In partnership with Google Cloud, this guide maps out the infrastructure of the future-proof SOC, focusing on resilience, cloud-native scale, and data unity.
Build the Autonomous SOC in 90 Days:Autonomy isn’t a five-year plan. It’s a quarterly objective. We laid out a concrete, week-by-week roadmap to transition your team from reactive ticket-taking to proactive, autonomous defense in just three months.
The Threat Escalation Matrix: Triage is an art, but it should be a science. This resource provides a practical framework for defining exactly when, how, and why an automated alert should escalate to a human, helping you dial in your signal-to-noise ratio.
Customer Case Studies: Real-World Autonomy at Global Scale
See how global organizations applied Torq Hyperautomation™ to solve specific operational challenges.
Kenvue: When the world’s largest pure-play consumer health company (the home of Tylenol and Listerine) spun off, they needed a cloud-native security architecture from Day 1. See how they achieved rapid time-to-value and massive scale.
Valvoline: Retail environments are notoriously difficult to secure. Valvoline used Torq to unify a distributed environment, automating the triage that used to bury their analysts and turning their SOC into a business enabler.
Agoda: In the high-velocity world of travel tech, downtime is revenue lost. Agoda leveraged Torq to bring machine-speed response to their SOC, ensuring that security keeps pace with their massive transaction volumes.
Bloomreach: Growth demands scalability. Bloomreach implemented Torq Hyperautomation to eliminate manual bottlenecks, enabling their security team to support rapid business expansion without simply adding more humans to the problem.
AMP’d Sessions: The Integrations That Made the Autonomous SOC Real
Security is a team sport. Our AMP’d Sessions (Alliance & Momentum Partners) brought together the brightest minds and best tech in the industry to show what happens when best-of-breed tools actually talk to each other.
Wiz: Torq turns Wiz’s deep cloud visibility into instant remediation by automatically syncing DevSecOps contexts and closing the loop on critical risks before they become breaches
Panther: This partnership enables a seamless AI-to-AI handoff where Torq ingests Panther’s high-fidelity detections and immediately executes complex identity and network remediation at machine speed.
Cyera: Cyera’s data insights turn into immediate protection by autonomously revoking public access to sensitive files and verifying user intent in minutes.
Reco: Torq operationalizes Reco’s SaaS identity insights by autonomously revoking risky access and enforcing policy across the chaotic sprawl of apps and shadow AI tools.
Intezer: By handing Intezer’s verified forensic evidence directly to Torq’s AI SOC Analyst, we unlock true agent-to-agent collaboration that autonomously resolves 95% of Tier-1 threats without a single ticket.
Zscaler: When Zscaler Deception lures an attacker, Torq instantly correlates the high-fidelity alert and executes an agentic runbook to verify, isolate, and block the threat in under sixty seconds.
For MSSPs and MDRs: The New Playbook for High-Margin, Automation-First Services
2025 was the year MSSPs stopped treating automation as an add-on and started using it to redesign their entire delivery model.
Don’t Die: Managed Services Edition:This manifesto reframes the MSSP challenge. Margins aren’t dying because of attackers — they’re dying from manual work, tool sprawl, and SLAs that no human-only team can sustain.
HWG Sababa Case Study:MSSP HWG Sababa used Torq to increase throughput, shrink response times, and expand customer coverage without expanding headcount.
SOAR is Dead Managed Services Manifesto:A strategic guide for MSSPs shifting from “we’ll triage your alerts” to “we’ll deliver outcomes.” It outlines how automation, standardization, and AI-driven service tiers unlock better margins and foster stickier customer relationships.
Security MDR Deep Dive: This blog breaks down why MDR is converging with autonomous SOC operations — and why agentic AI will power the next generation of MDR offerings. The message was clear: the future of managed detection and response is automation-led, not analyst-led.
2026 MSSP Trends:The biggest MSSP cybersecurity trends for 2026 — and how Hyperautomation is the only scalable path for managed security providers to meet rising customer expectations, close talent gaps, and deliver true autonomous outcomes across every environment.
Looking Ahead to 2026: The Year Autonomy Goes Mainstream
If 2025 was the year security teams proved that agentic AI and Hyperautomation work at enterprise scale, 2026 will be the year these capabilities become standard. The pressure on SOCs isn’t slowing — alert volume, cloud complexity, and identity-driven threats are all accelerating — and the gap between teams that automate and teams that don’t is widening fast.
The organizations leading this shift aren’t the ones hiring faster. They’re the ones designing for autonomy, unifying their data, and letting AI shoulder the work humans were never meant to do at volume. Torq will continue to invest heavily in multi-agent orchestration, AI-governed case management, and deeper ecosystem integrations so security teams can operate with more speed, clarity, and control.
If your goal in 2026 is to reduce MTTR, eliminate operational drag, and build a SOC that scales without expanding headcount, this library gives you the blueprint. And the next wave of innovation is already in motion.
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.
Last month, I watched two of our senior security researchers, with a combined 12+ years of experience, lose a staring contest to Claude.
We fed the model a Sysmon dataset from a training exercise they use for analyst recruiting. The attack was deliberately nasty: scattered across multiple devices, spread over hours, designed to test whether candidates could reconstruct the full chain from fragmented evidence, the kind of exercise that separates senior analysts from junior ones.
Claude produced a structured incident report in under 10 seconds. Complete with timeline, affected entities, MITRE ATT&CK mapping, and evidence citations for every claim.
One of them leaned back, looked at the screen, and said what we were all thinking: “Wow! This took me 3 hours and 4 years of cyber experience to produce. We can go home.”
We’re not going home. But that moment crystallized something we’d been circling around at Torq: LLMs aren’t just good at log analysis — they’re unnaturally good at it. The question isn’t whether to use them, but whether we’re using them intelligently.
Most implementations aren’t.
The Problem With Feeding Logs Into LLMs
Here’s what the naive approach looks like (we know because we tried it first):
You have 100,000 Sysmon events from an incident. You load a summary into the context, ask the model to identify leads, then use a generic search_pattern tool to investigate each one. Seems reasonable.
It fails in predictable ways.
The filename trap: Our baseline agent started by looking at a summary of filenames — EventData,OriginalFileName — to select investigation leads. It sees powershell.exe, svchost.exe, explorer.exe. These are legitimate system binaries, so it deprioritizes them. It might chase unknown_tool.exe instead.
The problem: Living-off-the-Land attacks (LOTL) abuse legitimate system binaries. An encoded PowerShell command downloading malware looks like powershell.exe in the filename column — indistinguishable from a thousand legitimate scripts. The attack gets missed before investigation even starts.
The noise flood: Even if the agent correctly selects powershell.exe as a lead, the generic search returns 500+ events. Legitimate scripts, scheduled tasks, admin activity — all mixed with the one malicious -enc command buried somewhere in the middle (where it easily gets lost, see Lost in the middle paper). The model either drowns in tokens or picks arbitrarily.
The context window tax: Enterprise Sysmon deployments generate 4-10 GB daily for 1,000 endpoints (with aggressive tuning, default configs hit 160 GB). Even with 200K token context windows, you’re processing a fraction of relevant data. And here’s the insidious part: LLMs exhibit primacy and recency bias. Critical events buried in the middle of your log dump get underweighted or missed entirely.
This isn’t a capability problem. The model can analyze logs brilliantly — we watched it happen. It’s an architecture problem. We’re spending context on log tokens when we should be spending it on intelligence tokens.
The Breakthrough: Specialized Tools Beat Smarter Prompts
The breakthrough came when we stopped thinking about prompts and started thinking about tools.
Consider what a senior analyst actually does when investigating Sysmon logs. They don’t read every event sequentially. They have heuristics — pattern-matching shortcuts built from years of seeing attacks:
“Show me PowerShell with -enc or downloadstring“
“Which processes touched LSASS?”
“Any connections to external IPs from unusual processes?”
“What ran from Temp folders?”
Each heuristic is a filter that takes thousands of events and surfaces the handful that matter. A 10,000:1 signal amplifier. What if we encoded those heuristics as tools instead of expecting the LLM to derive them from raw logs?
Instead of returning 770 PowerShell events and hoping the model finds the needle, this tool returns only the events with encoded or obfuscated parameters — with enough context (timestamp, user, truncated command) for the LLM to reason about what happened. The input/output ratio is roughly 10,000:1, but critically, the output is actionable.
Now the model’s context gets spent on reasoning about suspicious activity, not parsing noise.
Parallel, specialized hunters analyze the same event stream from different angles. Each hunter focuses on a distinct attack pattern, then feeds findings into a centralized threat analysis layer that produces a single, coherent report.A shared dataset is filtered into multiple hunter workflows running simultaneously. Each hunter applies targeted detection logic, enriches results with LLM reasoning, and generates structured findings in real time. All hunter findings converge into a threat analysis stage, where prior context is reviewed, signals are merged and deduplicated, and an LLM generates a final verdict and executive-ready report.
The Architecture: Eight Hunters, One Investigation
One agent with 50 tools struggles to choose. It wastes tokens reasoning about which tool to use, often picks wrong, and can’t parallelize. So, we went the other direction: deploying many focused agents with five tools each, all confident in their domain.
Eight specialists run in parallel, each with a focused mandate:
The taxonomy wasn’t arbitrary. We mapped it against MITRE ATT&CK categories, validated against our training data (which techniques actually appeared in the 99,398 events), and specifically addressed blind spots in the baseline approach. LOTL attacks got their own hunter because our filename-centric baseline completely missed them.
Why static deployment instead of dynamic routing?
We considered having a “router” LLM decide which hunters to invoke based on initial signals. We rejected it for four reasons:
Coverage guarantee. Security investigations can’t afford to miss an attack vector because a router made a bad guess. All hunters run, every time.
No selection tax. A router call costs tokens and adds latency for zero investigative value.
Parallelism. All hunters execute simultaneously. Dynamic routing would serialize them.
Manageability. Since every hunter runs every time, you can monitor individual contributions. Which hunter catches the most?
When a new attack technique emerges, you add or update one hunter — not untangle a giant spaghetti prompt. Modularity makes the system evolvable. The hunters themselves remain dynamic — they decide how to investigate within their domain. But whether to investigate isn’t a question.
Escape Hatches: When Hunters Need to Deviate
Every hunter follows a checklist (encoded in their system prompt), but investigations don’t always follow checklists. Sometimes you find an IOC that demands immediate deep-diving.
Two tools enable this:
search_all_columns(pattern): The universal grep. When the LOTL Hunter finds an encoded PowerShell command containing a suspicious URL, it can immediately search for that URL across the entire dataset:
2. add_finding(text, severity, category): Structured evidence collection. Each finding flows to the Threat Analysis Hunter and the final report with full attribution:
The pattern: follow the checklist, but deviate intelligently when you find something that demands it.
The second pass: hunting for blindspots. After the initial investigation round, the hunter implicitly asks itself, ”Given what you found, what might you have missed?” This surfaces the gaps that only become visible after initial findings establish context. A lateral movement finding might prompt the Process Hunter to re-examine parent-child chains it initially dismissed. A persistence mechanism might lead the Network Hunter to look for C2 traffic that it filtered out as noise. The first round builds the picture; the next round stress-tests it.
This is only possible because we optimized the context window. When you’re burning 103K tokens on a single pass, a second round is a luxury you can’t afford — the latency and cost kill you. At 16K tokens per round, you can run multiple passes and still come out ahead. The efficiency gains don’t just save money; they unlock investigative depth that wasn’t economically viable before.
The Example: Catching What the Baseline Missed
Here’s a concrete case that illustrates the difference.
The attack: An encoded PowerShell command downloads malware:
Lead selection looks at filenames, sees powershell.exe
Deprioritizes it (legitimate system binary)
Even if selected, generic search returns 500+ PowerShell events
Malicious command buried in noise
Attack missed
Multi-Hunter approach:
LOTL Hunter calls find_powershell_encoded()
Tool filters 99,398 events → returns only the 1 event with -enc
Hunter sees the encoded string, deviates from checklist
Calls search_all_columns("malware.exe") to trace the payload
Finds FileCreate and ProcessCreate events
Records structured finding with full attack chain
Attack caught, contextualized, and attributed.
The baseline couldn’t distinguish “malicious PowerShell” from “normal PowerShell” at the selection stage. The Multi-Hunter caught it because specialized tools surfaced the exact anomaly, and the agent had the freedom to follow the thread.
The Results
We ran both approaches against the same dataset (99,398 Sysmon events from a realistic attack scenario):
Metric
Baseline
Multi-Hunter
Delta
Total tokens
103,419
16,373
-84%
LLM calls
11
28
+155%
Avg tokens/call
9,400
585
-94%
IOCs detected
23
28
+22%
MITRE techniques mapped
8
12
+50%
More LLM calls, dramatically fewer tokens per call. The specialized tools do the heavy lifting of filtering — the model spends its context on analysis, not log parsing.
The quality difference matters more than the efficiency gains. 28 IOCs versus 23. 12 MITRE techniques versus 8. Lower false positives because each finding comes from a domain-specific tool with targeted heuristics, not a generic pattern match.
Beyond Sysmon: The Pattern Generalizes
We’re implementing the same architecture for other detection scenarios at Torq. Each becomes a HyperAgent with its own specialized tools:
Impossible travel detection: Authentication events from geographically distant locations within unrealistic timeframes. The naive approach flags every cross-timezone login; specialized tools, however, correlate device fingerprints, autonomous system number (ASN) changes, and sequence anomalies to separate compromised credentials from those of someone boarding a flight.
User & Entity Behavior Analytics (UEBA): Behavioral baselines are established for each user and device, with tools that detect deviations, including unusual login hours, abnormal command patterns, and atypical data access volumes. The pattern matching happens in tools, not prompts — the LLM reasons about why a deviation matters, not whether one exists.
Suspicious administrator activity: Admins performing actions outside expected duties. Tools filter for privilege surges, bulk modifications, disabled security controls, and access to resources outside normal patterns. Correlate this with time-of-day, originating IP, and historical behavior.
PrivEsc Watchdog: Newly granted permissions that enable privilege escalation
paths. Tools track the full chain: initial grant → intermediate role → root-equivalent capability. Alert on dangerous combinations like a low-privilege user receiving iam.serviceAccounts.actAs or a newly created policy with wildcard permissions.
The principle transfers: If you know your log structure and attack patterns, encode that knowledge as specialized tools rather than expecting the LLM to derive it from raw data.
Why Dedicated LLM Agents Are the Future
This isn’t surprising if you think about how human SOCs work. You don’t have one analyst who knows everything. You have specialists — malware analysts, network forensics experts, researchers — who collaborate on complex investigations.
Each brings domain-specific tools and heuristics.
LLM agents work the same way. Specialization beats generalization. Focused tools beat broad prompts. Parallel execution beats sequential reasoning.
Here’s the counterintuitive part: specialized tools can outperform even specialized models trained for a specific task. A purpose-built ML model for PowerShell analysis requires labeled training data, ongoing retraining as attack patterns evolve, and careful threshold tuning.
A well-designed tool encoding analyst heuristics — the regex patterns that actually indicate obfuscation — works immediately, updates with a code change, and explains exactly why it flagged something. The tool doesn’t hallucinate. It doesn’t drift. It does one thing reliably.
The model wasn’t smarter than them. It was faster — and architected to spend its intelligence on analysis rather than log parsing.
Want to Know How We Built This in a Day?
We vibe-coded the entire multi-hunter architecture using Claude Code — ultrathink mode for complex reasoning, parallel agent execution,and the architect plugin for system design. Combined with a repo structure designed for parallel development, we went from concept to working prototype in under 24 hours.
The engineering deep-dive covers the implementation details: LangGraph orchestration, tool design patterns, prompt engineering for each hunter, and the lessons learned from tools that didn’t work (there were several).
Arriving in London fresh off a record-breaking Q3 in EMEA — hitting 185% of our quarterly target and expanding regional customer growth by 284% — the momentum was undeniable. But while the show floor was louder and the stakes higher than ever, one thing was obvious: security leaders weren’t looking for more claims. They were looking for proof.
Torq delivered exactly that. Having already validated the shift with global enterprise customers like Kyocera, Siemens, and Zara, we brought that proof to the main stage in a standing-room-only session featuring Virgin Atlantic CISO John White. While the swag flew off the shelves, the true draw was the agentic AI powering Torq HyperSOC™.
Here’s everything you missed — and everything people are still talking about.
Virgin Atlantic: Flying into the SOC of the Future
Virgin Atlantic’s CISO, John White, didn’t come to Torq looking for a slightly better tool. He came to rethink the SOC from the ground up.
“The world has changed,” White told the audience. “It’s an immovable wave coming our way.” Over the course of 18 months, Virgin Atlantic saw a rise in conceptual attacks and supply chain incidents that legacy tooling couldn’t keep up with. Trying to meet this surge with the same tools, same workflows, and same headcount was a recipe for failure.
Why Virgin Atlantic Chose Torq
Traditional SOAR tools were already ruled out. They demanded heavy coding, specialist skills, and long deployment cycles. Virgin Atlantic needed:
A low-code/no-code platform any analyst could pick up
Fast time to value in days, not quarters
Vendor-agnostic integrations across SIEM, identity, endpoint, and cloud
Torq fit that profile. To prove it, the CISO handed a junior analyst a test: learn Torq and automate five use cases. In less than two weeks, that analyst went from skeptic to in-house automation specialist, turning roughly 40 hours of weekly manual work into fully automated workflows.
Automating During an Active Incident
The real test came during a live incident. With no extra budget, the CISO made the case to bring Torq in midyear — and deployed it while the team was actively managing an attack.
Because Torq worked out of the box, they could immediately automate the first set of Tier-1 tasks they had validated in proof of concept. Those workflows removed repetitive load during the incident, freeing analysts to focus on investigation, not busywork.
That move paid off twice: the team stayed ahead of the incident, and leadership saw clear evidence that Hyperautomation helped the “layer underneath” the SOC, rather than adding more overhead. “You can only do that if the solution works out of the box. Torq did,” said White.
The People Impact
The transformation reshaped the security team’s career paths:
Analysts no longer burned time on repetitive checks
Junior staff gained new skills and ownership through automation
The SOC shifted from reactive triage to proactive investigation.
Other teams — privacy, GRC, and beyond — started asking how they could use Torq to automate their own processes. What began as a SOC initiative started to influence how the wider organization thought about operational efficiency.
“No one gets into security to be a Tier 1 analyst forever. Automation gives them a future.”
For Virgin Atlantic, Torq delivered three outcomes at once:
The SOC reduced manual toil and alert fatigue without adding headcount.
Analysts gained more meaningful, senior work instead of repetitive triage.
Leadership saw better use of existing resources and faster incident handling.
“Automation for me is one of those things that kind of ticks so many boxes. It’s a win for the organization, a win for the security team, a win for the staff. It’s a win-win-win.”
If John White’s session explained the why, the Torq booth showed the how. The HyperSOC demo stopped attendees in their tracks at the conference. Security leaders crowded around to watch a full, agentic AI–driven investigation run end-to-end without human input.
Analysts, CISOs, and even competitors came to the booth to watch alerts enrich themselves, cases build in real time, and HyperAgents plan, reason, and execute response steps across identity, endpoint, cloud, and SaaS tools.
Why it hit so hard:
Real agentic reasoning, not pre-canned outputs or offline summaries
Full-stack orchestration across SIEM, EDR, IAM, CSPM, and SaaS
Native case management with AI-generated timelines, summaries, and next-step recommendations
In a sea of “AI-washed” SOC tools, Torq showed an autonomous system that actually works at enterprise scale — moving from category buzzword to the real thing, shipping today.
Agentic AI in the SOC — for Real
Agentic AI was the buzzword of the conference, and it seemed like every vendor had a new “AI Agent.” But there’s a big difference between marketing hype and actual AI in production handling real-world use cases in Fortune 500 environments.
Dynamic remediation that adapts to context and policy
Multi-agent collaboration at machine speed
If you want to see the most talked-about demo of Black Hat Europe 2025, you know where to find us. Get a demo or the ‘Don’t Die, Get Torq’ manifesto to get started.
Fareed Cheema is the Global Head of Sales Engineering at Torq, leading worldwide pre-sales strategy, execution, and technical innovation. Over the past 3.5 years, he has helped scale Torq’s technical and go-to-market teams while driving customer success in a rapidly changing security automation market. With more than 20 years in cybersecurity, Fareed blends deep technical expertise with strong enterprise sales and product strategy experience, building teams that translate complex technology into clear business value.
The MSSP cybersecurity market is entering a disruptive shift. Customer expectations are rising, security threats are accelerating, margins are shrinking, and the cybersecurity talent shortage continues to intensify. Traditional managed security service providers’ reliance on manual triage, ticket queues, and human-led SOC response can’t scale to meet 2026 demand.
At the same time, enterprise buyers are becoming more sophisticated. They want measurable security outcomes, not alerts. They want speed, not SLA excuses. They want a security service provider who can autonomously remediate threats, contain malware, continuously enforce compliance, and improve security posture instantly.
This is the new reality shaping how MSSP services are delivered. In response, the top managed security service providers are embracing AI-driven Hyperautomation, a shift that transforms MSSP cybersecurity from labor-intensive service delivery to scalable, machine-speed operations.
Below are four defining MSSP trends for 2026 and how Hyperautomation is powering the next generation of cybersecurity service providers.
Trend 1: AI-Driven Automation Becomes the Core of MSSP Cybersecurity
MSSPs are no longer competing on headcount or the size of their analyst teams; they win by automating security monitoring, investigation, and detection. In 2026, the MSSPs gaining the most market share will be the ones shifting their operating model from human-led workflows to AI-driven automation.
This shift includes adopting capabilities such as:
AI-driven triage that automatically eliminates noise and identifies real threats without human intervention
Agentic AI analysts that autonomously investigate alerts, perform vulnerability management, and contain endpoint threats
No-code automation frameworks that allow MSSPs to onboard new customers in hours, without engineering overhead
Unified multi-tenant case management, replacing dozens of disconnected ticketing queues and manual handoffs with a single, repeatable automation layer
Forward-thinking MSSPs implementing AI automation like Hyperautomation platforms are already achieving:
90–95% autonomous Tier-1 alert handling, effectively eliminating the most resource-draining portion of SOC operations
MTTR reduction from minutes to seconds, enabling machine-speed containment across customer environments
The ability to onboard more customers with fewer analysts, unlocking higher margins and accelerating growth without adding labor
This is Hyperautomation’s true value: the ability to scale managed security service delivery without hidden cost, increasing headcount, or operational complexity.
Trend 2: Cybersecurity Services Dominate MSSP Growth and Margins
Cybersecurity services represent the highest-margin opportunity of the managed security service provider business. As threats evolve, customers expect their MSSPs to deliver more than alerting; they expect action.
Across industries, enterprises now require MSSPs to support:
AI-enhanced MDR that identifies and prioritizes threats in real time
Identity threat detection, including impossible travel, privilege escalation, and abnormal SaaS activity
Cloud misconfiguration monitoring and remediation, especially across AWS, Azure, and GCP
Continuous compliance with evidence collection, drift detection, and automated audit reporting
AI-powered threat hunting guided by context from cloud, identity, endpoint, and network signals
Automated incident response, not manual Slack messages or ticket escalations
The message from enterprise customers is clear: “Don’t notify us. Fix it.” This expectation is forcing MSSPs to adopt autonomous response platforms that can:
Enrich and correlate alerts automatically, reducing noise and improving fidelity.
Remediate identity and cloud risks instantly, from disabling compromised accounts to correcting misconfigurations.
Document every AI action for compliance audits, insurance requirements, and customer reporting.
Execute cross-tool, multi-cloud response sequences that historically required tiered human intervention.
Legacy MSSPs operate with bloated tech stacks: multiple SIEMs,SOAR platforms, XDR tools, CSPMs, IAM systems, firewalls, ticketing queues, and custom scripts. This fragmentation crushes margins and burns out analysts who spend their days stitching SOC tools together instead of defending customers.
In 2026, MSSPs are aggressively shifting toward:
Fewer tools and deeper automation, freeing analysts from manual correlation and multi-console workflows
Unified platforms that connect detection → triage → case management → response within one operational layer
Automation-first SOC operations, where AI Agents drive the bulk of investigation and remediation
Multi-tenant orchestration, enabling standardized service delivery across every customer environment
As MSSPs consolidate platforms, they seek systems that eliminate:
Manual correlation of cross-tool alerts
High-maintenance SOAR playbooks
Ticketing swivel-chair work between systems
Cloud misconfiguration backlogs
Manual identity investigation and verification loops
This is exactly why a growing number of cybersecurity service providers are replacing legacy SOAR with Torq HyperSOC™, a unified, AI-native Hyperautomation platform built for multi-tenant MSSP environments.
The cybersecurity talent shortage is worsening. Hiring is slower, salaries are rising, turnover is high, and the expertise required to run a modern security operations center is increasing. MSSPs feel this pressure more than anyone because they support multiple customers with limited teams.
To stay competitive, MSSPs are turning to autonomous SOC capabilities, including:
AI SOC Analysts like Torq Socrates, who can investigate cases, perform triage, gather evidence, remediate threats, and interact with users autonomously
AI-driven detection triage, filtering out false positives and prioritizing incidents based on real business impact
Automated case investigation, eliminating the human burden of enrichment, log review, and context gathering
Automated user communication, handling Slack/Teams verification, MFA checks, and employee follow-up without analyst involvement
Multi-tenant capabilities, enabling MSSPs to scale services instantly across all customers
With Torq powering these workflows, MSSPs can:
Deliver 24/7 cybersecurity coverage without 24/7 staffing, improving coverage while reducing labor costs
Scale customers without scaling payroll, unlocking real margin expansion
Offer premium MSSP cybersecurity services with higher margins
Reduce churn, as customers see faster response times, transparent audits, and consistent outcomes
Modern MSSPs don’t need larger analyst teams; they need an autonomous SOC engine that multiplies the capabilities of the team they already have.
How Torq Hyperautomation Helps MSSPs Lead in 2026
Torq HyperSOC™ is the AI-native autonomous SOC platform MSSPs use to modernize their entire service delivery model through:
Multi-tenant Hyperautomation for repeatable service delivery
Agentic AI that triages, enriches, investigates, and remediates autonomously
Extensive integrations with SIEM, endpoint tools, EDR, XDR, CSPM, IAM, and cloud providers
MSSPs using Torq report:
10× analyst productivity
95% reduction in manual triage
Faster onboarding and customer growth
Stronger differentiation against competing cybersecurity service providers
“Based on customer feedback when we showcase our services, Torq is the ideal solution for adding value to our managed SOC, particularly with its seamless integrations. By accelerating our automations and responses, Torq Hyperautomation helps us stay ahead of the curve and the competition.”
MSSP Alert Live 2025 showcased where the managed security service provider market is headed: faster response, outcome-driven service delivery, and unified operations across cloud, identity, and endpoint. The sessions spotlight the same pressures MSSPs face daily (more alerts, more customers, fewer analysts) and why the shift toward AI-driven Hyperautomation is accelerating.
This year’s agenda reflects the challenges and opportunities we solve every day with Torq HyperSOC™ and our Managed Services offerings:
AI for incident response and crisis comms: Customers expect autonomous containment, not manual escalations. Torq’s multi-tenant architecture handles triage, enrichment, user verification, and containment automatically across every customer tenant.
How to scale MSSP teams despite talent shortages: MSSPs using Torq replace 90–95% of Tier-1 work with autonomous investigation and response. This lets providers expand their customer base without adding analysts.
Cyber liability and insurance: Auditable AI actions, standardized playbooks, and multi-tenant case management help MSSPs meet insurer expectations without adding compliance overhead. Torq equips MSSPs with evidence-rich reporting built for cyber liability reviews.
Selling next-gen security services: Customers want outcomes. Torq gives MSSPs the automation engine to deliver them: automated MDR, cloud risk remediation, SaaS access governance, identity verification, and complete case resolution at machine speed.
2026 is the Year MSSPs Transform Their SOC
The MSSPs that will win in 2026 aren’t the ones adding more tools or more people. They’re the ones embracing a new operational model powered by AI-driven Hyperautomation, where investigation, triage, enrichment, and even containment happen autonomously across every customer environment.
This shift is the only viable path to:
Delivering differentiated MDR services
Managing multi-cloud infrastructure
Closing thousands of alerts per day
Scaling customers without scaling payroll
Meeting rising expectations around response speed and outcomes
Improving enterprise security posture
Torq HyperSOC is enabling MSSPs to build the autonomous, multi-tenant SOC required to thrive in this new market, delivering faster response, higher margins, and a truly scalable service model.
2026 belongs to the MSSPs that automate, integrate, and deliver outcomes. To see the future of MSSP cybersecurity, get the Managed Services Manifesto.
What is MSSP cybersecurity, and what does an MSSP actually do?
MSSP cybersecurity refers to outsourced protection delivered by a managed security service provider that handles continuous security monitoring, threat detection, vulnerability management, and incident response. The MSSP meaning is simple: a third-party cybersecurity service provider that operates a 24/7 security operations center to defend an organization’s infrastructure, endpoints, cloud, and users from evolving threats and breaches.
What is the difference between an MSP, MDR, and an MSSP in cybersecurity?
An MSP manages IT systems, while an MSSP is a specialized cybersecurity service provider that focuses on managed security services, including threat detection, intrusion prevention, SIEM monitoring, and MDR-style response. The difference between MSP and MSSP comes down to depth: MSSPs deliver continuous security operations, advanced analytics, and compliance protection, not just IT maintenance. MDR providers focus specifically on advanced threat hunting, real-time detection, and rapid containment. MDR is laser-focused on response, while MSSPs provide full-stack security operations.
What core MSSP services do managed security service providers offer today?
Modern MSSP services include intrusion detection, SIEM management, endpoint security monitoring, vulnerability scanning, threat hunting, SOC operations, firewall management, cloud and identity security, and automated incident response. A managed security service provider centralizes these capabilities to reduce risk, strengthen security posture, and provide continuous protection across hybrid and multi-cloud environments.
What are the benefits of managed security services for enterprise SOC teams?
The top benefits of managed security services include 24/7 monitoring, faster detection, reduced impact of breaches, stronger compliance, and access to advanced cybersecurity expertise. MSSPs act as an outsourced security partner, improving visibility across infrastructure, endpoints, cloud, and networks. This helps SOC teams reduce noise, increase response times, and enhance their overall security posture.
How does Hyperautomation transform MSSP cybersecurity operations in 2026?
Hyperautomation transforms MSSP cybersecurity by replacing manual SIEM triage, log analysis, and case investigation with AI-driven automation. It accelerates detection, identifies threats across endpoints and infrastructure, automates response actions, and improves SOC efficiency. This enables MSSPs to scale services, reduce labor costs, prevent breaches, and deliver faster, more consistent outcomes for customers.
How do MSSPs help prevent breaches, malware, and intrusion across multi-cloud environments?
MSSPs reduce breach, malware, and intrusion risk by delivering continuous security monitoring, SIEM/XDR correlation, endpoint protection, firewall management, and automated containment. Their cybersecurity services combine threat hunting, vulnerability management, and incident response to identify threats early and neutralize them before they spread across cloud, on-prem, or hybrid environments.
The modern security stack is crowded, but often disconnected. You have best-in-class tools for detection, identity, and cloud, but if they aren’t talking to each other, your team is stuck acting as the manual glue.
AMP is about more than just API keys. We co-build deep, production-ready integrations that allow our partners to signal high-fidelity data to Torq, which then orchestrates the response across your entire infrastructure.
In Season 1 of The AMP’d Sessions, we showcased how these integrations work in the real world. Here is how Torq and our partners are closing the loop on security operations.
Wiz: Autonomous Cloud Security
Cloud environments move too fast for manual ticketing, and the disconnect between Security and DevOps often leaves critical risks exposed for days. This partnership bridges that gap by turning Wiz’s high-fidelity visibility into machine-speed action.
When Wiz flags an alert — like a vulnerable container with exposed secrets — Torq instantly ingests the alert and triggers a cross-team workflow. By automatically spinning up Slack channels, syncing contexts between DevSecOps and Cloud teams, and pre-populating Jira tickets, Torq ensures the right people have the right info instantly. Once the fix is applied, Torq validates the remediation via Wiz and autonomously closes the case.
Intezer: The Power of Agent-to-Agent Collaboration
Tier-1 analysts often burn out from repetitive triage before they can tackle critical threats, but this integration changes the dynamic through agent-to-agent collaboration. Intezer’s AI agents emulate elite forensic analysts, investigating alerts and extracting artifacts with 97.6% accuracy to filter out false positives before they ever reach your queue.
Once the threat is confirmed, Intezer hands that verified forensic context to Torq’s AI SOC Analyst, Socrates. Socrates immediately takes the baton to orchestrate the response — isolating hosts, blocking hashes, and resetting credentials across the environment. This allows the autonomous SOC to resolve over 95% of Tier-1 cases without a human ever needing to open a ticket.
Even with strong prevention, threats inevitably slip through. That’s where Zscaler Deception comes in, deploying SaaS-based decoys to lure attackers and reveal “patient zero” moments early in the kill chain. When a decoy is touched, Zscaler flags the high-fidelity alert, and Torq HyperSOC™ springs into action.
Socrates correlates the telemetry and autonomously executes an agentic runbook — contacting the user via Slack to verify activity and performing MFA checks. If the threat is valid, Torq isolates the endpoint and blocks the user instantly, achieving sub-minute containment.
For most SOCs, data exposure is a blind spot. Cyera’s Data Security Posture Management (DSPM) platform addresses this by continuously scanning cloud and SaaS environments to pinpoint sensitive risks, like exposed patient records or financial data.
In the AMP’d demo, when Cyera detected a Microsoft 365 file containing personally identifiable information (PII) shared publicly, Torq automatically created a case and launched a remediation workflow. Socrates revoked the public access immediately and messaged the employee to confirm intent. The entire process from detection to evidence collection and closure took less than five minutes, creating a continuous feedback loop between visibility and action.
Legacy SIEMs force teams to compromise on data retention and cost, but Panther’s cloud-native data lake allows for limitless scale and long-term retention. Panther uses a “Detection-as-Code” model to generate high-fidelity, AI-triaged case summaries that are passed directly to Torq. This initiates an AI-to-AI communication where Torq Socrates reasons through Panther’s findings.
In the use case, Panther detected an anomalous login from a watchlist country followed by enumeration. Socrates autonomously queried the data lake for more logs, interviewed the user via Slack, and, upon confirming the threat, disabled the Okta account and blocked the IP, closing the loop at machine speed.
SaaS is the fastest-moving attack surface, and most breaches stem from the same problem: identity drift across hundreds of connected apps and an explosion of unvetted AI tools. Reco maps this chaos with deep, identity-driven visibility across every SaaS application: who has access, what data is exposed, and where permissions exceed policy.
When Reco flags a high-risk access event, Torq HyperSOC™ turns that signal into immediate, explainable action. Socrates enriches identity context, validates activity, interviews users in Slack, and enforces policy through autonomous workflows. Whether the right move is revoking OAuth permissions, blocking risky AI apps, or escalating for manager review, the system executes consistently across the entire environment.
Together, Reco and Torq give SOC teams a full end-to-end loop for SaaS access security — continuous discovery, precise identity intelligence, and autonomous remediation, delivered without adding workload to analysts.
Across every partner and every episode, one theme dominated: You don’t fix SecOps by throwing more dashboards at analysts. You fix it by building autonomous, closed-loop systems.
AMP’d Season 1 showed exactly how the strongest security stacks get there:
AI-to-AI communication that eliminates human bottlenecks
