If you are evaluating security platforms in 2026 based on which one has the best chatbot or can write a slightly better Python script for you, you’re fighting the last war. 

Attackers are already using AI to scale their operations with speed and precision. If your “AI SOC platform” is just a co-pilot that summarizes tickets while humans do all the work, you’re behind.

The modern SOC is shifting from automated (static playbooks and scripts) to autonomous — an AI SOC platform powered by agentic AI that can reason, plan, and act within explicit guardrails.

We break down what the best AI SOC platforms actually need to deliver, how leading architectures differ, and why platform choice now is really an architecture decision.

What Sets Top AI SOC Platform Architectures Apart in 2026

To operate at machine speed, defend against AI-enhanced adversaries, and eliminate manual work, a next-generation AI SOC platform must deliver five core capabilities. These capabilities map directly to where legacy systems fail: data sprawl, slow investigations, brittle automation, and siloed case management.

1. A Unified Operational Data Layer

Legacy architectures assumed that every alert and log file had to be funneled into a SIEM for analysis, creating a massive data bottleneck and a single point of failure. As cybersecurity analyst Francis Odum noted at Torq’s SKO 2025: “Legacy SOAR assumed everything starts in the SIEM. Now, teams connect automation directly to EDR, email, and identity systems”.

A true AI SOC platform must deliver:

• SIEM-agnostic connectivity: The platform should consume alerts and logs from any SIEM (Splunk, Sentinel, QRadar, Sumo Logic, Elastic) without forcing data migration or lock-in.
• Native integrations across identity, cloud, SaaS, EDR, NDR, and email security: This includes Okta, Entra ID, AWS/GCP/Azure, CrowdStrike, SentinelOne, Proofpoint, Zscaler, Netskope, and more.
• Decentralized processing: Instead of aggregating data into a centralized point before taking action, the platform integrates directly with data lakes and tools to create a unified control plane.

When SOC tools and data are disconnected, SOCs suffer higher mean time to respond (MTTR), more context switching, and lower detection quality. The best AI SOC platforms treat unified, real-time telemetry as a non-negotiable foundation.

2. Autonomous Investigation and Response 

In a next-generation SOC, analysts should never have to manually:

• Enrich alerts
• Pivot across six browser tabs
• Copy and paste logs
• Correlate IPs, hashes, and identities
• Ask users “Was this you?”
• Check cloud exposure severity
• Determine whether an alert is real or noise

A true AI SOC platform takes over these tasks and autonomously executes:

• Identity enrichment (such as roles, MFA events, privileges, and historic activity)
• Endpoint posture and behavioral indicators
• SaaS OAuth scope analysis
• Network and cloud asset risk context
• Threat intelligence lookups
• Log retrieval, summarization, and normalization
• Evidence collection for case management

This shift significantly improves critical metrics like MTTD and MTTR by removing the latency of manual investigation.

3. Agentic AI Capabilities 

The best AI SOC platforms must include agentic AI, which is AI that can reason, plan, adapt, and take actions within defined guardrails. In a fully realized AI-native SOC, a multi-agent system (MAS) can handle 90%+ of Tier-1 security analysts’ tasks.

Agentic AI enables:

• Goal-driven planning: Instead of executing a static playbook, the AI determines how to reach an outcome (e.g., “Validate whether this login is malicious”).
• Dynamic tool use: AI selects which systems to query — SIEM, identity provider, EDR, cloud APIs — based on context.
• Contextual memory: The AI remembers case details, user signals, prior actions, and earlier investigations.
• Independent decision-making: Within guardrails, AI decides:
  • Is the alert true or false?
  • Should a user be challenged?
  • Is the cloud resource exposed?
  • Which action mitigates the threat fastest?

The platform must ensure this happens safely, predictably, and auditably — not as “black box” reasoning.

4. Native Case Management 

Traditional ticketing systems were never designed for security investigations. They fragment context, slow down collaboration, and give AI very little structure to reason over.

A true AI SOC platform needs native case management designed specifically for security operations with:

• Autonomous case generation: Cases should be created automatically from alerts based on severity, correlation, identity risk, or cloud exposure.
• AI-driven prioritization: AI analyzes blast radius, business criticality, and user behavior to determine which cases matter most.
• Integrated collaboration: Slack, Teams, email, and ticketing systems (like Jira or ServiceNow) are synced without forcing analysts to leave the AI SOC console.
• Full evidence timeline: Every alert, enrichment, AI decision, human approval, and automated action must be fully logged.
• Audit-ready transparency: Compliance and cyber insurance increasingly require AI explainability. Native case management makes this possible.

5. Open Ecosystem + Model Context Protocol (MCP)

Flexibility is the difference between a scalable AI SOC platform and a platform that traps you in inefficiencies.

Top AI SOC platforms must provide:

• Comprehensive integrations: Hundreds of connectors for identity, cloud, EDR, SIEM, firewalls, ticketing, SaaS, DevOps tools, and threat intelligence solutions.
• No-code + low-code workflow creation: Analysts should be able to build or edit automation with zero Python dependency.
• Support for API-first and event-driven architecture: AI should react instantly to events — not wait for cron jobs or polling intervals.
• Rapid onboarding without professional service or engineering dependency: If it takes weeks of professional services to onboard new integrations, it’s already obsolete.
• Model Context Protocol (MCP) support: To facilitate reliable communication between AI agents and tools, leading architectures now support MCP, an open protocol that standardizes the way applications provide context to AI agents.

AI SOC Platform Architecture Comparison

Most products marketed as an “AI SOC platform” fall into three architectural categories.

1. AI-Enhanced Platforms 

Many products marketed as AI SOC platforms are better described as AI-enhanced security platforms. Architecturally, these solutions are centralized detection and analytics ecosystems that rely on large-scale data aggregation to improve visibility, correlation, and analyst productivity.

Aggregating and normalizing telemetry across identity, endpoint, cloud, network, and SaaS tools is essential for agentic reasoning at scale. When signals are locked inside individual silos, each tool only sees part of the picture — and understands it in part. Aggregation sets the stage for AI to correlate related activity, assemble the whole picture, and surface real risks that would otherwise remain obscured.

The architectural challenge arises from how that aggregation is implemented.

Platforms like Cortex XSIAM and Microsoft Sentinel require customers to ingest the majority of their telemetry into vendor-owned, proprietary data lakes to unlock their most advanced AI capabilities. While this can improve detection and analytics within the platform, it introduces several structural risks security leaders must evaluate carefully, such as:

• Vendor lock-in by design: Once large volumes of historical telemetry are stored in proprietary formats, migration becomes costly and operationally disruptive. This creates renewal leverage for the vendor, limiting long-term architectural flexibility.
• Captive storage economics: Customers are locked into premium ingestion and retention pricing models with limited tiering or external storage options, despite growing data volumes year over year.
• Integration asymmetry: These platforms typically offer deep, native integrations for tools within their own ecosystem, while providing shallower or less capable integrations for competing third-party security products.
• Platform-first optimization: The data lake is optimized to retain customers within a single ecosystem, rather than enabling best-of-breed security architectures across vendors.

As a result, the AI experience can initially feel powerful — until teams need to investigate or remediate across tools the vendor doesn’t own. At that point, automation often degrades into brittle connectors, custom engineering, or manual analyst effort. This is what many security leaders now refer to as the ‘integration tax’.

A true AI SOC platform still aggregates and normalizes data, but does so without holding that data hostage. It favors open standards (such as OCSF), vendor-agnostic access, and flexible storage choices. The goal isn’t centralization for its own sake; it’s open, normalized telemetry that empowers agentic AI to reason and act across heterogeneous, multi-vendor environments.

2. Legacy SOAR

Legacy SOAR platforms helped define automation years ago, but they were never architected for autonomous operations or agentic AI. These systems still rely on playbook-driven, script-heavy automation, using Python and operator-defined logic. 

Because SOAR engines were built for manual playbook triggering, not autonomous reasoning, vendors layer generative AI on top rather than rebuilding the stack around it.

Legacy SOAR tools fall short because:

• Their core automation engine is still script-based, brittle, and infrastructure-heavy
• AI cannot operate beyond summarizing or accelerating playbook creation
• They cannot autonomously investigate, correlate, or remediate cases
• Scalability and maintainability depend heavily on engineering resources
• AI is bolted on, not built into the core reasoning and execution layer

In short: the AI is a feature, not the engine of the platform.

3. A True AI SOC (AI-Architected)

Torq pioneered the AI SOC category because traditional SOAR couldn’t handle the scale of modern hybrid cloud enterprises.

A true AI SOC platform must:

• Correlate and reason over multi-vendor, multi-cloud telemetry
• Generate and prioritize cases automatically
• Make policy-aware decisions in real time
• Execute remediation actions safely and autonomously
• Maintain full auditability and operational control

Torq delivers this through:

• Generative AI for investigation, summarization, and communication
• Agentic AI for adaptive reasoning and action
• Hyperautomation to orchestrate actions across your entire security stack
• Case Management to unify triage, investigation, and response in a single view
• Multi-Agent System Architecture for coordinated, parallel execution across tools

Torq’s AI SOC agents, led by Socrates and bolstered by HyperAgents, don’t just suggest actions — they can execute them within your guardrails. For example, they can:

• Interview users via Slack or Teams to validate activity
• Investigate alerts across SIEM, EDR, IAM, cloud, and SaaS tools
• Enrich, correlate, and summarize findings into a native case
• Remediate threats automatically where policy allows
• Maintain an immutable, auditable trail of every step
  

Torq works well for all SOCs, but especially lean teams that want to eliminate backlog, and enterprises that need an AI SOC platform that can scale without inheriting the fragility and maintenance burden of script-heavy legacy systems.

“As new entrants crowd into the space with ambitious roadmaps and evolving terminology, Torq increasingly functions as the reference point others are measured against…. In that sense, Torq is more or less the de facto leader of the AI SOC space. While the category is now being treated as emerging, Torq’s position reflects something closer to incumbency — an established platform in a market that is only just catching up to what it represents.

– Forbes, The AI SOC Boom Is Real, But The Work Started Long Before The Buzz

10 Questions to Ask Before Choosing an AI SOC Platform

Ask these ten questions during your next demo to separate the AI SOC platform contenders from the pretenders.

1. Can the AI autonomously investigate and resolve security cases using predefined runbooks written in natural language?
2. Does the AI provide structured, evidence-linked case summaries with direct citations to original forensic data?
3. Can the platform safely execute containment actions with human-in-the-loop approvals and predefined guardrails?
4. Does the solution integrate natively with your existing SIEM, EDR, IAM, cloud, and SaaS stack without custom engineering?
5. Does the vendor use customer data to train or fine-tune AI models, or is all data kept isolated?
6. Is the system compliant with SOC 2 Type II, HIPAA, GDPR, and other major trust frameworks?
7. Does the solution provide immutable logs of all AI-driven actions, inputs, and outputs for auditing and insurance needs?
8. Is the AI restricted to act only within explicitly enabled workflows, with no standalone entitlements to IT assets?
9. Does the architecture support true multi-tenancy isolation for MSSP or multi-business-unit deployments?
10. How does the AI SOC Analyst license work, and are there extra costs tied to usage, tuning, or model quotas?

How Valvoline Transformed Security with an AI SOC Platform

Valvoline’s experience illustrates what separates a true AI SOC platform from legacy SOAR and point solutions that limit AI capabilities to just the first 30 seconds of triage. When Valvoline’s security team was cut in half during a major divestiture, their legacy SOAR couldn’t keep up. Critical integrations failed, phishing alerts overwhelmed analysts, and investigations stalled under manual workload.

Some vendors claim AI capabilities while stopping at alert classification — telling you which alerts to investigate, then handing everything else back to your overwhelmed analysts. Valvoline needed a platform that handled the entire incident lifecycle: detect, triage, investigate, contain, and remediate. 

Torq transformed that reality in days. Within 48 hours of deployment, Valvoline automated high-volume, repetitive Tier-1 tasks, especially phishing triage, which previously consumed up to 12 analyst hours per day. Torq’s no-code workflows, agentic AI decisioning, and unified case management allowed the team to streamline investigations, accelerate containment, and eliminate manual steps that previously buried analysts.

With Torq, Valvoline now:

• Saves 6–7 analyst hours every day through automated email and alert triage
• Executes real-time containment when users click malicious links, including password resets, session termination, and cross-platform isolation
• Correlates evidence automatically across Microsoft 365, Defender, CrowdStrike, Rapid7, and more
• Runs workflows built by non-developers, thanks to Torq’s intuitive no-code design
• Maintains full auditability through native case management with complete evidence timelines

“Torq takes the vision that’s in your head and actually puts it on paper and into practice.”

– Corey Kaemming, CISO, Valvoline

The Best AI SOC Platform Is an Architecture Choice

The security landscape of 2026 demands more than a slightly faster version of your 2020 stack. It requires a fundamental shift in how your SOC operates.

The future isn’t about who has the prettiest chatbot. It’s about which AI SOC platform architecture gives you:

• An aggregated and normalized security data lake
• De-duplicated and correlated telemetry, to reduce noise
• Transparent agentic triage with guardrails, for clarity and focus
• Native, auditable case management
• Autonomous investigation and response actions
• An open ecosystem that deeply integrates with your security stack

Build an autonomous SOC that fights at machine speed, with humans firmly in control of risk and policy. Get the AI or Die Manifesto to learn how to deploy AI in the SOC the right way.

FAQs

Security Operations Centers (SOCs) are the command center of an organization’s frontline cybersecurity defenses — responsible for monitoring threats, prioritizing alerts, and orchestrating remediation. However, today’s SOCs are facing an existential crisis: an overwhelming volume of increasingly complex and AI-scale threats combined with a shortage of skilled analysts. This perfect storm is pushing SOCs to their breaking point, burning out their teams and leaving their organizations vulnerable.

Legacy security automation solutions struggled to keep up with the evolving threat landscape, especially at scale. The rise of artificial intelligence (AI) has been hailed as a game-changer for SOCs, offering the potential for unprecedented efficiency gains.

But what does effective AI use in the SOC look like, and what’s the difference between AI in the SOC and an AI SOC? Below, we break down everything you need to know about AI-powered security operations.

What is an AI SOC?

But here’s what matters most: the AI SOC doesn’t stop at analysis.

While many solutions focus solely on detection and triage, the true value of an AI SOC lies in managing the complete threat lifecycle — from triage through investigation to response. The agentic SOC takes action and closes cases autonomously.

Modern security operations is shifting from automated (static playbooks and scripts) to autonomous (agentic AI that can reason, plan, and act within explicit guardrails). This distinction matters: the difference between AI as a feature and AI as the engine of your security operations is the difference between incremental improvement and operational transformation.

AI in the SOC vs. AI SOC: What’s the Difference?

Not all AI-powered security is created equal. There’s a critical distinction between adding AI capabilities to an existing SOC and building a truly AI-native SOC.

AI in the SOC refers to bolt-on AI tools layered on top of traditional SOC infrastructure — a copilot here, a chatbot there, maybe some machine learning (ML)-based detection. These point solutions can provide incremental improvements, but they typically stop providing any real value at a crucial tipping point: the verdict. AI that simply triages alerts but doesn’t take the next step to turn analysis into action won’t fundamentally change how the SOC operates. Analysts still context-switch between disconnected tools, manually correlate data across systems, and spend hours on repetitive tasks to actually contain and remediate threats. In this scenario, the AI assists, but the human remains the bottleneck.

An AI SOC is architecturally different. It’s built from the ground up with AI at the core — not as an add-on, but as the foundation. In a true AI SOC:

• AI agents don’t just advise — they act. They autonomously triage, investigate, and remediate threats across the complete lifecycle.
• The platform is unified, not fragmented. A single operational data layer connects your entire security stack without forcing data migration or vendor lock-in.
• Humans shift from operators to overseers. Instead of manually executing every step, analysts provide strategic direction and handle only the cases that truly require human judgment.
• Automation is agentic, not scripted. Rather than rigid playbooks, AI reasons through novel situations, adapts to new threat vectors, and takes goal-driven action within defined guardrails.

AI in the SOC speeds up analyst work slightly. A true AI SOC fundamentally reimagines how analysts spend their time.

The Technical Foundations of an AI SOC

Security automation has evolved way past SOAR and even the basic no-code/low-code automation platforms that quickly became standard-issue features. The new cornerstones of the modern autonomous SOC are Hyperautomation and AI agents.

• AI-driven Hyperautomation: By seamlessly integrating your security stack and instantly automating any security process using thousands of pre-built integration steps and AI-generated workflows, Hyperautomation offloads routine tasks, reduces analyst burnout, and accelerates threat response.
• Multi-Agent System: Specialized AI agents automate incident response by interpreting natural-language instructions and collaborating to autonomously execute tasks such as alert triage, containment, and remediation. Human analysts can interact with AI agents using natural language to accelerate enrichment, investigation, and recommended next steps.

Five Core Capabilities of a True AI SOC

To operate at machine speed, defend against AI-enhanced adversaries, and eliminate manual work, a next-generation AI SOC must deliver five core capabilities:

1. A unified operational data layer: A true AI SOC delivers SIEM-agnostic connectivity with native integrations across identity, cloud, SaaS, EDR, NDR, and email security — enabling decentralized processing without forcing data migration or vendor lock-in.
2. Autonomous investigation and response: A true AI SOC eliminates manual alert enrichment, tab-switching, and log correlation by autonomously executing identity enrichment, endpoint posture analysis, threat intelligence lookups, evidence collection, and more.
3. Agentic AI capabilities: The best AI SOCs include agentic AI that can reason, plan, adapt, and take actions within defined guardrails — enabling goal-driven planning, dynamic tool use, contextual memory, and independent decision-making that is safe, predictable, and auditable.
4. Native case management: A true AI SOC requires purpose-built case management with autonomous case generation, AI-driven prioritization, integrated collaboration, full evidence timelines, and audit-ready transparency — not legacy ticketing systems that were never designed for security investigations.
5. Open ecosystem + Model Context Protocol (MCP): Top AI SOCs provide comprehensive integrations, no-code workflow creation, API-first architecture, and support for MCP — the open protocol that standardizes communication between AI agents and tools.

AI in the SOC Terminology, Explained

This new landscape of AI in the SOC comes with a LOT of similar-but-different terminology. GenAI, AI Agents, OmniAgents, agentic AI, multi-agent systems — we get it, it can be confusing. 

Here’s a breakdown of all the AI powering modern security operations, what each one does, and how Torq HyperSOC™ puts them all to work. 

AI SOCs Complete Threat Lifecycle Management

One of the benefits of a true AI SOC is that it manages the complete threat lifecycle. Here’s how each stage transforms traditional security operations:

Triage: The AI SOC ingests and normalizes telemetry from across your security stack, correlating and deduplicating events to reduce noise. Agentic AI analyzes risk context and threat intel to deliver verdicts that separate false positives from actual risk — before alerts ever reach a human analyst.

Investigate: Cases are assigned to a task force of specialized, customizable AI Agents that work at the direction of your staff to gather evidence, assemble timelines, and summarize findings. This removes manual bottlenecks and expands SOC capacity, all with the transparency, oversight, and control your team demands.

Respond: The AI SOC enables autonomous response actions to contain threats quickly and ensure critical threats are seen by the right people. Over 90% of cases can be remediated completely autonomously, freeing your team to do what they do best: threat hunting, strategic planning, and high-level decision making.

Top Use Cases for AI SOCs

By analyzing vast amounts of data from across your security stack and executing intelligent automations, AI unlocks efficiency gains across SOC functionalities such as:

• Incident investigation: Analyze massive volumes of alerts to identify patterns, suppress low-fidelity alerts, and automate triage and validation, accelerating the investigation process from start to resolution. 
• Case management: Streamline the process of prioritizing, tracking, and managing security incidents by intelligently enriching and automating cases.
• Workflow generation: Prompt AI with a natural language description of your use case to instantly build security automation workflows — no code required.
• Case summarization: Analyze all relevant data points associated with a security alert to provide easy-to-digest, evidence-backed summaries of complex security cases, improving SOC analysts’ efficiency and collaboration.
• Documentation: Automatically generate documentation for complex automated processes, increasing both efficiency and accuracy from shift-handovers to compliance audits.
• Executive reporting: Prompt the system to generate case info in the right tone and level of information for a specific persona, such as for a non-technical executive or board member. 
• Team collaboration: Automatically alert Slack or Teams channels when a case is created, escalated, resolved and more.
• Resource optimization: Use AI to assign cases to an available analyst based on workload and shift schedules. 
• Data correlation: Combine and correlate data from all tools in your security stack to provide a holistic view of your security environment.
• Threat response: Automate tasks like threat detection and containment for faster incident resolution.

How Do AI SOCs Transform Traditional Security Operations? 

Scaling SOC operations: AI agents can handle an influx of security events: triaging, investigating, and remediating the majority of Tier-1 and Tier-2 alerts. This frees up analyst bandwidth to focus on urgent incidents and strategic projects, enabling SOCs to efficiently scale their operations without increasing headcount. Torq’s AI-powered Hyperautomation scales elastically, handling unlimited alert volumes without degradation. Carvana’s agentic AI now handles 100% of Tier-1 alerts, with no increase in headcount required.

Shifting to a proactive security posture: Agentic AI goes beyond just detecting and counteracting attacks by applying real-time intelligence to identify patterns and detect emerging threats. This allows SOCs to adopt a less reactive, more preemptive approach to address vulnerabilities before they can be exploited or breached. 

Reducing alert fatigue and analyst burnout: By autonomously triaging alerts and reducing false positives, AI agents reduce the number of irrelevant alerts that analysts must wade through. And by automating tedious, repetitive tasks and auto-remediating most low-level alerts, AI-driven Hyperautomation helps senior analysts regain time and capacity to focus on more rewarding work, such as strategic projects. 

Accelerating incident response: Manual investigation and remediation take hours; time attackers use to move laterally and escalate privileges. Socrates coordinates detection, enrichment, containment, and case management at machine speed, auto-remediating 95% of cases within minutes. Valvoline cut analyst workload by 7 hours per day after implementing Torq.

Speeding up MTTR: All of the efficiency gains from leveraging AI in the SOC translate to more alerts resolved, faster.

Will AI Replace Humans in the SOC?

Adopting AI in the SOC is not about replacing human SOC analysts — it’s about augmenting and empowering them. With a looming 4 million+ cybersecurity talent shortage, organizations must not only retain their existing analysts, but also help them work more efficiently. On top of that, organizations are recognizing that human-only defenses are inadequate to counter the evasive and persistent threats posed by AI-driven attacks.

AI reduces analyst burnout: A multi-agent system can reduce the strain on SOC teams by offloading rote tasks, auto-remediating the majority of Tier 1 tickets, and upleveling the skills of junior analysts. This frees up senior analysts to focus their expertise on critical threats and strategic projects, helping their organization achieve a stronger overall security posture.

Human expertise must remain the final line of defense: Done the right way, AI-powered SOCs keep humans “in the loop” as the ultimate decision-makers for high-stakes threats following rigorous, multi-tiered AI evaluation and case enrichment that helps human analysts take informed, decisive action.

“By 2028, multiagent AI in threat detection and incident response will rise from 5% to 70% of AI implementations to primarily augment — not replace — staff.” 

– Gartner Inc.

How Torq Delivers a True AI SOC

Torq isn’t AI bolted onto a legacy platform — it’s a true AI SOC built from the ground up. The Torq AI SOC Platform delivers all five core capabilities, combining agentic AI and automation to triage, investigate, and respond to threats with speed, scale, and transparency.

• Socrates, the OmniAgent AI SOC Analyst: Socrates intelligently automates alert triage, incident investigation, and response, extending your SOC teams’ capabilities and improving response times across the board. Socrates coordinates a full Multi-Agent System (MAS) — planning, investigating, remediating, and managing security cases with human-like decision-making and machine-speed execution. Socrates can auto-remediate 95% of cases within minutes. For critical cases that require human intervention, your analysts can collaborate with Socrates using natural language to summarize case details, enrich cases with additional investigation and threat intelligence, and trigger remediation workflows
• AI Workflow Builder: Simply describe your desired security automation workflow in natural language, and Torq’s AI Workflow Builder will generate a tailored solution in seconds. Rather than spending hours manually building workflows from scratch, your team is freed up to focus on more strategic security initiatives.
• AI Case Summaries: Help your team make the right decisions quickly by presenting them with a concise, insightful, and verifiable AI-generated summary of each case. No more wading through pages of logs and incident details! The easy-to-read summaries empower SOC teams to work faster, make informed decisions with confidence, and seamlessly transition between shifts by giving the incoming team clear case context backed by citations.
• AI Data Transformation: Simplify complex data manipulation for security operations by easily transforming complex JSON data using natural language — no coding required. Each transformation is broken down into precise, testable micro-transformations that users can edit, validate, and modify individually.
• Runbook Execution: Intelligently plan customized investigation and response strategies based on the organization’s historical outcomes and adapt to new threat vectors, ensuring faster containment.
• Deep Research Investigations: Uncover hidden attack patterns across disparate data sources, perform detailed root cause analyses, and dynamically assess threat impact — giving SOC teams context previously out of reach without hours of manual digging.
• Limitless Integrations: 300+ pre-built integrations with 4,000+ steps, plus AI-powered creation of new integrations and workflows.

Torq is the first autonomous security platform to support Model Context Protocol (MCP) natively — making it the most autonomous and truly agentic SecOps platform available.

The Future of the SOC

When deployed effectively, an AI SOC contains threats immediately while extending and enhancing your existing staff’s capabilities. This will become more critical than ever as attackers leverage AI to scale at machine speed.

So, what does the future of SOC automation look like? Sophisticated multi-agent AI continuously learns from historical data and real-time incidents to generate insights and recommendations, automate routine security tasks, and auto-remediate the majority of alerts, with a top layer of human analysts providing strategic oversight for critical cases. This means faster, more proactive responses to threats and vulnerabilities — and a more secure future for organizations everywhere.

Want to learn how to deploy AI in the SOC the right way? Read the AI or Die Manifesto to learn CISO considerations, fake AI red flags, and evaluation questions.

FAQs