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 sophisticated 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 use of AI in the SOC look like? Below, we show top use cases for leveraging AI in the SOC and explore how AI is transforming security operations.

The Technical Foundations of an AI-Powered SOC

Security automation has evolved way past SOAR — with Hyperautomation and AI Agents forming the new cornerstones of the modern autonomous SOC.

• 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 actions. Human analysts can interface with the AI agents using natural language for accelerated enrichment, investigation, and recommended next steps.

What’s the Difference? All the AI in the SOC, 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.

Top Use Cases for AI in the SOC

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 of the tools in your security stack, providing a holistic view of your security environment.
• Threat response: Automate tasks like threat detection and containment for faster incident resolution.

How Do AI-Powered 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 (which is vital amidst today’s shortage of skilled cybersecurity talent).

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 gain back the time and capacity to focus on more rewarding work like strategic projects. 

Speeding up MTTR: All of the efficiency gains from leveraging AI in the SOC translates 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..” 

Source: Gartner Inc.

How Torq’s AI Capabilities Supercharge SecOps

Torq has been very deliberate in how we’ve extended the capabilities of the Torq platform using AI to solve real problems for SOCs with products and features like:

• 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.

Torq now has multi-agent RAG (Retrieval-Augmented Generation) incorporated into HyperSOC™ which has supercharged its ability to do deep research, analyze threats, and coordinate responses at machine speed — and is the first autonomous security platform to support a Model-Context Protocol (MCP) natively in its architecture. These advancements make our latest HyperSOC release our most autonomous model to date and the first truly agentic SecOps platform. 

The Future of the SOC: Better, Faster Human Decision-Making Through AI Automation and Insights

When deployed effectively, AI in the SOC extends and enhances the capabilities of your existing staff so they can make better decisions faster. 

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.

Generative AI (GenAI) uses large language models (LLMs) to generate new content, synthesize data, and make context-aware decisions. In a cybersecurity organization, this means GenAI can help triage alerts, enrich threats, write playbooks, and assist analysts in real time.

But here’s the problem: Most SOCs are only scratching the surface of how generative AI can be used in cybersecurity. They’re using GenAI to summarize logs or generate scripts, which still requires human oversight and remains reactive. Did you know you could take your AI so much further? 

How Can Generative AI Be Used in Cybersecurity? 

GenAI is built on deep learning, a subset of machine learning, using large neural networks known as transformers. These transformers are trained on billions of data points and optimized to understand and mimic language, behavior, and structure.

Many GenAI systems are modeled after GPT (Generative Pretrained Transformer), which has learned how to respond to prompts in human-like ways by identifying patterns across massive data sets. 

Here are some ways Generative AI can be used in cybersecurity: 

• Triage and enrichment: GenAI can automatically triage incoming alerts, enrich data from SIEM, EDR, and other sources, and generate clear, concise summaries. 
• Threat detection: Trained on historical attack data and threat patterns, GenAI models can identify indicators of compromise (IOCs) and anticipate emerging tactics. 
• Case documentation: Generative AI can produce case reports, threat timelines, and case summaries. These auto-generated insights reduce the need for manual documentation and simplify compliance reporting.
• Threat hunting: Security analysts can use GenAI to query threat intelligence databases and data lakes using natural language prompts. This simplifies threat hunting workflows and empowers junior analysts to work at a higher level.
• Workflow design: GenAI allows users to describe desired automations in natural language, which the system then converts into executable workflows. This eliminates the need for manual scripting and accelerates automation adoption across teams.

All of this helps analysts move faster — but it’s still reactive. It still requires humans in the loop. And it’s still just the beginning.

Challenges of Using Generative AI Alone

Without the right guardrails, GenAI in cybersecurity comes with serious risks:

• Accuracy issues: GenAI can hallucinate. That means it can produce outputs that are confidently wrong. And in cybersecurity, that’s dangerous. An inaccurate summary of a threat, a misidentified IOC, or a fabricated correlation can derail an investigation, waste valuable analyst time, or worse, lead to improper remediation actions.
• Data privacy: GenAI models are only as good as the data they’re trained on and how that data is handled. Feeding sensitive logs, incident data, or user information into GenAI without proper controls can lead to unintentional exposure of private data or regulatory violations. This is especially risky in regulated industries (finance, healthcare, etc.), where compliance failures come with heavy penalties.
• Infrastructure overhead: Running LLMs requires serious computing, storage, and ongoing management. Even when using APIs from providers, the costs of integrating, fine-tuning, and securing the system can add up quickly. Without a well-architected platform, organizations often end up with fragile, expensive prototypes that don’t scale.
• Threat actor access: You’re not the only one using GenAI. Threat actors are, too. From auto-generating phishing emails and malware variants to simulating voices or bypassing MFA with deepfakes, adversaries are industrializing their attacks with the same tools defenders are exploring.
  

This is why GenAI alone doesn’t cut it. It enhances the SOC — but it doesn’t free it. It still relies on humans to verify outputs, make decisions, and connect the dots. To truly transform security operations, you need more than content generation — you need contextual understanding, autonomous action, and real-time orchestration.

That’s why Torq goes beyond GenAI — combining it with agentic AI, Hyperautomation, and RAG-powered microagents to deliver a self-sustaining, intelligent SOC.

IDC: GenAI is Just the Beginning 

A recent IDC Spotlight Report reinforces what leading cybersecurity teams already suspect: Generative AI is only the beginning. The real transformation happens with agentic AI.

Although only 7% of organizations are using agentic AI today, 60% expect it to impact their SOC operations within the next 18 months significantly. The benefits are tangible: organizations embracing this shift are seeing a 50% reduction in mean time to detect (MTTD), automated response for 90% of alerts, and a 35% lower risk of major breaches.

Torq’s HyperSOC platform, powered by agentic microagents, delivers exactly the kind of automation and intelligence IDC highlights — and it’s already available today.

Torq’s Take: From Generative AI to Autonomous Cybersecurity

At Torq, generative AI in cybersecurity was just the beginning. We combine GenAI with agentic AI, Hyperautomation, and Retrieval-Augmented Generation (RAG) to create what no one else in the market has: a truly autonomous SOC.

Agentic AI: Thinks Like a Human Analyst

Agentic AI is the brain behind the operation. Torq’s multi-agent system, led by Socrates, understands context, makes decisions, and learns from experience. How? 

• It uses semantic memory to understand relationships between threats, assets, and users.
• It applies episodic memory to recall past incidents and resolutions.
• It executes using procedural memory, adapting workflows in real-time based on its growing knowledge base.

Unlike GenAI, which waits for a prompt, agentic AI acts independently. It triages alerts, investigates root causes, and escalates only when necessary — moving SOCs from a human-in-the-loop to a human-on-the-loop approach. That means analysts step in only when they’re truly needed.

Hyperautomation: Machine-Speed Response Across Your Stack

Torq’s Hyperautomation engine seamlessly orchestrates your entire security ecosystem — across EDR, SIEM, IAM, email security, ticketing systems, cloud platforms, and beyond.

These AI-driven workflows:

• Automatically isolate compromised endpoints
• Revoke credentials and enforce MFA
• Trigger alert suppressions and log escalations
• Sync case updates across your tools of record
  

Hyperautomation connects all the dots — transforming detection into response with zero delay.

RAG-Enabled Microagents: Smarter Agents

Retrieval-augmented generation (RAG) enhances our specialized AI Agents with memory, precision, and real-time data access. Each microagent is trained on a specific domain, like investigation, remediation, or case management, and uses RAG to:

• Pull in relevant threat intel, logs, and past incidents
• Filter out noise and focus only on actionable data
• Generate concise, accurate case summaries and recommendations

Think of them as subject matter experts inside your AI-powered SOC — each one armed with a knowledge base that updates every second. 

Defending Against GenAI Security Threats

Adversaries are using GenAI too — for phishing, deepfakes, malware variants, and more. That’s why Torq’s defense stack includes:

• Behavioral detection: We spot the telltale signs of GenAI-generated attacks such as weird phrasing, impossible travel, or AI-crafted obfuscation. 
• Automated response: The second a threat is flagged, Torq acts. Endpoints isolated. Credentials locked. Sessions terminated. Tickets opened. Teams alerted. No hesitation. 
• Adaptive workflows: Attackers adapt. So do we… automatically. Torq’s workflows update themselves based on real-time threat intel, evolving tactics, and active defense insights. What was a one-off attack yesterday becomes a blocked pattern today.

Go Beyond GenAI Cybersecurity with Torq 

Generative AI in cybersecurity got us started — summarizing alerts, drafting playbooks, and answering questions. But Torq takes it further. 

With agentic AI, Torq’s platform went from suggestion to autonomous decision-making. With Hyperautomation, Torq executes those decisions instantly across your entire stack. And with RAG-enabled microagents, every move is precise, contextual, and based on real-time intelligence. 

That’s how you build a truly autonomous SOC.

Want to go beyond GenAI cybersecurity? Get the AI or Die Manifesto.