AI SOC Agents in Gartner Hype Cycle for Security Operations

According to Gartner’s 2025 Hype Cycle for Security Operations (download your complementary copy here), AI SOC Agents now appear as an emerging category that promises measurable gains in throughput and speed for core SOC workflows when deployed with pilots, guardrails, and clear success criteria. This recognition signals that agentic AI in Security Operations is entering real evaluations across enterprises that prioritize coverage, speed, accuracy, explainability, and cost.

What is included in the Gartner Hype Cycle for Security Operations 2025?

Gartner includes AI SOC Agents as a new market entry with early adoption, moderate benefit rating, and a focus on augmenting human analysts across common SOC activities with deployments expected to start as controlled pilots tied to workflow outcomes rather than tool counts. 

The report also highlights rapid maturation across adjacent areas like exposure assessment platforms, the rise of CIRM for incident management at scale, and the role of standards such as OCSF and telemetry pipelines in making AI assistance more reliable and economical to operate in the SOC. Security leaders are advised to baseline current operations, run vendor-neutral pilots, and evaluate AI features embedded in incumbent SIEM or XDR before adding new standalone systems.

What are AI SOC Agents?

AI SOC Agents are agentic AI systems embedded in security operations to assist analysts through natural language investigation, event triage automation, alert enrichment, attack path context, reporting summarization, and next step guidance, with the intent to improve throughput without removing human control over critical actions. 

Gartner positions them as augmentation tools that help teams auto investigate noisy alerts while preserving human attention for high impact incidents, threat hunting, and response.

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When should you consider AI SOC Agents?

Gartner highlights three common drivers behind the growing interest in AI SOC agents. If your security team lacks the resources to investigate every alert, AI agents can help reduce the burden by automatically handling lower-priority investigations, allowing analysts to focus on higher-risk threats. If hiring, training, and retaining SOC talent is a challenge, offloading repetitive tasks to AI can free up junior analysts to take on more valuable work, which often leads to stronger engagement and retention. And if your team is under pressure to improve coverage without expanding headcount, AI SOC agents may offer a way to extend capacity without compromising outcomes.

What are the benefits of AI SOC Agents?

According to Gartner, AI SOC agents can help teams manage time-consuming tasks that slow down operations. That includes handling false positives, enriching alerts, summarizing findings, generating timelines, and enabling natural language queries. These capabilities can reduce analyst fatigue and improve consistency.

Gartner also notes that AI agents can increase overall capacity by assisting with routine tasks, giving teams room to take on more work without adding headcount. For junior analysts, this support can lower the learning curve by simplifying complex processes and making it easier to contribute earlier in their role.

What are the obstacles for AI SOC Agents?

Gartner notes that AI SOC agent tools are still early in their maturity, and many of the promised benefits have yet to be fully validated in real-world environments. Security leaders should evaluate these tools carefully, looking for evidence of real workflow improvements and watching for signs of AI washing.

Licensing models are another consideration. Some vendors tie pricing to specific SOC activities, which can make it harder to deploy AI agents broadly across the team. For smaller teams in particular, justifying the cost may be challenging unless the tool can clearly demonstrate improvements over existing workflows.

What Gartner recommends

Before exploring AI SOC agent tools, Gartner recommends first establishing a clear baseline of your current operations. Understanding which tasks consume the most time or cause the most friction can help shape your evaluation criteria and support any cost justification efforts.

Starting with a pilot is also advised. Focusing on well-defined use cases like alert triage or false-positive reduction can help assess whether the technology delivers meaningful value and fits within your existing workflows.

Gartner also recommends checking with existing vendors like your SIEM or XDR provider, especially if your team relies heavily on platforms such as CrowdStrike or Palo Alto Networks. While some are beginning to add agent-like features, these capabilities are early-stage and often limited to their own ecosystems. For teams that need deeper investigations and broader coverage, dedicated AI SOC agents offer a more practical option today.

Evaluation checklist for AI SOC Agents

• Explainability and transparency: Does every recommendation and finding include explainable evidence and transparent reasoning that a senior analyst can validate quickly?
• Depth, quality, and accuracy: How often does the AI SOC agent accurately analyze and interpret the signals from various data sources and come to a correct determinination?
• Privacy posture: Is enterprise data isolated, is model training opt out by default, and are prompts and outputs retained with proper access controls and retention policies?
• Data handling: Can the agent operate on governed telemetry selections, with PII minimization, and with export control aligned storage locations?
• Integration depth: Are there native connectors to SIEM, SOAR, XDR, EDR, identity, EAP, AEV, and CIRM, with support for OCSF and telemetry pipelines to control cost and schema drift?
• Feedback loop quality: Can analysts rate outputs, correct errors, and push improvements into investigation outcomes or playbooks with audit trails?
• Failure modes: How does the agent behave on low confidence tasks, ambiguous signals, or sparse context, and does it default to safe handoff?
• Cost model clarity: Is pricing by agent, investigation, or compute predictable, and can the vendor quantify savings against MTTR or ingestion reductions?
• Human-in-the-loop controls: Can teams enforce approval steps for containment, identity, and control changes with exception logging?
• Detection engineering feedback: Can detection engineers submit rules and receive triage results, and can triage outcomes feed back into detection tuning or suppression logic?
• Response integration: Can the agent initiate or recommend response actions through existing SOAR or ITSM workflows, with appropriate gating and auditability?
• Role-based control: Can permissions and visibility be scoped by function (analyst, detection engineer, manager), with enforcement across investigation and response workflows?
• Context awareness: Can the agent incorporate organization-specific context such as known benign indicators, asset inventories, or suppression lists?
• Escalation support: Can the agent assign investigations to human analysts with all supporting evidence, rationale, and decision points preserved?
• Model control: Can customers bring their own model or swap in a preferred LLM, with control over updates, fine-tuning, and model selection?

For security teams actively exploring AI SOC Agents or planning pilot programs, it’s worth seeing how these capabilities operate in a real-world environment. Prophet Security delivers an agentic AI SOC platform that automates the repetitive and manual processes involved in investigating and responding to security threats.

Request a demo to see how it works in your environment.

Frequently Asked Questions (FAQ)

What is an AI SOC Agent?

An AI SOC Agent is a reasoning-based AI system embedded in security operations to assist with tasks like triage, investigation, alert enrichment, summarization, and next-step guidance. It operates at the analyst level to augment human decision-making, not replace it.

Why did Gartner include AI SOC Agents in the 2025 Hype Cycle for Security Operations?

Gartner recognized AI SOC Agents as a new category due to their early adoption and ability to improve investigation throughput, consistency, and speed. Their inclusion reflects increasing enterprise interest in agentic AI for SecOps.

How are AI SOC Agents different from embedded AI features in SIEM or XDR platforms?

Unlike embedded features that are limited to vendor ecosystems, AI SOC Agents are standalone systems that reason across data sources, suggest next steps, and guide investigations—making them more flexible and capable across tools.

What SOC challenges do AI SOC Agents help solve?

AI SOC Agents help reduce alert fatigue, automate investigation of low-risk alerts, improve response time, enable faster triage, and lower the burden on overworked analysts. They also help junior analysts ramp faster.

What are the benefits of using AI SOC Agents in security operations?

Benefits include increased analyst capacity, reduced mean time to investigate (MTTI), consistent investigation quality, natural language interaction, and faster coverage of noisy or repetitive alerts.

When should a SOC team consider piloting AI SOC Agents?

A team should consider a pilot when struggling with high alert volume, limited headcount, slow triage speed, or burnout. Gartner recommends tying pilots to measurable workflow improvements instead of treating it as a feature checklist.

Do AI SOC Agents replace human analysts?

No. AI SOC Agents are designed to augment analysts by handling high-volume, repetitive tasks while preserving human oversight for critical decisions like containment, escalation, and threat response.

What criteria should security leaders use to evaluate AI SOC Agent platforms?

Key evaluation criteria include explainability, integration depth, model control, feedback loop quality, pricing transparency, failure modes, and human-in-the-loop support.

How do AI SOC Agents support detection engineering workflows?

AI SOC Agents close the feedback loop by surfacing triage outcomes, enabling rules to be tuned based on real-world investigation results. This improves detection accuracy and reduces false positives.

Can AI SOC Agents operate safely on low-confidence or ambiguous alerts?

Yes. Leading agents are designed to default to safe handoff when confidence is low or signals are ambiguous, preserving context for human analysts to take over.

What role does explainability play in AI SOC Agent adoption?

Explainability is critical. Analysts need to see evidence, provenance, and reasoning behind the AI's conclusions. Gartner recommends only evaluating tools that make decisions auditable and transparent.

What is the difference between SOAR, SIEM, and AI SOC Agents?

SIEMs collect and correlate data, SOARs orchestrate response actions, and AI SOC Agents reason through alerts and evidence. They complement but do not replace one another.

Do AI SOC Agents require you to bring your own LLM or model?

Some platforms support bring-your-own-model (BYOM) for customization and control, but most offer managed LLMs with enterprise governance and opt-out model training policies.

How do AI SOC Agents improve analyst onboarding and retention?

By reducing manual workload and guiding investigations through natural language, AI SOC Agents help junior analysts become productive faster and reduce burnout risk for the whole team.

What should be included in a pilot for evaluating AI SOC Agents?

A good pilot includes clearly scoped use cases (like triage or false-positive reduction), baseline metrics, and success criteria tied to workflow outcomes, not generic tool capabilities.