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Traditional SOAR is usually the better fit for repeatable procedures with clear rules and bounded actions; AI security agents may help with investigations that require contextual, multistep work across tools. They are not necessarily alternatives: a SOC can retain deterministic playbooks, use agents to assemble and interpret evidence, and require human approval for consequential response actions. The right choice depends on the workflows, integrations, permissions, and oversight your team can validate.
How AI security agents differ from traditional SOAR
SOAR—security orchestration, automation, and response—connects security systems and runs workflows defined by people. The National Security Agency describes its automation and orchestration approach as replacing manual security tasks with policy-driven actions across the enterprise. NSA Automation and Orchestration Pillar.
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AI security agents can pursue a goal through multiple steps, use information gathered along the way, and adjust their next actions to context. Microsoft contrasts this with predefined SOAR playbooks and describes an agent loop of perceiving, reasoning, planning, acting, and learning. Those are useful distinctions between operating models, not a guarantee that every product labeled an agent behaves alike. Microsoft’s overview of agentic AI in cybersecurity.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →| Decision axis | Traditional SOAR | AI security agents | What to validate |
|---|---|---|---|
| Workflow choice | Runs predefined workflows and policy rules. | Can reason over context and plan multistep work. | Test the same representative incidents, including unfamiliar or changing cases. |
| Repeatability | Actions and decision paths can be explicitly specified. | Behavior can depend on context and agent decisions. | Require traceable decision and action records; test repeatability where deterministic handling matters. |
| Integration | Orchestrates connected security systems through workflows. | Can retrieve information or act across connected tools. | Verify the actual connectors, permissions, data quality, and failure paths in your environment. |
| Human control | People define workflows and policies in advance. | Oversight can range from review at each step to bounded autonomy. | Specify which actions need approval, particularly those with significant impact. |
| Governance | Requires workflow, policy, and integration governance. | Also raises questions about agent identity, delegated authority, prompt and tool risks, and unpredictable behavior. | Set identity, least-privilege permissions, authorization boundaries, audit, and rollback before expanding autonomy. |
| Upkeep | Procedures must stay aligned with workflows and integrations. | Needs evaluation and constraints as models, tools, and context change. | Track maintenance and failure modes separately; the sources do not establish which approach costs less to operate. |
When traditional SOAR fits
SOAR is a natural candidate when the SOC can define inputs, decision rules, and permitted actions ahead of time. Potential examples include routine alert enrichment, policy-controlled notification, and repeatable response steps with clearly defined safe conditions. NSA guidance frames automation as part of a broader architecture integrating security systems and policy-driven actions; it does not imply that every SOAR deployment has the same capabilities. NSA Automation and Orchestration Pillar.
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Before relying on a playbook, establish who owns it, how changes are reviewed and tested, which integrations it depends on, and how analysts handle exceptions. A well-defined process still needs a safe path for missing data, failed connectors, and cases outside its assumptions.
When AI security agents may help
Agents may be useful for investigations that cross systems or need contextual steps that are difficult to specify for every case in a static workflow. Google Cloud’s reference architecture describes querying alerts, enriching findings with threat intelligence, checking asset misconfigurations, retrieving endpoint telemetry, and incorporating human approval in a coordinated investigation. It is an example architecture, not evidence that every SOC will achieve the same result. Google Cloud’s security operations workflow architecture.
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Google also describes its security operations agents as gathering evidence, analyzing alerts, and returning an explained verdict, with connections to third-party tools through MCP. These are product statements; verify interoperability, data access, permissions, and error handling with the tools and environment you intend to use. Google Cloud Security’s agentic AI for security operations.
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How to choose: evaluate workflows, not labels
- Choose representative work. Include routine incidents with stable rules and investigations that require evidence from several systems. Test unfamiliar cases and incomplete or conflicting inputs as well as normal ones.
- Map the action boundary. For each step, identify what the system may read, recommend, or change. Distinguish enrichment and analysis from actions that alter endpoint, identity, or email state.
- Check the real integrations. Confirm connectors, data freshness and quality, permissions, and what happens when a source or connector is unavailable.
- Set an evaluation baseline. Compare the proposed workflow with your current process using measures relevant to your SOC, and inspect errors, exceptions, and decision records—not just successful runs.
- Start with assistance and expand carefully. Microsoft recommends lower-risk assistive use cases first, with greater autonomy as governance and operational maturity improve. Preserve human approval for consequential actions until testing supports a narrower boundary. Microsoft’s overview of agentic AI in cybersecurity.
Google reports “50% faster Mean Time to Respond (MTTR)” as an outcome associated with organizations adopting Google SecOps with AI agents. This is a Google vendor claim, not an independent agents-versus-SOAR benchmark: the cited page does not provide enough detail to establish the population, baseline, measurement design, or causal contribution of agents. Ask for methodology before using the figure in a purchasing comparison. Google Cloud Security’s agentic AI for security operations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What governance an autonomous agent requires
An agent that can call tools or change systems needs an identity and a strictly bounded authority—not just a prompt telling it to behave. NIST’s NCCoE says traditional identity and access management approaches may not fully address autonomous agents’ challenges. It identifies risks including data leaks, compliance failures, prompt injection, and unpredictable behavior. The project hub described a planned SP 1800-series practice guide; it should not be treated as a completed standard. NIST NCCoE’s Agentic AI Identity and Authorization Project Resource Hub.
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NIST’s March 2025 adversarial machine learning report provides a taxonomy of attack concepts, lifecycle stages, attacker goals, and mitigations. It is useful background for threat reviews, not a product certification or proof that a particular agent is secure; the report page notes it may be updated. NIST AI 100-2 E2025, Adversarial Machine Learning.
- What identity does each agent use, and can its permissions be limited to the task?
- Which data may it read, and which tools may it invoke?
- Can it change endpoint, identity, or email state, or only propose those changes?
- Which actions require a person’s approval, and who is authorized to approve them?
- What happens when sources disagree, a connector fails, or an input attempts to manipulate the agent?
- Can an analyst reconstruct the evidence, decision, and action afterward, and can the action be rolled back?
Microsoft describes approval workflows, role-based access controls, and auditing as guardrails for its approach. Those controls are useful design considerations; check how they work in the specific product and deployment you are evaluating. Microsoft’s overview of agentic AI in cybersecurity.
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Can a SOC use both?
Yes. A hybrid architecture is a sensible option when different work requires different levels of flexibility: use playbooks for established procedures with clear rules, agents to gather and synthesize context across tools, and human approval for sensitive response. Google’s investigation architecture illustrates cross-tool work with approval, while NSA describes policy-driven automation and Microsoft describes contextual, multistep agents. This combination is an architectural synthesis of those capabilities, not a tested claim that hybrid deployments always perform best.
Whether you keep SOAR, add agents, or combine them, the decision should rest on tested workflow fit, verified integrations, clear authority boundaries, and usable audit records. The available sources do not establish that agents universally outperform SOAR or should replace it.
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