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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Traditional SOAR is usually the better fit for security procedures that can be specified in advance; AI security agents may help when investigations require contextual, multistep work across tools. Neither is a universal replacement for the other. A SOC should choose based on the workflows it needs to automate, the evidence and integrations available, and how it will constrain and audit software actions. For many teams, a hybrid design is a reasonable option: use playbooks for established procedures, agents to assist with complex investigations, and human approval for sensitive response.
How do AI security agents differ from traditional SOAR?
Traditional Security Orchestration, Automation and Response (SOAR) connects security systems and runs defined, policy-driven workflows. The National Security Agency describes this approach as using automated actions across the enterprise to replace manual security tasks. See the NSA Automation and Orchestration Pillar.
An AI security agent can pursue a goal through a series of steps, using context gathered along the way to adjust what it does next. Microsoft contrasts this with predefined SOAR playbooks and describes an agent loop of perceiving information, reasoning, planning, acting and learning. That is a description of Microsoft’s approach, not a guarantee that every product marketed as an agent behaves the same way. See Microsoft’s overview of agentic AI in cybersecurity.
The distinction is about how work is selected and carried out: a playbook follows a specified path, while an agent may choose and sequence actions in response to the case. Agents still depend on configured tools, data access and authorization; they are not automatically able to use every system in a SOC.
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Which approach fits which SOC workflow?
| Decision area | Traditional SOAR | AI security agents | What to evaluate |
|---|---|---|---|
| Workflow choice | Runs predefined processes and rules. | Can reason over context and plan multistep work. | Test representative routine incidents as well as unfamiliar or changing cases. |
| Repeatability | Actions and decision conditions are specified in the workflow. | Behavior can vary with case context and agent decisions. | Require traceable decision and action records; test repeatability where deterministic behavior matters. |
| Tool integration | Orchestrates connected security systems through configured workflows. | Can retrieve information from or act through connected tools, subject to its access. | Verify connectors, permissions, data quality and failure handling in your environment. |
| Human control | Control is expressed through workflow design and policy. | Oversight can range from review at each step to bounded autonomy. | Set approval requirements for high-impact actions before deployment. |
| Governance | Requires ownership and change control for workflows and policies. | Adds questions about agent identity, delegated authority, prompt and tool risks, and unpredictable behavior. | Define least-privilege access, authorization boundaries, audit and rollback. |
| Upkeep | Procedures and integrations need maintenance as systems change. | Requires evaluation and constraints as tools and models change. | Track each approach’s maintenance and failure modes; available sources do not establish which is less costly. |
SOAR for established procedures
SOAR is a natural fit when a procedure’s inputs, decision rules and permitted actions can be defined ahead of time. Examples include routine alert enrichment, policy-controlled notifications and repeatable response steps with clear safe conditions. The NSA frames orchestration as part of a broader automation architecture integrated with SIEM, rather than as a guarantee that every workflow can be fully automated.
Fit depends on the deployment. Review connector coverage, who owns each workflow, how changes are tested and approved, and what operators should do when a case falls outside the defined path.
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Agents for contextual investigations
Agents may be useful when an investigation requires several steps across tools or when it is difficult to write a static workflow that covers the relevant cases. Google’s reference architecture describes coordinating an investigation that queries alerts, adds threat-intelligence context, checks cloud security posture, retrieves endpoint telemetry and requests human approval. See Google Cloud’s security operations workflow architecture.
This illustrates a possible design, not a promise of interoperability or results in your SOC. Validate the actual products, connectors, permissions, data and exception cases your team uses.
Can agents replace SOAR, or should a SOC use both?
The available evidence does not show that agents universally outperform or replace SOAR. A combined design can make sense when a SOC has both repeatable procedures and investigations that depend on context. Keep deterministic playbooks for bounded actions with clear rules; use agents to assist with evidence gathering and synthesis across tools; require human approval where an action could materially affect endpoints, identities, email or business operations.
This is an architectural option, not a proven best design for every organization. The right division depends on which tasks can be safely specified, what the agent can access and whether analysts can verify its work.
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How should a SOC introduce agents safely?
Microsoft recommends beginning with lower-risk, assistive use cases and increasing autonomy as governance and operational maturity improve. A staged rollout gives a team a chance to test behavior and controls before allowing an agent to take consequential actions.
- Choose a bounded, low-risk task. Start with assistance such as gathering and summarizing investigation evidence, rather than autonomous containment or account changes.
- Test representative cases. Compare agent-assisted work with current workflows, including exceptions, conflicting evidence and unfamiliar cases. Record where the agent’s reasoning or proposed actions need correction.
- Limit access and require review. Specify which data the agent can read and which tools it can invoke. Keep approval gates on consequential actions until the team has evidence that behavior stays within intended boundaries.
- Audit and handle failures. Ensure analysts can reconstruct the evidence, decisions and actions. Define what happens when a connector fails, sources disagree or an input tries to manipulate the agent.
- Expand only when controls hold up. Increase autonomy in steps, with clear owners for permissions, workflow changes, exceptions and rollback.
Microsoft describes approval workflows, role-based access controls and auditing as guardrails. These controls need to be verified in the specific deployment rather than assumed from a product description.
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What governance risks need particular attention?
NIST’s NCCoE says autonomous agents create challenges that traditional identity and access management approaches may not fully address. Its Agentic AI Identity and Authorization Project Resource Hub identifies risks including data leaks, compliance failures, prompt injection and unpredictable behavior. The hub describes a February 2026 concept-paper project timeline and a planned SP 1800-series practice guide; it is not a completed standard.
NIST’s AI 100-2 E2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, published in March 2025, provides background on attack concepts, attacker goals and mitigation approaches. It is not a certification of any SOC agent or product, and NIST’s page notes the report may be updated.
Before expanding an agent’s autonomy, answer these operational questions:
- What identity does each agent use, and can its actions be attributed to that identity?
- Which data can it read, and which tools can it call? Can it alter endpoint, identity or email state?
- Which actions require approval, and who is authorized to provide it?
- How are conflicting sources, unavailable connectors and manipulated inputs handled?
- Can an analyst reconstruct the evidence considered, the decision made and the action taken?
How should SOCs assess performance claims?
Google Cloud’s Agentic AI for Security Operations page reports “50% faster Mean Time to Respond (MTTR)” as an outcome associated with organizations adopting Google SecOps with AI agents. This is a vendor claim, not an independent comparison of agents with SOAR. The page does not provide enough detail to establish the population, baseline, measurement design or causal contribution of agents, so a SOC should request the methodology before using the figure in a purchasing comparison. It should not treat the result as an expected outcome for its own environment.
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Evaluate candidate systems against current workflows using cases, data sources, permissions and exception paths representative of your SOC. Measure outcomes that matter to the team, and distinguish improvements attributable to the tool from changes in process or staffing.
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