Should security teams compare agentic SecOps with traditional SOAR or treat them as separate control models?

Why agentic SecOps is not just SOAR with a new label

SOAR and agentic SecOps both automate security work, but they do not control the same way. SOAR is built around predefined playbooks, deterministic branching, and operator-approved orchestration. Agentic SecOps adds runtime reasoning, tool selection, and adaptive action, so the security question shifts from “did the workflow run?” to “was the agent’s authority, context, and output properly bounded at execution time?”

That difference matters because the control surface expands from workflow correctness to decision accountability. A traditional automation step may fail safely when a rule is wrong; an agentic step may choose the wrong tool, overreach on scope, or chain actions in a way the original designer did not enumerate. In practice, that means the control model has to account for identity, policy, and observation of the agent’s decisions, not only the playbook logic.

SOAR is still valuable where repeatability is the goal, especially for enrichment, ticketing, containment, and other tasks that benefit from stable decision trees. Agentic SecOps is more useful where the environment is ambiguous, the evidence is incomplete, or the next action depends on interpreting context across multiple tools. Those are different operating assumptions, so treating them as separate models avoids forcing one governance pattern onto the other.

Where the control boundary changes

The practical boundary is whether the system is executing a known sequence or selecting among possible actions at runtime. In SOAR, the organisation usually governs the workflow designer, the automation account, and the approval path. In agentic SecOps, the organisation must also govern the agent’s delegated authority, the tools it may invoke, the conditions under which it may escalate, and the evidence trail for each action it takes.

This is why identity and authorisation become materially more important once the system can decide. If the agent can query, remediate, or isolate resources on its own, then the control model has to answer who the agent is acting for, what it is allowed to touch, and when human approval is required. The relevant distinction is not branding, it is whether the runtime can create new consequences without a precompiled branch in a playbook.

That is also why comparisons should be framed at the control level rather than the tooling level. Two platforms may both trigger tickets and call APIs, yet one remains deterministic orchestration while the other behaves like a bounded decision-maker. For practitioners, the useful comparison is not “Which product has more automation?” but “Which model gives us acceptable control over authority, reversibility, and auditability?”

How practitioners should evaluate the two models

Compare them by asking what must be proven before you trust the action. With SOAR, you verify the workflow design, integration reliability, and approval logic. With agentic SecOps, you also need to verify delegated scope, tool permissions, action attribution, and whether the agent can be stopped or constrained before an unsafe action completes. That is a stronger governance requirement, not just a broader feature set.

For that reason, agentic SecOps should usually be introduced where the team can tolerate bounded autonomy and can measure it. If you cannot clearly define allowed tools, revocation conditions, and accountability for each action, the system is not ready to behave like an agent. If you can define those constraints, then the model can reduce analyst load without turning automation into an uncontrolled side channel.

For a useful comparison lens, NHI and agent governance discussions such as AI Agent Authorisation Guide, AI Agent Observability, Audit and Incident Response Guide, and Zero Trust for AI Agents are directly relevant because they map the control problem to authority, observability, and privilege boundaries.

Risk and Threat Considerations

Agentic SecOps increases exposure if teams assume SOAR-style determinism still applies. The main risk is excessive or mis-scoped authority, where an agent can take actions that were never intended for unsupervised execution, especially when it can chain tools or respond to ambiguous evidence. That creates both operational risk and a larger attack surface if an attacker can influence the agent’s reasoning or inputs.

Failure mechanism: The system grants runtime discretion without equally strong limits on tool scope, approval thresholds, logging, and rollback, so a bad recommendation becomes a real action instead of a contained suggestion.

Impact: The result can be mistaken containment, over-collection of data, disruptive remediation, or a compromised decision trail that makes incident review and accountability much harder.

Practitioner Guidance

What to prioritise: Decide first whether the use case needs deterministic execution or bounded autonomy. If the desired outcome is repeatable remediation with low ambiguity, SOAR remains the cleaner model; if the work depends on interpretation, then treat the agent as a delegated actor and govern it accordingly.

What to verify: Confirm that every action the agent can take is traceable to an explicit authority boundary, not just to a prompt or workflow step. You should be able to answer what it can do, on whose behalf, with which tools, and under what stop condition.

Practitioner takeaway: Do not compare the two as if one were simply a smarter version of the other. The real decision is whether you want predefined automation or runtime decision-making, because that choice changes the required controls around authority, auditability, and blast radius.

Related resources from NHI Mgmt Group

• How should teams compare agentic security tools before using them in production?
• How should security teams implement an AI control plane for agentic workloads across multiple tools and models?
• How should security teams govern agentic AI environments when traditional posture tools only cover applications or models?
• What breaks when security teams treat untrusted input and sensitive data as separate risk categories in agentic systems?