How do organisations decide between SOAR playbooks and agentic AI automation?

Choosing the automation layer that matches the decision you want to delegate

Organisations usually get the SOAR versus agentic ai question wrong when they treat both as “faster automation” rather than as different decision models. SOAR is strongest when the response path is already defined, the trigger is known, and the team wants consistent execution with low ambiguity. Agentic AI becomes relevant when the environment is messy enough that the system must interpret context, gather evidence across tools, and then choose among plausible next steps. The practical issue is not speed alone, but whether the organisation can tolerate judgment inside the workflow. For a useful reference point on agentic risk, the OWASP OWASP Agentic AI Top 10 is helpful because it frames failure modes around autonomy, tool use, and control boundaries. In practice, many security teams discover the real distinction only after a workflow has already been over-automated or over-constrained.

How the two models differ when an incident has to be handled

SOAR playbooks work best when the organisation can pre-authorise a sequence such as enrich, triage, notify, contain, and close. The value comes from consistency: the same inputs should produce the same actions, and those actions should be explainable to operators, auditors, and incident responders. That makes SOAR a strong fit for routine containment, alert handling, and well-bounded case management.

Agentic AI changes the shape of the workflow. Instead of following a fixed branching path, the system may need to decide which source to query, which signals matter, and whether the evidence is strong enough to act. That can reduce manual investigation burden, but it also introduces a control problem: the more discretion the system has, the more you need guardrails around approval, tool scope, and rollback. NHI Management Group would treat this as a decision about authority, not just orchestration.

Useful way to separate the two:

• Use SOAR when the task is repeatable and the exception paths are limited.
• Use agentic AI when the task requires interpretation across several tools or datasets.
• Use SOAR when a human can predefine the safe next step.
• Use agentic AI when the system must infer which next step is appropriate, then hand off for review.

For governance and model-risk framing, the NIST NIST AI Risk Management Framework remains useful because it emphasizes mapping, measurement, and management of AI risk rather than assuming autonomy is inherently safe. Where the workflow touches adversarial manipulation of tools, prompts, or model outputs, the MITRE ATLAS adversarial AI threat matrix is a stronger complement than generic cyber guidance. The guidance breaks down when the organisation cannot define the tool permissions, approval boundaries, or failure conditions that make agentic action safe enough to trust.

Where the boundary gets blurry and governance has to decide

Tighter automation often increases operational predictability, but it can also slow response when analysts need flexible reasoning, so organisations have to balance consistency against adaptability. That trade-off becomes most visible in hybrid workflows, where SOAR handles the deterministic steps and agentic AI only assists with interpretation.

Edge cases appear when a process is partly structured and partly judgment-based. For example, a playbook may be deterministic for known alerts but still benefit from AI-assisted summarisation, prioritisation, or evidence gathering. That is usually a better first move than letting an agent take direct action. The industry has not fully standardised where to draw this line, but a practical rule is to keep the execution path deterministic wherever the action itself changes state, touches production access, or triggers external communications.

Organisations also need to distinguish between “recommendation” and “delegation.” An AI system that proposes next steps is easier to govern than one that can execute them. That distinction matters more as the environment becomes more sensitive, especially where privileged access, incident containment, or user impact is involved. The best designs often keep the agent in a advisory role until evidence thresholds and approval rules are satisfied.

One useful authority for this boundary is the CSA MAESTRO agentic AI threat modeling framework, because it is oriented toward the control and trust issues that emerge when an agent can call tools or chain actions. The question stops being simple if the organisation cannot tell whether the system is making a suggestion, selecting a path, or executing with real authority.

Risk and Threat Considerations

The main risk in agentic automation is not that it is “AI” but that it can combine tool access, contextual inference, and action selection in ways that are harder to predict than a fixed playbook. SOAR reduces this risk by constraining the response surface, while agentic AI expands it. That increases exposure to incorrect actions, prompt or tool abuse, and over-trust in machine-generated confidence.

Failure mechanism: A workflow becomes fragile when the system is allowed to interpret ambiguous inputs, choose tools, and act without enough human confirmation or scoped permissions. In that situation, an attacker, a bad input, or a model error can steer the agent into executing the wrong sequence, disclosing sensitive context, or taking an action that should have required review.

Impact: The practical consequence is loss of control over incident handling, privilege misuse, inaccurate containment, or unintended operational change. In the worst case, the automation itself becomes an avenue for escalation or defensive blind spots because teams assume the system is safer than it actually is.

Practitioner Guidance

What to prioritise: Classify workflows by decision rigidity before you classify them by technology. If the same input should always produce the same response, keep that path in SOAR. If the workflow needs contextual judgment, keep the agent’s authority narrow until you can prove the outputs are stable enough to trust.

Decision rule: Treat direct execution as the exception, not the default, whenever the action can affect production systems, privileged access, or external stakeholders. If a human would reasonably want to challenge the decision before it happens, the agent should not be the final actor.

What to verify: Confirm three things before trusting agentic automation: what tools it can reach, what approvals it still needs, and what it will do when evidence is incomplete. If any one of those is vague, the design is not ready for autonomous action.

Practitioner takeaway: The best organisations do not choose between SOAR and agentic AI as rivals; they assign each to the level of judgment it can safely carry, then preserve human authority wherever the consequence of being wrong is material.

Related resources from NHI Mgmt Group

• What is the difference between agentic AI governance and traditional automation governance?
• What is the difference between agentic AI and normal automation for IAM teams?
• What is the difference between AI automation and agentic AI from an identity perspective?
• What is the difference between agentic AI governance and traditional workflow automation?