Use them for different jobs. SOAR is best for deterministic, repeatable response actions, while AI is better for probabilistic analysis, context gathering, and guided investigation. A mature SOC keeps the separation clear so that response stays predictable even when analysis becomes more adaptive.
Why AI and SOAR solve different parts of SOC response
Teams should treat AI and SOAR as complementary control layers, not substitutes. SOAR works best where the response path is deterministic: enrich an alert, open a ticket, isolate an endpoint, disable an account, or notify a team in a known sequence. AI is more useful where the work is ambiguous, such as summarising evidence, clustering related alerts, drafting hypotheses, or helping an analyst decide what to investigate next.
The practical distinction is that SOAR encodes an approved decision path, while AI helps navigate uncertainty. That matters because response systems need consistency under pressure. If you use AI to improvise the response itself, the same alert can produce different actions depending on context quality, model output, or prompt framing.
For teams building out cybersecurity operations, the right question is not whether AI is “better” than SOAR, but which parts of the workflow need repeatability and which parts benefit from adaptive reasoning. Mature programmes often place AI upstream of SOAR so the model helps analysts understand the incident, then SOAR executes the approved playbook.
When AI should sit before automation, and when it should not
AI belongs before automation when the signal is noisy, the context is incomplete, or the analyst must merge multiple sources before a decision is safe. Examples include triaging related alerts, extracting entities from tickets and logs, or summarising a long investigation thread into a short case brief. That use case improves speed without handing execution authority to an unpredictable component.
SOAR should remain the execution layer when the action has a clear trigger, an approved control objective, and low tolerance for variation. If a condition is known and the response is policy-driven, automating the step avoids delay and keeps evidence, approvals, and response timing consistent. If the condition is still being interpreted, automation should wait until the interpretation is complete.
A useful operating model is to let AI recommend, but let SOAR act. The more an action affects containment, customer impact, or business continuity, the more important it is that the action path be explicit and testable. NIST AI Risk Management Framework is useful here because it reinforces governance, measurement, and oversight for AI-assisted decisions, rather than assuming the model itself is the control.
What changes when AI is used as a workflow layer instead of a response engine
Using AI as a workflow layer changes the failure mode. A SOAR playbook usually fails visibly, for example a connector breaks or a step times out. An AI-assisted workflow can fail more subtly by misclassifying intent, omitting context, overconfidence in a recommendation, or encouraging an operator to trust a fluent summary too quickly. That is why the safest pattern is to constrain AI to analysis, case preparation, and guided investigation.
This separation also helps with auditability. Security teams usually need to show why a containment action occurred, what evidence was available, and who approved it. A deterministic SOAR workflow is easier to log and reproduce than a model-driven action path. AI can still improve decision quality, but it should not blur the boundary between recommendation and execution.
If you are mapping this to operational controls, look for places where a human can validate the AI output before the response starts. That is especially useful in NIST SP 800-53 Rev. 5 style control environments, where auditability, access control, and incident handling all assume the organisation can explain and repeat its actions.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0, NIST SP 800-53 Rev 5 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OV-01 — Oversight of risk management strategy | AI-before-SOAR decisions affect governance over response design and accountability. |
| Recommendation — Define where AI may advise and where SOAR must execute under governed response policy. | ||
| NIST SP 800-53 Rev 5 | AU-6 — Audit Review, Analysis, and Reporting | AI-assisted response needs reviewable evidence of what happened and why. |
| IR-4 — Incident Handling | The question is about how incidents should be handled when combining AI analysis with automated response. | |
| AC-6 — Least Privilege | SOAR execution should be bounded to the minimum privileges needed for repeatable response. | |
| Recommendation — Log AI recommendations and SOAR actions so analysts can reconstruct incident decisions. Keep containment actions deterministic and preapproved within incident-handling playbooks. Restrict playbook permissions so automated response can only perform approved actions. | ||
| NIST AI RMF | GOVERN — Govern | AI used in SOC workflows requires governance over roles, accountability, and acceptable use. |
| Recommendation — Set governance rules that separate AI-assisted analysis from automated response authority. | ||
Practitioner Guidance
What to prioritise: Separate analysis from action. Use AI to reduce analyst workload and improve context quality, then pass only approved, deterministic steps into SOAR.
What to verify: Every automated response should have a clear trigger, a bounded action set, and a rollback or exception path. If the team cannot explain the exact condition that starts the playbook, the workflow is too loose.
Common mistake: Treating AI output as if it were a playbook. A model can recommend a containment path, but it should not be the authority that decides whether production systems are modified, accounts are disabled, or notifications are sent.
Decision rule: If the step must be repeatable under audit and pressure, keep it in SOAR. If the step requires interpretation, summarisation, or synthesis across messy evidence, let AI support the analyst first.
Practitioner takeaway: The best design is usually not AI versus SOAR, but AI for sense-making and SOAR for execution, because response quality improves when judgment and enforcement are kept separate.
Related resources from NHI Mgmt Group
- How should security teams validate changes to AI agent workflows before shipping them into production use?
- How should security teams govern AI-driven data discovery workflows that use MCP to change scanners and classifiers?
- How should security teams use AI-driven data transformation to keep SOC workflows reliable at scale?
- How should security teams use IAST and RASP in NHI governance?