SOAR Playbooks vs. Agentic AI: What Triggers the Response?

Two security systems can isolate the same endpoint automatically while reaching that decision in very different ways. 

A SOAR playbook may execute containment when predefined conditions are met. While an AI agent may examine the evidence, determine that isolation is necessary, and select the action itself.

Both can automate incident response, but understanding how each makes decisions matters when you’re deciding what to trust with production access.

SOAR Playbooks vs. Agentic AI: What’s the Difference?

• Security orchestration, automation and response (SOAR) platforms connect security tools and automate workflows such as alert enrichment, case management, and containment.
• Agentic AI introduces model-driven decision-making into those workflows, allowing a system to choose investigative steps or tools based on the information it receives. 

The two approaches can work together. An AI agent can operate within a SOAR platform, and a SOAR playbook can execute actions recommended by an agent. 

How SOAR Playbooks Work

A conventional SOAR playbook follows predefined conditions and actions.

For example, a playbook might receive an endpoint alert, check its severity, confirm the asset is eligible for automatic containment, and isolate it if the approved conditions are met. 

Security teams configure these rules in advance, including exceptions, approval requirements, and what happens when an integration fails. 

This makes SOAR well suited to repeatable tasks where the response criteria are clear.

How Agentic AI Works

An AI agent can interpret evidence and select its next investigative step from the tools available to it.

For example, an agent investigating suspicious PowerShell activity might retrieve endpoint logs, examine related identity events, and investigate a second host after identifying a possible connection.

The sequence can change as new evidence becomes available. That flexibility can help with investigations where the next step depends on what has already been discovered.

However, the agent’s permissions determine the operational risk. An agent restricted to reading evidence has different responsibilities from one authorised to disable accounts or isolate endpoints. 

How the Same Containment Action Works in Three Different Systems

Consider a SOC investigating suspicious activity on an employee’s workstation. The investigation produces enough evidence to consider isolating the device. 

Three different workflows could reach that outcome.

1. A SOAR Playbook Executes a Pre-Approved Response

The alert triggers a playbook that checks the organisation’s containment criteria. It confirms that the workstation belongs to an eligible asset group, verifies the required threat indicators, and calls the isolation API.

The system then checks whether isolation succeeded.

In this workflow, people have already authorised the circumstances under which containment can occur. The playbook executes that policy when its conditions are satisfied.

The important tests concern the accuracy of those conditions, the data supporting them, and how the workflow handles exceptions.

For example, what happens if the asset inventory is outdated or the endpoint is incorrectly classified?

2. An AI Agent Recommends Isolation for Human Approval

An agent investigates the alert using approved tools and recommends isolating the workstation. It presents the supporting evidence, identifies the target, and explains the proposed action.

An incident handler reviews the recommendation and approves or rejects it. A separate execution service then checks the authorisation before isolating the device. 

Here, AI helps determine what action may be appropriate, while the responder retains authority over containment. 

Testing should establish whether the recommendation is supported by evidence, whether analysts can identify errors, and whether the system respects the approved scope.

3. An AI Agent Selects and Executes Isolation

In the third workflow, the agent investigates the alert, decides isolation is necessary, and calls the response tool without seeking fresh human approval.

Its permissions may still restrict which assets it can isolate or which tools it can access. However, within those limits, the model’s interpretation determines whether containment proceeds.

This introduces additional risk because an incorrect conclusion can directly affect production systems.

Teams need to test how the agent handles ambiguous evidence, incorrect asset information, and situations where containment could interrupt legitimate business activity.

The final endpoint state may be identical across all three workflows. The difference is how the action was selected, authorised, and executed. 

Where Should You Use SOAR Playbooks or Agentic AI?

The choice depends on the work you’re automating and how much flexibility the workflow requires.

Use SOAR for Predictable, Repeatable Tasks

Playbooks work well when your team can define the conditions and expected response in advance.

Examples include:

• Enriching alerts with threat intelligence or asset information.
• Creating cases and routing them to the appropriate queue.
• Blocking known indicators under approved policies.
• Executing predefined containment actions on eligible assets.

These workflows benefit from consistency and clear execution rules.

The priority is to ensure the conditions are reliable, exceptions are handled correctly, and the automation stays within its authorised scope.

Use Agentic AI for Flexible Investigations

Agentic AI can be useful where investigations involve several possible paths or require information from multiple tools.

An agent might examine endpoint activity, retrieve identity logs, and identify related events before preparing a summary or recommending further investigation.

This can reduce the manual work involved in gathering and connecting evidence.

Combine Both Where Appropriate

A hybrid workflow can bring together flexible investigation and controlled execution.

For example, an agent could gather evidence and recommend containment. An analyst would approve the recommendation, and a SOAR playbook would carry out the approved action.

This gives teams a way to use AI-generated analysis while retaining established controls around production changes.

What to Evaluate Before Deploying Either Approach

A vendor demonstration should show how the system reaches a decision, what permissions it uses, and what happens when something goes wrong.

Ask the vendor to walk through a complete response, from the original alert to the confirmed outcome.

1. Trace What Triggered the Action

Identify the component responsible for selecting the response.

• If a playbook acts on an AI-generated severity score, ask how that score is calculated and what evidence supports it.

For an agent, examine which information it retrieved, why it selected a particular tool, and what alternatives were available. 

The audit trail should preserve the relevant evidence and actual tool operations so your team can reconstruct the decision. 

2. Check How Permissions Are Enforced

Ask the vendor to demonstrate an action outside the system's permitted scope.

• For example, change an eligible workstation to a critical server that is excluded from automatic isolation.

The system should block that request through an application-level control rather than relying on the model to recognise that the action is prohibited. 

Check that permissions are limited to the required operations and that approval remains tied to the specific action and target. 

3. Test Failures and Unexpected Conditions

Successful demonstrations reveal only part of how a system behaves.

Include cases involving:

• Missing evidence: A required log source or enrichment service is unavailable.
• Conflicting information: Two sources suggest different conclusions.
• Incorrect targets: An asset is misidentified or belongs to an excluded group.
• Untrusted content: Retrieved material contains instructions intended to redirect the agent.
• Execution failures: A response API accepts a request but does not complete the action.

For agentic workflows, repeat important tests to see whether decisions remain consistent.

OWASP explains how attacker-controlled content can influence a model’s behaviour. The execution layer should prevent that content from granting additional authority. 

4. Verify the Outcome

A successful API call does not necessarily mean the incident response succeeded. For endpoint isolation, confirm that the intended device was isolated and that the action did not affect an unauthorised target. 

The investigation may also need to establish whether other assets were compromised or whether the attacker retained access elsewhere.

Measure the full workflow using indicators such as:

• Correct execution and containment outcomes.
• Unnecessary or incorrect response actions.
• Time required to reach an authorised decision.
• Analyst effort needed to investigate and verify results.

These measures help establish whether automation improves your SOC’s performance without introducing unacceptable operational risk. 

Build a Response Process You Can Trust

SOAR playbooks and agentic AI can both help your SOC move faster. The right approach depends on the decisions you want to automate, the evidence available, and the consequences of an incorrect action. 

Before expanding either system, make sure your team can explain what triggered the response, who authorised it, and whether it achieved the intended result.

Those are also questions worth testing during a realistic incident.

TryHackMe gives your defenders an environment to practise investigations, containment, and coordination while an incident unfolds. 

You can see how your team handles critical decisions, identify where response slows down, and use performance reporting to guide improvements.