Teams should look for fewer owned scripts, fewer exception paths, and faster recovery from integration failures. If AI increases the rate of playbook creation without reducing the number of workflows that need human maintenance, it is only accelerating complexity rather than removing it.
Why This Matters for Security Teams
AI only reduces SOAR complexity when it removes operational friction, not when it generates more orchestration surface area. Security teams should measure whether a workflow still needs constant human edits, brittle exception handling, or manual recovery after connector failures. NIST’s NIST Cybersecurity Framework 2.0 is useful here because it pushes teams to assess whether controls are improving resilience, not just activity volume.
The same logic appears in NHIMG research on the state of secrets in AppSec: fragmentation, slow remediation, and overconfidence tend to hide real complexity until failures cascade. In SOAR environments, AI can create the illusion of simplification by drafting more playbooks, but if every new automation adds another approval branch or connector dependency, the operating burden rises. Practitioners should look for fewer owned scripts, fewer handoffs, and shorter recovery paths after change. In practice, many security teams discover that AI has increased alert throughput long before it has reduced the number of workflows that still require human maintenance.
How It Works in Practice
The clearest way to tell whether AI is reducing SOAR complexity is to trace the full lifecycle of a workflow, from trigger to recovery. If AI is genuinely helping, it should reduce the number of custom scripts, lower the dependency count per playbook, and shrink the set of exceptions that require manual intervention. Current guidance suggests evaluating both operational load and control quality, rather than counting only how many automations were created.
Teams often get better signal by comparing before-and-after metrics across a few stable categories:
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Owned scripts: count the number of playbooks that require custom code, not just generated steps.
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Exception paths: measure how often analysts must override or branch around AI output.
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Recovery time: track how quickly a failed integration or malformed action can be rolled back.
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Maintenance burden: separate routine tuning from emergency fixes caused by drift or hallucinated actions.
For governance, map these metrics to evidence in the NIST Cybersecurity Framework 2.0 and compare them with the threat patterns described in NHIMG’s LLMjacking research, where compromised identities and exposed credentials turn automation into an attack path. If AI is being used to generate playbooks, the key question is whether the generated output stays bounded by policy, or whether it expands the number of moving parts that still need expert supervision. These controls tend to break down in highly integrated SOC environments because one failed upstream API or token expiry can force analysts back into manual triage across multiple dependent workflows.
Common Variations and Edge Cases
Tighter automation often increases governance overhead at first, so organisations have to balance speed gains against the cost of validating each new workflow. Best practice is evolving, and there is no universal standard for how much SOAR complexity AI should remove before it is considered successful.
One common edge case is AI that improves first-line triage but leaves the deeper remediation path untouched. In that model, the front end looks faster while the back end remains as brittle as before. Another case is environment-specific complexity: regulated sectors may accept more review gates, but those gates should still be justified by risk rather than inherited from old playbooks. The DeepSeek breach is a useful reminder that exposed credentials and poorly bounded systems can turn automation into exposure, not efficiency. Security teams should also watch for vendor claims that conflate “more generated content” with “less complexity”; those are not the same outcome.
Where AI genuinely helps, it should make the SOAR estate easier to operate during outages, upgrades, and staff turnover. If the team still needs specialised knowledge to keep every automated branch alive, the complexity has been relocated, not reduced.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-05 | SOAR AI depends on non-human identities and secret handling across automations. |
| OWASP Agentic AI Top 10 | A-03 | AI-generated playbooks can expand autonomous action paths and hidden dependencies. |
| CSA MAESTRO | MAESTRO-2 | MAESTRO addresses governance for agentic workflows and control complexity. |
| NIST AI RMF | AI RMF helps assess whether AI is improving resilience or adding operational risk. | |
| NIST CSF 2.0 | RC.RP-1 | Recovery time is a direct signal of whether AI reduced workflow fragility. |
Inventory every SOAR identity and enforce rotation, least privilege, and secret isolation for each automation.
Related resources from NHI Mgmt Group
- How can teams tell whether AI is improving security or just adding complexity?
- How can teams tell whether AI role separation is improving security or just reducing cost?
- How can teams tell whether AI readiness work is actually reducing risk?
- How can security teams tell whether AI agent access is drifting out of scope?
