AI-Assisted Security Triage and Mitigation Across Hundreds of Microservices

I am working on AI-assisted triage and mitigation of security issues in a complex environment spanning hundreds of microservices. The challenge is to turn fragmented findings into actionable decisions using service ownership, dependency relationships, exposure, business impact and supporting evidence.

The initiative has achieved initial success. Broader effectiveness and scale remain under evaluation.

Use this as a proposed reference workflow, not a claim that every step is deployed:

1. Normalize approved findings and group duplicates.
2. Retrieve approved code, service-catalog, and dependency context; cite evidence and flag missing or stale information.
3. Have AI summarize each issue, suggest a priority and owning team, and distinguish evidence from assumptions. Escalate uncertain cases.
4. Draft mitigation options, candidate fixes or compensating controls, implementation tradeoffs, and verification steps.
5. Have security engineers review triage. Route changes through normal tickets and pull requests for service-owner approval; require human approval for production changes and risk acceptance.
6. Retest fixes and use reviewer corrections to improve the workflow.

Apply least privilege, approved data and models, secret redaction, audit trails, and safeguards that treat retrieved content as untrusted. Do not allow automatic closure or production changes based solely on AI.

Initial success has been achieved in this ongoing initiative. The focus is to turn security findings into evidence-linked triage summaries, priority and ownership recommendations, practical mitigation options and verification checklists for human review.

The next phase is to validate broader effectiveness and scale through agreement with expert triage, false-positive and missed-risk rates, time to a reviewed decision, routing accuracy and mitigation retest success. These are planned evaluation measures.

Reference design considerations:

• Use stable finding and service identifiers to connect scanner evidence, code context, ownership and dependencies.
• Return structured recommendations containing evidence references, assumptions, missing context, proposed owner, priority rationale, mitigation options and retest steps.
• Treat retrieved code and finding text as data, never as instructions to the AI system.
• Use approved model endpoints and least-privilege retrieval; redact secrets and unnecessary personal data.
• Log the model/prompt version, evidence used, reviewer decision and retest outcome so errors can be traced and corrected.
• Keep the design vendor-neutral. Specific products and completed integrations are not specified.