🚀 New TraceLogicAI Component: The Remediation Engine

TraceLogicAI is adding a new component designed to move AI governance beyond identifying problems.

The Remediation Engine will turn detected weaknesses into structured, actionable corrective plans.

When TraceLogicAI identifies a failure, the new component will generate:

Finding
The RAG response cited an outdated policy.

Business risk
Employees may receive incorrect guidance.

Likely cause
The vector database contains multiple policy versions.

Recommended correction

1. Identify the approved policy owner.

2. Remove superseded versions.

3. Add approval status and expiration metadata.

4. Re-index the corpus.

5. Rerun the evaluation.

Owner
Knowledge-management or HR policy owner

Verification criteria
The approved policy must appear in the top three retrieval results.

The Remediation Engine will also include corrective-action templates for common failure modes:

• LLM used without grounding: Add RAG, approved tools, citations, or human review.
• Undefined workflow: Map the human process and decision rules first.
• Poor document quality: Assign owners, versions, review dates, and access metadata.
• Excessive permissions: Apply least privilege and approval gates.
• Unsafe MCP integration: Review authentication, tools, secrets, logging, and validation.
• Weak testing: Generate edge, failure, and adversarial test cases.
• No measurable value: Establish operational baselines and ROI targets.

Users will be able to take immediate action directly from each result:

✅ Create remediation plan
✅ Assign owner
✅ Export control checklist
✅ Rerun after correction

This new component will connect detection, ownership, correction, and verification into a single workflow.

Because finding an AI weakness is only the beginning.

The real value comes from making sure it is corrected, assigned, tested, and closed.