AI Governance Framework: Ownership, Controls, Layered Validation, Evidence

Governance is the guardrail for provenance-first agent systems. Use this framework to keep delivery fast while proving control and compliance.

Core layers

1. Inventory and ownership — system list, accountable owners, escalation path
2. Controls and policy — access rules, change gates, data handling, role personas
3. Evaluation and reliability — Layered-CoT checks, drift tests, sandbox-promote pattern
4. Evidence and audit — signed logs, approvals, incident reports

Operate with a “sandbox → promote” cadence

• Sandbox: let agents explore (creative CoT, bold tool calls).
• Promote: validate with Layered-CoT and RAV/RAC before outputs reach users.
• Lock: store decisions in append-only audit logs.

# Governance gate (python-ish) tying sandbox → promote → audit
from datetime import datetime

def promote(run_id, draft, verdict):
    """Only promote if validation passed."""
    record = {
        "run_id": run_id,
        "timestamp": datetime.utcnow().isoformat(),
        "status": "promoted" if verdict.ok else "blocked",
        "reason": verdict.reason,
        "inputs": draft.context,
        "outputs": draft.result,
    }
    audit_log.append(record)  # write to signed store
    return record["status"] == "promoted"

Implementation blueprint

• Policy + controls: /ai-governance/, /ai-controls/, /ai-data-governance/, /ai-governance-policy-llm-systems/
• Risk + evaluation: /ai-risk-management-framework/, /ai-risk-assessment-framework/
• Observability + evidence: /ai-observability/, /llm-observability-agent-workflows/, /llm-audit-trail-agent-pipelines/
• Security: /ai-security-policy/, /llm-security/, /owasp-llm/

How this ties to the COT repo

• Agent roles and personas: see Multi-Agent-COT-Prompting (persona control + instruction reset).
• Layered-CoT: use layered reasoning with checkpoints before promotion.
• Orchestration: route requests through a reliable orchestrator with risk-weighted gates.

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