Overview & context

This guide shows how to operationalise AI risk and governance through automated DevOps pipelines. It connects policy-level controls from ISO/IEC 42001 and NIST RMF to executable scripts, build checks, and dashboards. Each pipeline stage acts as a governance gate that validates ethics, data integrity, bias, and security before deployment.

Objectives & benefits

• Automate compliance — build governance checks directly into CI/CD.
• Enforce risk thresholds for fairness, drift, explainability, and robustness.
• Generate evidence automatically for audits (JSON, logs, screenshots).
• Enable rollback and model registry integration for traceability.

Architecture overview

The architecture connects MLOps pipelines (Azure ML, Vertex AI, AWS SageMaker, GitHub Actions, Jenkins, etc.) with the AIMS evidence repository and risk register.

Stage 1 — Code Commit: Governance tags + model card template auto-generated  
Stage 2 — Data Validation: Quality, bias, completeness, privacy scan  
Stage 3 — Model Training: Bias & drift thresholds validated (Python tests)  
Stage 4 — Testing: Explainability, reproducibility, security scan  
Stage 5 — Deployment: Risk profile check + evidence archive  
Stage 6 — Monitoring: Continuous drift & performance metrics  

Governance gates & checkpoints

• Govern Gate: Verify model card, owner assignment, and documentation completeness.
• Map Gate: Check risk profile and regulatory category match (EU AI Act Annex III).
• Measure Gate: Validate trustworthiness KPIs (bias, accuracy, robustness).
• Manage Gate: Confirm CAPA closure, risk residuals < threshold, and approval sign-off.

Pipeline integration examples

name: AI Model Governance
on: [push]
jobs:
  compliance:
    runs-on: ubuntu-latest
    steps:
      - name: Run Bias Test
        run: python tests/test_bias.py
      - name: Validate Explainability
        run: python scripts/check_explainability.py
      - name: Upload Evidence
        run: python scripts/upload_to_aims.py --artifact logs/results.json
      - name: Governance Approval
        uses: ZenAIGovernance/approval-gate@v1
  

Automation & evidence capture

• Each pipeline run generates an Evidence ID (EV-###) linked to AIMS.
• Metrics automatically logged: accuracy, bias, fairness, drift.
• Logs pushed to AIMS Evidence folder (JSON, CSV, PNG).
• Failed gates trigger CAPA creation with timestamp and model ID.

Security & compliance validation

• Scan models for prompt injection, data leakage, and dependency vulnerabilities.
• Use vulnerability scanners (Bandit, Snyk, Dependency-Check) pre-deploy.
• Encrypt all artifacts in storage and during transfer (AES-256, TLS 1.3).
• Review compliance logs weekly to ensure pipeline integrity.

Reporting & dashboards

• Pipeline success/failure rate dashboard in Looker / Power BI.
• Compliance Score = (# successful gates / total gates) × 100.
• Threshold breaches auto-notified to Oversight Officer & AI Governance Board.
• Monthly summary sent to Management Review as part of AIMS performance evaluation.

Common pitfalls & good practices

• Manual governance: Replace human approvals with automated checks wherever possible.
• No evidence link: Always attach pipeline outputs to AIMS Evidence IDs.
• Unaligned thresholds: Ensure bias/drift thresholds match Risk Register values.
• Pipeline sprawl: Standardise pipeline templates for all AI teams.

Implementation checklist

• Pipeline Governance Gates defined (Govern/Map/Measure/Manage).
• Compliance scripts operational and linked to AIMS Evidence Repo.
• Automated CAPA creation integrated with issue tracker (Jira/ServiceNow).
• Monthly reports submitted to AI Governance Board.
• All pipeline evidence retained ≥ 3 years for audit.

© Zen AI Governance UK Ltd • Regulatory Knowledge • v1 12 Nov 2025 • This page is general guidance, not legal advice.