Cutting through the noise

AI governance frameworks proliferated in 2023–2024. Enterprises need clarity, not yet another policy. This playbook distills mandatory controls into four workstreams.

1. Inventory & classification

• Build an AI system register that tags each use case with business owner, model type, and data origin.
• Map critical systems to EU AI Act risk levels (minimal, limited, high, unacceptable).
• Use Microsoft Purview to document data lineage and retention for each model pipeline.

2. Policy & design guardrails

• Adopt Microsoft’s Responsible AI Standard v2 as the template: fairness, reliability, privacy, safety, inclusiveness, transparency, accountability.
• Embed review checkpoints into Azure DevOps pipelines: bias testing, adversarial robustness, explainability reports.
• Require human-in-the-loop overrides for any high-risk automation.

3. Monitoring & incident response

• Deploy Azure AI Content Safety or custom classifiers to detect policy violations.
• Log prompt completions and model decisions with PII redaction.
• Run quarterly “AI kill switch” drills to rehearse disabling models without impacting critical services.

4. Governance structure

• Form an AI Governance Council with Legal, Risk, HR, and Business units.
• Provide Copilot usage guidelines and publish do/don’t scenarios.
• Offer escalation channels for employees to report concerns anonymously.

Toolkit download

Bundle your policies, impact assessment templates, and checklists in a single Microsoft Loop workspace. Reference your version in SharePoint for traceability.

Use the placeholder for a compliance briefing or stakeholder update.

Wrap with a CTA to schedule an AI governance workshop.