⚡ Quick Summary
This brief, business-oriented checklist from Cognitive View offers a practical entry point for organizations aiming to evaluate and mature their AI governance practices. Covering eight key areas—from discovery to regulatory alignment to ethics—it blends process-level prompts with concrete operational checkpoints. Designed for immediate use, it helps identify blind spots in AI deployment and create a phased roadmap to stronger governance.
🧩 What’s Covered
The checklist is organized into eight thematic domains, each with action-oriented goals and bullet-level instructions:
1. AI Discovery
- Map all AI/ML-powered tools, including SaaS and shadow AI
- Document internal and third-party systems
- Align use cases with departmental owners and business functions
2. Data Governance & Management
- Set standards for data quality and classify sources
- Validate consent, apply minimization, and ensure encryption
- Emphasize automated anomaly detection and RBAC logging
3. Model Development & Deployment
- Define success metrics early (e.g., accuracy, F1, fairness)
- Apply versioning, change logs, and CI/CD tooling
- Monitor for drift and adversarial vulnerability
- Include post-deployment maintenance planning
4. Governance & Risk Management
- Align with NIST AI RMF, ISO/IEC, OECD principles
- Use risk assessments (DPIA, AIA, DRA) and assign risk levels
- Create cross-functional oversight roles (e.g., AI Risk Officer)
- Enforce third-party audits and documentation repositories
5. Regulatory & Compliance Alignment
- Build a regulation inventory (EU AI Act, GDPR, HIPAA)
- Track compliance gaps and remediation timelines
- Maintain traceability documentation and breach protocols
6. Ethics, Fairness & Transparency
- Regular bias testing (e.g., disparate impact ratio)
- Require XAI methods like SHAP or LIME
- Enable opt-outs, appeals, and human-in-the-loop workflows
7. Ongoing Improvement & Culture
- Deliver training on AI risk and ethics
- Use retrospectives, feedback loops, and stakeholder input
- Pilot new models in sandboxed, low-risk settings
8. Next Steps & Self-Assessment
- Start with a quick maturity review
- Assign ownership by domain (Legal, Data, Product)
- Prioritize high-risk domains for immediate improvement
- Consider using Cognitive View’s own AI Governance Platform for support
💡 Why it matters?
This is one of the most usable entry-point tools for non-expert teams to kickstart responsible AI governance. It reduces the friction of aligning to frameworks like ISO 42001 or NIST RMF and provides concrete, actionable questions. Especially valuable for organizations that need to inventory risks and create fast-but-thoughtful governance structures.
❓ What’s Missing
- No scoring or benchmarking function—the checklist is qualitative
- Doesn’t include implementation examples, templates, or control libraries
- Not mapped to specific obligations in the EU AI Act, which limits regulatory specificity
- Vendor-neutral language is helpful, but the final pages lean toward platform promotion
👥 Best For
- Mid-sized companies and financial institutions scaling genAI usage
- Privacy, risk, and compliance teams seeking a first-round audit tool
- Digital leads preparing for internal governance conversations
- SMEs adapting to new procurement or supply chain expectations
- Consultancies and auditors needing a structured intake tool
📄 Source Details
- Title: AI Governance Readiness Checklist (Brief Version)
- Publisher: Cognitive View
- Date: 2024
- Length: 10 pages
- Format: Actionable checklist with section-by-section instructions
- License: Open-access preview; full version available via demo request
- Website: www.cognitiveview.com
📝 Thanks to the Cognitive View team for distilling governance best practices into an operational and readable format for teams starting their AI assurance journey.
