Privacy Risk and AI Governance Checklist

Responsible for AI implementation, governance, or compliance?

This checklist guides legal, tech, and compliance leaders through key questions to assess privacy risks, map accountability, and strengthen your data and governance posture before problems arise.

Use this checklist to:

Identify AI-related privacy and governance blind spots
Strengthen your oversight practices
Move from reactive compliance to strategic clarity

Key Questions to Assess Your Readiness

1. Have we clearly documented the purpose, scope, and intended outcomes of each AI tool in use?

  • I Without clear objectives, it is difficult to manage privacy impact, align with ethical standards, or assess downstream risk.

2. Do we have an up-to-date data flow map for every system using AI?

  • Understanding how data moves through your systems is essential for identifying risk and ensuring compliance with data minimization principles.

3. Have we conducted a Privacy Impact Assessment (PIA) or Data Protection Impact Assessment (DPIA) for high-risk AI use cases?

  • Formal assessments build a record of diligence and support transparency and accountability—especially under the EU AI Act or similar frameworks.

4. What measures are in place to secure training and input data—especially sensitive or personal data?

  • Security breaches in training data introduce legal, ethical, and reputational risks.

5. Do we have internal controls or dashboards to trace, explain, and audit AI outputs?

  • Explainability tools and trace logs build trust, ensure accountability, and meet growing regulatory expectations.

6. Are we regularly testing for bias, model drift, or performance degradation over time?

  • Ongoing validation helps maintain fairness, reliability, and trust in how systems perform across different groups.

7. Have we aligned vendor evaluation criteria with AI governance and privacy standards?

  • Procurement and vendor management processes should include specific questions about AI use, data handling, and bias mitigation.

8. Do we have appropriate cyber and data security controls in place for AI systems

  • AI systems require robust defenses against attacks like data poisoning, model inversion, or adversarial inputs.

9. Are we actively monitoring governance KPIs for compliance, bias, and privacy posture?

  • Metrics such as error rates, privacy violations, and resolution speed signal system health—and readiness for audit or scrutiny.

10. Do we have a defined process for human review, override, or escalation in decision-making?

  • Human-in-the-loop oversight is essential for ethical decision-making and is required in many high-risk use cases.

Bonus Question

Am I helping my team stay grounded—even when the direction is coming from somewhere else?

  • I lead through change with clarity, consistency, and care—even when I am not the one who chose the tools.

Reflection

As you review your responses, take note of any questions you answered with “no” or “not sure.” These are indicators—pointing to areas where stronger leadership, clearer oversight, or a more strategic approach is needed.

They highlight where your systems, team, or governance posture may need reinforcement and where thoughtful action can build trust and long-term readiness.

Ready to Strengthen Your Governance Strategy?

UpwardAction® Advisory supports legal, tech, and compliance leaders in navigating the complexity of AI governance, data privacy, and risk management.

We provide strategic coaching, frameworks, and training that strengthen your ability to lead with clarity, oversight, and confidence—so your systems stay compliant, your team aligned, and your reputation protected.

About TC Cooper

TC Cooper is the President of UpwardAction® and a former senior agency official for privacy in the U.S. federal government. She has completed the coursework for IAPP’s Artificial Intelligence Governance Professional (AIGP) designation and brings decades of legal, operational, and compliance experience into her advisory and coaching work.

TC helps organizations lead AI integration and innovation with a values-based approach – strengthening compliance, trust, and team performance in an era of rapid change.

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