AI governance and data readiness – Secure foundations for AI in the public sector

Overview

1. Welcome and why this matters now 

• Rapid AI adoption across the public sector
• Growing risk of Shadow AI and unmanaged data usage
• Balancing innovation with regulation, security and public trust

2. AI governance – The do’s 

• Treat governance as a business-wide responsibility, not just IT
• Align AI governance to existing public sector frameworks
• Establish clear guardrails (approved tools, data usage, accountability)
• Build governance into the AI lifecycle, not after deployment

3. AI governance – The don’ts 

• Don’t rely on policies without enforcement
• Don’t ignore Shadow AI
• Don’t over‑govern and block innovation
• Don’t separate AI governance from data governance

4. Data readiness: The foundation for safe AI 

• AI is only as reliable as its data
• Key elements of AI‑ready data:

  • Ownership, classification and quality
  • Secure access and identity controls
  • Lineage, monitoring and auditability

• Why data readiness directly reduces AI risk

5. From theory to action 

• Understanding your current AI and data reality
• Moving from fragmented controls to governance by design
• Building a phased, pragmatic roadmap

6. Q&A + Close & CTA 

• Audience questions
• CTA: assess AI and data readiness and regain control safely