Risk Management Show AI Risk Layers EXPLAINED: Models, Applications, Agents with Walter Haydock
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Jun 16, 2025 
Walter Haydock, founder of StackAware and an expert in AI risk management, dives into the critical layers of AI risk: models, applications, and agents. He discusses the importance of data quality and the unique challenges each layer presents in decision-making and compliance. Walter emphasizes the need for human oversight in AI systems and shares actionable strategies for organizations to develop robust AI policies. Additionally, he highlights the growing significance of proactive risk management for sustainability and success in today’s AI-driven landscape.
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Three Layers of AI Risk
- AI risks exist in three layers: models, applications, and agents that interact autonomously.
- Data provenance, regulatory obligations, and human oversight are key considerations at each layer.
AI Agents Example: Microsoft Copilot Studio
- Microsoft Copilot Studio exemplifies AI agents by connecting GPT-4 to business processes.
- Risks arise when agents inherit permissions or make unchecked database changes.
Practical Steps for AI Risk Management
- Establish an actionable AI policy integrated with existing security and use policies.
- Maintain an asset inventory and perform risk assessments to manage AI risks effectively.