From the course: Non-Functional Requirements in the Cloud: Foundations, Planning, and Implementation
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AI-specific governance
From the course: Non-Functional Requirements in the Cloud: Foundations, Planning, and Implementation
AI-specific governance
- [Instructor] We've explored AI-specific NFRs, but how do we ensure these are actually met consistently and accountably? That's where AI-specific governance comes in. In the cloud, especially with the rapid adoption of AI, traditional governance models might not be sufficient. We need frameworks that acknowledge the unique characteristics of AI systems. One framework that helps us think about AI security from a governance perspective is MAESTRO, which stands for Multi-Agent Environment, Security, Threat, Risk, and Outcome. Developed by Ken Huang, MAESTRO addresses gaps in traditional threat modeling frameworks by focusing on AI's autonomy, machine learning-specific issues, agent interactions, and system level concerns. We'll talk through MAESTRO here to give an example of extra considerations required, but you may choose to use any framework that best suits your needs at the time as frameworks continue to be developed as we speak. MAESTRO provides a seven-layer reference architecture…
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