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Key takeaways Key areaWhat enterprises should doSecurityProtect models, data, APIs, and AI agents throughout the

​ An AI system can return accurate results and still leak personal data, breach privacy

A successful AI agent can still produce a failed enterprise system. An Agentic AI prototype

AI projects fail in production when teams treat a prototype as the finished product. Key

Cloud architecture and AI systems solve different enterprise problems. Choosing the wrong modernization path can

AI projects fail when enterprises start with tools instead of architecture, data, security, and business

Why legacy data can block enterprise AI progress AI cannot produce reliable business value when

AI adoption often fails at the system layer, not the model layer. Many U.S. enterprises

AI governance fails when an enterprise cannot prove who controls an AI system, why it

Key takeawaysDetailsPrimary goalProduce reliable evidence for regulators and internal reviewers.Why it mattersClear records close compliance