Securing AI Systems: An Enterprise Defense Framework
An O'Reilly guide to governing AI risk across discovery, data, policy, enforcement, and monitoring
AI is now embedded in 78% of enterprise environments, yet most security programs were built for a different era of computing. Traditional data loss prevention (DLP) monitors file transfers, but AI risk moves through fragments, conversational context, and inference that legacy tools cannot reliably trace. Written by Pamela Isom and published by O'Reilly in collaboration with Cyberhaven, this guide gives security and IT leaders a five-pillar operating model for governing AI from shadow tool discovery through agentic workflow control.
Key Takeaways:
- Traditional DLP protects files; AI security requires governing how data is interpreted, recombined, and embedded in automated decisions, a structural shift legacy tools were never built to make
- Shadow AI and embedded AI features in enterprise software create exposure before any policy can be enforced; visibility has to come first
- Agentic AI systems must be governed as active participants in enterprise operations, not tools, because they retrieve data, coordinate tasks, and initiate actions across systems without human review
Better understand the data risks inherent with agentic AI, and better secure your enterprise environment.
