Securing AI Systems: An Enterprise Framework
An O'Reilly eBook by Pamela K. Isom, former AI director at the U.S. Department of Energy. A five-pillar model for governing AI risk and closing legacy DLP gaps.
A five-pillar model for governing AI risk
Why file-centric DLP misses AI-driven exposure
How to govern shadow AI and agentic workflows
A red-team framework for testing AI controls
Trusted by security teams at
What you'll take away
Four Numbers Behind Why AI Outpaces Traditional Data Security
78%
AI adoption outpaces governance
of organizations now use AI in at least one business function, up 23 points from a year earlier (Stanford HAI 2025).
39.7%
Sensitive data rides along
of all human interactions with AI tools involve sensitive data, often shared in fragments, not files (Cyberhaven Labs 2026).
48%
Agentic AI is the top threat
of cybersecurity professionals rank agentic AI as the leading attack vector, ahead of deepfakes (Dark Reading poll).
40%
Agents are scaling fast
of enterprise applications will include AI agents by year-end, up from under 5% in 2025 (Gartner).
"The question is not whether the system works. The question is whether the organization can maintain awareness and control when it matters most."