Everything here is written for one reader: the leader who has to approve an AI deployment and then own what happens next. No hype, no doom — what actually breaks in production, and what to do about it.
Frameworks
- The Reversibility Test: Grant AI Autonomy by Undo, Not by IQ — 2026-06 — The question that decides what an agent should do alone isn’t how accurate it is. It’s whether you can pull the action back, and how fast.
- The Executive AI Deployment Checklist — 2026-03 — A 5-layer diagnostic for moving from static compliance to dynamic integrity.
- The AI Corporate Governance & Usage Policy Template — 2026-03 — A plug-and-play governance framework for the people who carry the liability.
- The Zero-Trust Agent: Cryptographic Action Guardrails — 2026-03 — Moving past “human-in-the-loop” to risk-scored cryptographic execution. The technical deep-dive.
Field notes
- AI Safety Has Never Worked a Change Window — 2026-06 — The online AI-safety conversation is theoretical. Real safety lives where it’s 2am and the business is screaming.
- The Agentic Shift: Architecting Dynamic Integrity — 2026-04 — Why the move from generative to agentic AI breaks your existing security model.
Case study
- The $100M Hallucination: Post-Mortem of a Failed Enterprise AI Agent Deployment — 2026-03 — What happens when static security meets an agentic system with no output guardrails.
Start here if you’re new to this
- Beyond the Hype: 3 Critical LLM Vulnerabilities Every Leader Must Understand — 2026-03 — Indirect prompt injection, contextual data leakage, and semantic drift, in leader terms.
I’m Paul Mozaffari — 28 years securing enterprise infrastructure, now focused on the AI layer. If you’re putting AI into production and want a second set of production-scarred eyes on it, find me on LinkedIn.