Most AI governance lives in documents and reviews. I build systems where authority is explicit, decisions are owned, and nothing acts by default.
I treat AI the same way Zero Trust treats networks: every decision must prove it is allowed at the moment it executes.
Instead of dashboards and post-hoc audits, I design control planes that enforce integrity during reasoning:
Limit what an AI can consider at the context level, before inference begins.
Identify drift in real time across providers, models, and reasoning chains.
Apply trust boundaries at execution, not after. Authority before action.
Log every decision with cryptographic, verifiable evidence. Not logs. Proof.
I bring 15 years designing and defending systems for the DoD, Fortune 100 companies, and regulated enterprises across cybersecurity consulting and federal infrastructure.
After a significant federal agency breach, I helped move Zero Trust from conceptual guidance into operational reality within federal environments, before reference architectures, tooling maturity, or formal policy existed. That work became the government standard.
Today I apply those same principles to artificial intelligence. Every major government technology failure follows the same pattern: capability advances faster than enforcement, governance lags behind deployment, and trust assumptions go unexamined until they are exploited.
My goal: make advanced AI powerful without letting it act on unowned intent.
Advisory engagements for organizations navigating AI compliance and pre-execution governance.
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