AI Governance
Cloud security and AI governance go hand in hand. As AI becomes a load-bearing layer in cloud architecture, the question stops being "can the model do it" and becomes "is it allowed to, can you prove it, and what stops it when it's wrong." I design that control layer.
The core principle: governance as enforceable code, not prose. A rule written in a system prompt is an aspiration the model can ignore under pressure. A rule enforced outside the model is a control. Everything below is built to be the second kind.
NIST AI RMF Alignment
Map every autonomous system to the Govern / Map / Measure / Manage functions of the NIST AI Risk Management Framework — so AI risk is identified, measured, and owned, not assumed away.
Deterministic Guardrails
The hard limits live in code outside the model — a pre-execution gate that blocks disallowed actions and a risk shim that rejects out-of-policy operations. The model cannot talk its way past them.
Capability-Scoped Authorization
Each agent gets only the tools and scope it needs. Authority is centralized and auditable; nothing executes without an explicit, logged, scoped grant — the agent is the trust boundary.
PII-Egress Controls
Sensitive data is filtered before it can leave — egress scrubbing and least-data-movement, so an autonomous agent cannot exfiltrate what it should never touch, by accident or under attack.
Provenance & Audit
Signed, attributable actions and a complete audit trail. You can always answer who (or what) did it, with what authority — turning trust from a claim into evidence.
Human-in-the-Loop for High-Stakes
Irreversible, financial, public, or safety-affecting actions never run on model discretion alone — they pass deterministic checks or explicit human confirmation. Supervised autonomy with a hard boundary.
This isn't theory — it's how I build. The same controls that keep an autonomous agent inside policy are the ones a regulated, cleared, or security-conscious organization needs before it can trust AI with anything real.
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