Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
AI governance evidence needs to be built into normal operation as a byproduct, not assembled reactively when an auditor or regulator asks for proof.
Assessing an AI vendor needs AI-specific questions about training data use, retention, and model behavior, beyond the general security questionnaire already in use.
Shadow AI, tools and integrations adopted without central visibility, is best addressed by making sanctioned tools easier to use, not by relying on bans alone.
An enterprise AI acceptable use policy needs specific, checkable rules by use-case category, not general values, and a clear path from each rule to its enforcement.
A written AI policy describes intent; enforcement makes that intent operate in the running system, and an organization needs both to actually manage risk.
Classifying AI use cases by risk should weigh action capability, data sensitivity, and reach, since these three factors predict worst-case impact better than novelty.
An AI inventory needs to be built through direct outreach to engineering teams, not documentation alone, and maintained as a living process rather than a one-time list.
The EU AI Act's risk-tiered obligations translate into concrete security and documentation work for high-risk systems, worth reviewing with counsel before scoping.
The OWASP Top 10 for LLM Applications gives teams a shared vocabulary for recurring risks like prompt injection and excessive agency, useful for review, not a checklist alone.
The NIST AI RMF organizes AI risk work into govern, map, measure, and manage functions, giving teams a shared structure without prescribing specific tools or controls.
AI risk management works as a repeating cycle of identifying, assessing, treating, and monitoring risk, not a one-time assessment filed away after launch.
AI governance decides what rules apply and who is accountable; AI security implements the controls that make those rules real, and neither substitutes for the other.
