AI Security

Security and governance for AI systems — covering both the policy/control framework organizations need to deploy AI responsibly, and the identity primitives autonomous agents need to act safely within those frameworks. Most organizations adopting AI lack the controls and audit trails their security, legal, and risk teams need before approving production use. At the same time, as agents start making API calls, accessing data, and taking action on their own, traditional IAM models built around human users and service accounts strain at the seams.

This practice covers both halves. On the governance side: mapping how AI is being used, where the risks concentrate, and what controls have to travel with each deployment — drawing on existing GRC frameworks where they fit and building new patterns where they don't. On the identity side: scoping models, delegation patterns, and the audit instrumentation needed to give AI agents real authority without giving them too much. The goal is security that engineers and product teams can actually live with — clear, lightweight, and integrated into the development flow rather than bolted on after the fact.