Why Enterprises Won't Scale Agents They Can't Audit
Agent rollouts don't stall in engineering — they stall in review. The four questions every auditor asks, and why the trust gap is really an audit gap.
Research and analysis on AI agent trust, auditability, evaluation standards, and continuous monitoring.
Agent rollouts don't stall in engineering — they stall in review. The four questions every auditor asks, and why the trust gap is really an audit gap.
The most important finding in Microsoft's 2026 enterprise-AI research isn't about what models can do — it's about why they don't get used. Trust is now the go-to-market surface.
A trust score worth acting on is anchored to standards that outlive any vendor. How NIST AI RMF, CSA guidance, and Know Your Agent compose into a defensible agent trust score.
The agent you approved no longer exists — its model was updated underneath it. Why a point-in-time review is a photograph of a river, and what continuous agent assurance actually looks like.
You cannot hold an agent accountable if you cannot reliably identify it. Why a portable agent-identity layer — identity, reputation, validation — is the layer the agent economy is missing.
A security brief on where agent trust really lives — below the agent, in an auditable AI data layer: the seven controls, identity-first agents, deny-by-default access, and delegation that only narrows.