AI Governance
AI governance is how an organization decides which AI models can run, what data they can access, and what audit evidence each request must produce.
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AI governance is the set of decisions and controls an organization puts around its use of AI: which models are approved, what data they are allowed to see, who can use them, and what evidence each interaction must leave behind. Policy is only half of it — governance is real when those decisions are enforced automatically, not just written in a document.
Behest enforces governance at the request layer, where it can act before a model runs. Model allowlists stop unapproved or unreviewed models at the gateway, PII Shield scrubs sensitive data out of prompts, Sentinel defends against prompt injection, and an immutable audit trail records every call — model, tokens, cost, user, and security events like redactions and blocks.
Mapping to frameworks
These controls line up with the technical expectations of emerging regimes such as the EU AI Act and the NIST AI Risk Management Framework — audit evidence, access control, and guardrails against misuse. Behest does not certify your program; it provides the request-path controls and evidence your own auditors and risk owners look for.
Frequently asked questions
How does Behest help with EU AI Act and NIST AI RMF compliance?
Behest provides the technical controls required by emerging AI regulations. This includes immutable audit trails of all AI interactions, model allowlists to prevent the use of unapproved shadow IT models, and built-in guardrails (PII redaction and prompt injection defense) that map directly to NIST AI RMF risk management requirements.
What are model allowlists?
Model allowlists let administrators strictly define which LLMs can be used by which applications or departments. If a developer tries to call an unapproved model (e.g., an experimental model that hasn't passed legal review), Behest blocks the request at the gateway level, ensuring enterprise-wide policy enforcement.