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    AI Governance built into the request path.

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    Model allowlists, safety screening, and evidence your CISO and AI risk officer can take to the board — enforced before the LLM runs, not pasted into a slide deck afterward.

    AI Governance on Behest means every chat completion passes policy gates: approved models only, PII scrubbed pre-model, prompt-injection checks, and structured audit data — so enterprise buyers can align EU AI Act self-classification and NIST AI RMF workflows to what actually happens in production.

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    Built-in Token FinOps

    Enforce hard budgets, attribute costs per session, and get complete visibility into your enterprise AI spend.

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    How a request is governed

    The same control plane that powers your AI backend applies governance inline — developers keep a single integration; security gets defensible defaults.

    Tap a step to see what happens on every request.

    Model allowlist

    Only approved models run

    Tenant admins control which upstream models each environment can call. Changes follow your governance process — no surprise routes to new endpoints after deploy.

    Illustrative control path. Exact retention, allowlist UX, and break-glass access are configured per deployment and contract.

    EU AI Act readiness

    Buyers in 2026 expect GPAI deployer transparency, use-case risk self-classification, and contract terms that cover no-train commitments, incident notification, and model-change notice. Behest is structured so your legal and AI risk teams can map those obligations to technical controls — allowlists, disclosure of auxiliary models where applicable, and retention-aware logging.

    Behest does not assign a risk class on your behalf; your organization documents prohibited and high-risk uses in governance artifacts supplied during enterprise review.

    NIST AI RMF alignment

    Map Behest to the framework your regulators and customers already cite: Govern policy and roles, Map systems and data flows, Measure behavior and spend signals, Manage incidents and rollouts with budgets, RBAC, and kill switches shared with Token FinOps.

    • Model card packs and usage policy templates ship with enterprise deals for buyer-side documentation.

    Controls procurement teams ask for

    • Tenant model allowlists

      Approve, scope, and deprecate models per tenant without redeploying client applications.

    • PII Shield

      Microsoft Presidio-based detection and scrubbing before payloads reach the LLM.

    • Sentinel

      Inline prompt-injection and abuse screening on inbound user and agent traffic.

    • Audit-oriented usage records

      Structured request metadata for governance reviews, with redaction policies suited to regulated data.

    • BYOK and isolation

      Provider keys encrypted at rest, logical tenant isolation, and self-hosted deployment options.

    Same AI backend. FinOps + Governance together.

    Developers integrate once via Add AI to Your App. Finance gets Token FinOps for chargeback-ready attribution. Security gets the controls on this page — one gateway, three stakeholders satisfied.

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    Frequently asked questions

    What is AI Governance in Behest?
    Behest applies policy and safety controls on the hot path of every LLM request: tenant model allowlists, PII redaction before the model sees text, Sentinel prompt-injection screening, structured usage records, and configurations you can align to procurement and risk frameworks — without asking developers to rebuild a gateway.
    How does Behest support EU AI Act transparency and GPAI expectations?
    Behest is built for deployments where customers self-classify use-case risk under the EU AI Act. The platform supports downstream transparency artifacts (model disclosure, allowlists, change notification commitments in enterprise agreements) and technical measures like audit-oriented logging and sub-model disclosure where internal pipelines invoke additional models. Final legal classification remains the customer’s responsibility.
    How does Behest map to the NIST AI RMF?
    Behest’s controls align to NIST AI RMF functions enterprises already use: Govern (policy and allowlists), Map (data flow and model routing visibility), Measure (usage, cost, and safety signals per request), and Manage (budgets, kill switches, and operational response hooks). Exact mapping depth is documented in enterprise security reviews.
    Does Behest use customer prompts to train foundation models?
    Enterprise agreements are structured so Behest does not use customer data to train foundation models, with contractual no-train language and attestation expectations for upstream providers consistent with 2026 procurement standards. Your legal team receives the exact clauses during review.
    How do tenant model allowlists work?
    Customer administrators define which upstream models each tenant may call. Requests outside the allowlist are blocked at the gateway, so new provider routes cannot appear without a deliberate policy change — reducing shadow AI and unapproved model drift.

    Enterprise Token FinOps: Enforce hard budgets and attribute costs per session.

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