Need Policy Enforcement for Enterprise AI Consumption
Enterprises need a gateway-enforced layer that authenticates, meters, and tier-routes employee and application AI consumption — not just visibility into spend, but active enforcement of token quotas, model-tier fallback, and outbound traffic restrictions.
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Persona Story:
Laura, the head of AI, has rolled out AI assistants to the entire workforce but cannot let employees talk to model providers directly. She needs a gateway in front of the model APIs that authenticates the call, charges it against the right cost center, enforces a weekly token budget, falls back to a cheaper model when the budget is exhausted, and denies the call entirely when policy demands. Spend visibility is not enough — leadership needs the gateway to actually block.
Problem Context
- Employees and applications need access to commercial AI models (Claude, OpenAI, Bedrock-hosted models) but cannot send arbitrary outbound traffic from corporate networks
- Token budgets need to be enforced per-user, per-team, and per-cost-center, not just reported on
- Different models have different cost / capability profiles — policy needs to route to the cheaper model when budget runs low and deny entirely when limits are hit
- AI FinOps tooling reports spend after the fact; what’s missing is a control plane that blocks before the call goes out
Problem Impact
- Without enforcement, costs spiral and finance discovers the overrun a month later
- Without an enforcement layer, every employee endpoint becomes a potential outbound data leak surface
- Teams build ad-hoc proxies that don’t share quota state, producing inconsistent enforcement across the company
- Audit trails are fragmented across model providers, internal proxies, and BI cost dashboards
Naftiko Today
- REST + MCP exposure with declarative consumes wraps any model provider behind a consistent gateway boundary, so policy lives in one capability YAML
- External bindings let token budgets and tier-down rules ride existing enterprise secret management instead of being baked into application code
- Capability-as-bridge pattern allows wrapping Claude / OpenAI / Bedrock together so the policy layer is the same regardless of which backend serves the request
- Standardized request / response logging emits the metering events that downstream FinOps and SIEM tools need without requiring per-application instrumentation
Naftiko Tomorrow
- Token-budget enforcement primitives (Second Alpha) would surface as first-class capability rules — quota windows, tier-down conditions, deny conditions — rather than custom code per integration
- A2A adapter (Second Alpha) would let an enforcement capability stand in front of agent-to-agent traffic so model calls flowing through agent chains are still policy-bound
- Enterprise IAM integration with Keycloak and OpenFGA (v1.1) would tie token budgets to identity and authorization context, not just IP / API key
- Policy library (v1.1) would ship reference enforcement patterns — per-employee weekly budget, per-team tier ladder, per-cost-center routing — so Laura’s first deployment is a config change, not a build