How should security teams implement governance for enterprise LLM traffic across multiple teams and models?

Why This Matters for Security Teams

Enterprise LLM traffic is not just another outbound API pattern. Once multiple teams, models, and vendors are in play, security teams lose visibility into who is calling what, why it was allowed, and whether the request is still within policy. That creates a governance gap across identity, spend, data handling, and audit. The risk is amplified when teams share API keys or route traffic directly from application code, because revocation becomes slow and attribution becomes weak.

This is why gateway-level governance is becoming the practical control point. It centralises policy enforcement without forcing every application team to reimplement controls, and it supports per-team identities, request logging, budget enforcement, and data routing rules. Current guidance from NIST AI Risk Management Framework and the OWASP Agentic AI Top 10 both point toward stronger runtime governance, but there is no universal standard for how every enterprise should structure the control plane.

NHI research reinforces the point: in The State of Non-Human Identity Security, 85% of organisations reported limited visibility into third-party OAuth connections, which is a familiar pattern when AI traffic is not mediated centrally. In practice, many security teams encounter uncontrolled LLM usage only after spend spikes, data leakage, or model sprawl has already created the audit problem.

How It Works in Practice

Effective governance starts by treating the gateway as a policy plane, not merely a proxy. Each team, service, or application gets its own workload identity, then the gateway maps that identity to approved models, rate limits, spending thresholds, and data handling rules. That lets security teams enforce least privilege at request time rather than trusting static app-level configuration.

A workable implementation usually includes four layers. First, authenticate the caller with a workload identity instead of a shared secret. Second, evaluate policy at runtime based on team, model, request type, destination, and data sensitivity. Third, log the full transaction for audit and chargeback. Fourth, route requests only to approved models or regions. This approach aligns well with NIST Cybersecurity Framework 2.0 and the control themes in CSA MAESTRO agentic AI threat modeling framework, especially where runtime decisions and traceability matter.

Security teams should also separate policy from application code. That reduces drift when teams adopt different models or orchestration libraries, and it makes revocation immediate if a service is compromised. Using the gateway to enforce per-team budgets and model allowlists also creates a clean place to measure risk, compare usage across teams, and block exfiltration paths before data reaches an external provider. The AI Agents: The New Attack Surface report is useful here because it shows how quickly autonomous systems exceed intended scope when governance is weak.

These controls tend to break down when teams bypass the gateway for direct vendor calls, because policy no longer sits on the critical path.

Common Variations and Edge Cases

Tighter gateway control often increases operational overhead, requiring organisations to balance policy precision against developer velocity and model experimentation. That tradeoff is especially visible in research teams, prompt-testing environments, and product groups that need rapid access to new models.

One common variation is a hybrid model: strict controls for production traffic, lighter controls for sandbox environments, and explicit approval for new models or regions. That can work, but current guidance suggests it should still preserve identity, logging, and budget visibility across all environments. Another edge case is multi-provider routing, where one team may use different models for inference, summarisation, or safety checks. In that setup, governance should follow the data flow, not just the application owner.

There is also a practical question of data residency and prompt content filtering. Teams often assume a single allowlist is enough, but enterprise LLM traffic may include secrets, regulated data, or customer records. In those cases, routing rules should be coupled with content classification and explicit exceptions. The OWASP NHI Top 10 is relevant because it highlights how identity and access failures become security failures when non-human workloads move across toolchains and vendors. Best practice is still evolving, so security teams should treat initial governance as a living control set, not a one-time policy rollout.

Related resources from NHI Mgmt Group

• How should security teams make NHI best practices usable across the business?
• How should security teams use IAST and RASP in NHI governance?
• Why is single-provider AI agent governance not enough for enterprise security?
• How should security teams govern SSO across multiple enterprise applications?