Identity and security teams should own the policy layer, not the gateway configuration alone. The proxy can enforce decisions, but governance belongs in versioned policy that can be tested, reviewed, and audited. That keeps model access, tool exposure, and argument-level rules consistent across changing agents and protocols.
Why This Matters for Security Teams
Shared proxies are convenient, but they can hide the real control problem: an AI agent is not a fixed user with a stable access pattern. When multiple agents, workflows, or tenants pass through the same proxy, the gateway may see traffic, but it does not by itself define who can do what, under which context, and for how long. That policy belongs in a versioned layer that security and identity teams can review, test, and audit. Current guidance on agentic systems aligns with this separation of enforcement and governance in the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework.
NHI Management Group’s research shows why this matters operationally: in LLMjacking: How Attackers Hijack AI Using Compromised NHIs, attackers attempted access to publicly exposed AWS credentials within an average of 17 minutes. That pace leaves little room for ad hoc proxy rules or manual approvals. Security teams need policy that can constrain model access, tool calls, and argument-level behavior before the proxy forwards the request.
In practice, many security teams discover the ownership gap only after a shared proxy has already been used to over-broaden access across agents or environments.
How It Works in Practice
The cleanest operating model is to treat the proxy as the enforcement point and the policy layer as the source of truth. The proxy should authenticate the workload, inspect the request, and apply a decision that has already been defined in policy-as-code. That policy should specify which agent identity is calling, what tool or model it is requesting, what data classification is involved, and whether the action is allowed in that context. This is the practical bridge between governance and runtime control.
For shared proxies, security teams should prefer workload identity over shared secrets. That means using cryptographic identity for the agent or service, such as SPIFFE-style workload identity or OIDC-backed tokens, rather than assuming the proxy alone can distinguish safe from unsafe callers. This matters because static IAM roles do not map well to autonomous behavior. Agents can chain tools, change objectives mid-session, or escalate through indirect prompts, so authorization needs to be evaluated at request time. The CSA MAESTRO agentic AI threat modeling framework and the MITRE ATLAS adversarial AI threat matrix both reinforce the need to model dynamic misuse paths rather than trust the transport layer.
- Define policy in version control so changes are reviewable, testable, and attributable.
- Bind each request to workload identity, not just a proxy route or shared API key.
- Evaluate intent, context, and data sensitivity at runtime before forwarding to the model or tool.
- Issue short-lived credentials where possible and revoke them automatically after the task completes.
- Log policy decisions separately from proxy traffic so auditors can reconstruct why access was granted or denied.
NHI Management Group’s Top 10 NHI Issues and Lifecycle Processes for Managing NHIs both point to the same operational reality: governance fails when identity, secrets, and runtime authorization are treated as separate conversations. These controls tend to break down in high-throughput multi-agent environments because request context is lost between orchestration layers and the proxy only sees the final call.
Common Variations and Edge Cases
Tighter policy control often increases deployment overhead, requiring organisations to balance faster agent iteration against stronger review and traceability. That tradeoff becomes visible when teams run many agents through one shared gateway, because a policy tuned for one workflow can unintentionally overfit or block another. There is no universal standard for this yet, so best practice is evolving around intent-based authorization and context-aware decisions rather than static allowlists alone.
Some environments do need exceptions. For example, low-risk internal agents may share a proxy but still require separate policy namespaces, while regulated workloads may demand tenant-specific policy, stronger approval gates, and distinct logging retention. Shared proxies also create ambiguity when platform teams own the routing layer but not the semantics of model use. In those cases, security and identity teams should own the policy language, while platform teams own uptime and forwarding mechanics. The split matters because a proxy can enforce a decision, but it cannot safely invent the policy behind that decision.
For governance alignment, the NIST AI Risk Management Framework and the Ultimate Guide to NHIs — Regulatory and Audit Perspectives support this separation of duties. The practical takeaway is simple: shared proxies are infrastructure, not governance. When ownership is unclear, policy drifts into configuration sprawl, and that is when agent access becomes hardest to audit and easiest to abuse.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 | Agentic apps need runtime policy for autonomous tool use and shared proxies. |
| CSA MAESTRO | GOV-1 | MAESTRO emphasizes governance boundaries and control ownership in agentic systems. |
| NIST AI RMF | AI RMF addresses governance, accountability, and risk controls for AI systems. |
Separate policy ownership from enforcement and document who approves each agent capability.
