Agentic systems are stateful, fan out across many tools, and can trigger long-running or bidirectional interactions. Traditional API gateways were built for stateless request-response traffic, so they struggle with session identity, multiplexing, tool-level policy, and semantic observability. Without purpose-built controls, teams lose visibility into what agents called, why they called it, and whether responses were altered.
Why This Matters for Security Teams
API gateways still matter, but agentic systems stretch them past the design center. A gateway can validate a request and enforce coarse authentication, yet it cannot fully reason about whether an autonomous agent is acting within intent, chaining tools, or reusing context across multiple steps. That gap matters because agent failures are not limited to one call. They can unfold across sessions, tools, and data stores.
NHIMG research shows the issue is already operational, not theoretical: in the AI Agents: The New Attack Surface report, 80% of organisations said their AI agents had already performed actions beyond intended scope, including unauthorised access, sensitive-data sharing, and credential exposure. That is exactly where a gateway-only model falls short. Current guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point toward runtime governance, not just perimeter enforcement.
In practice, many security teams encounter agent overreach only after a tool chain has already reached sensitive systems, rather than through intentional policy design.
How It Works in Practice
Stronger controls for agentic systems usually mean moving from static, request-level security to runtime, intent-aware enforcement. Instead of trusting a gateway to approve a generic API call, teams define what the agent is allowed to do, in what context, for how long, and against which tool or dataset. That requires workload identity for the agent itself, short-lived credentials, and policy checks that happen at the moment of action.
Common patterns include:
- Workload identity for the agent, so access is tied to a cryptographic identity rather than a reusable shared secret.
- Just-in-time credentials that expire after the task, reducing the blast radius of compromise.
- Policy-as-code at request time, so approval depends on task context, data sensitivity, and destination service.
- Tool-level logging that records what the agent invoked, not just whether the HTTP request succeeded.
This is where the CSA MAESTRO agentic AI threat modeling framework and the MITRE ATLAS adversarial AI threat matrix become useful: both help teams model how autonomous behaviour changes the control plane. NHIMG’s OWASP NHI Top 10 also reflects the reality that secrets, tokens, and delegated access must be governed as high-value runtime assets, not static configuration.
These controls tend to break down when legacy services only support long-lived API keys or when an agent must span multiple vendors that do not expose consistent policy hooks.
Common Variations and Edge Cases
Tighter control often increases orchestration overhead, requiring organisations to balance blast-radius reduction against developer friction and latency. That tradeoff becomes most visible in mixed environments where some tools are modern and policy-aware while others still depend on static bearer tokens or coarse gateway rules. There is no universal standard for this yet, so current guidance suggests layering controls rather than waiting for a single perfect platform.
For low-risk read-only agents, some teams accept narrower gateway enforcement and stronger monitoring. For high-risk workflows such as payments, code changes, or customer-data retrieval, best practice is evolving toward session-scoped authorisation, step-up approval, and explicit tool allowlists. The important distinction is that an agent is not a human user with a stable job function. It is a goal-driven workload that can pivot unexpectedly, which makes static RBAC insufficient on its own.
For additional implementation examples, NHIMG’s Gemini AI Breach and CoPhish OAuth Token Theft via Copilot Studio show how prompt injection and token abuse can defeat controls that stop at the gateway.
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, OWASP Non-Human Identity Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A03 | Covers agent misuse of tools and unsafe autonomous actions. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Addresses credential lifecycle risks that gateways cannot solve alone. |
| CSA MAESTRO | TRM-02 | Maps to threat modeling for agentic workflows and control-plane gaps. |
| NIST AI RMF | Supports runtime governance and accountability for autonomous AI behavior. | |
| NIST Zero Trust (SP 800-207) | SA-4 | Zero trust is relevant because agents should be continuously evaluated. |
Continuously authenticate agent workloads and re-evaluate access on every sensitive request.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 24, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
