Firewalls only validate source and destination addresses, while IAM validates what an identity is allowed to do. Neither proves that the requesting entity is the authorized workload. That gap matters when agents are compromised, redirected, or operating through shared infrastructure. Without connection-level identity assurance, a legitimate credential can still drive illegitimate traffic and access.
Why the control gap exists between network trust and identity trust
Firewalls and IAM answer different security questions, so each leaves a blind spot that matters for AI agents. A firewall sees where traffic comes from and where it goes, while IAM decides whether an identity has permission. Neither one, by itself, proves that the entity making the request is the same workload that should be using that access.
That distinction matters because agentic systems can be proxied, redirected, or shared across infrastructure. If the connection is legitimate but the workload behind it is not, the control plane still sees an allowed source, or an allowed identity, while the actual actor may be something else.
In practice, this is why connection-level identity assurance becomes a separate requirement. Without it, security teams may have valid network policy and valid access policy yet still fail to bind a request to the correct agent instance, runtime, or trust context.
How AI agents create a gap neither firewall nor IAM closes
AI agents are not just users with tokens. They can operate through orchestration layers, shared services, delegated credentials, or tool APIs that make the request path more indirect than classic human access. That extra indirection breaks the simple assumption that the source IP or the authenticated principal is enough to explain who is really acting.
Firewalls are strong at coarse containment, but they do not evaluate intent, workload identity, or delegated authority inside an allowed channel. IAM is strong at permissioning, but it typically answers “is this principal allowed?” rather than “is this the authorized workload making the call right now?”
For that reason, AI agent security often needs an additional layer that ties request, principal, and runtime context together. Zero Trust for AI Agents is useful here because it frames the problem as verifying the agent, principal, and request, not just the network route or the standing role.
The same issue shows up when agent permissions are too broad or too durable. AI Agent Authorisation Guide is relevant because per-action policy and just-in-time access reduce the chance that a legitimate credential can be reused for an illegitimate action.
For readers wanting the broader identity model behind this, Agentic AI Identity Guide explains why delegation, registration, authentication, and retirement all matter once an autonomous system can act on its own behalf.
What practitioners should add beyond firewall and IAM
The missing control is not “more network rules” or “more roles.” It is tighter proof that the request is bound to the correct workload, tool invocation, and session context. That usually means separating policy for connectivity, identity, and action, then making sure each one is checked at the point where it can still stop misuse.
What to verify: confirm whether the agent is using a shared host, a shared token, or a delegated session that can outlive the workload that received it. If the answer is yes, the access path is already weaker than the firewall or IAM screen suggests.
Decision rule: if a credential can authorize traffic from multiple runtime contexts, treat the request as under-bound and add request-time identity checks, short-lived authorization, or stronger workload attestation before allowing sensitive actions.
What good looks like: the agent’s identity, the request origin, and the permitted action all line up at execution time, and a reused credential cannot silently move between workloads or environments.
Practitioner takeaway: firewall policy and IAM are necessary controls, but AI agent security fails when either one is used as a substitute for request-level workload assurance.
Risk and Threat Considerations
When an agent can borrow a valid path or a valid credential, the main risk is silent abuse of legitimate access. That can look like ordinary traffic to the firewall and ordinary permission use to IAM, even though the real actor has changed or the runtime has been hijacked.
Failure mechanism: the defender trusts source address and principal separately, but neither control binds the request to the authorized agent instance, so redirected, shared, or compromised execution still passes control checks.
Impact: an attacker or rogue process can drive legitimate access through an illegitimate workload, which increases the chance of unauthorized data access, tool misuse, and lateral movement without an obvious policy violation.
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 OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5 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 | ASI03 — Identity & Privilege Abuse | Agent security gaps arise when identity and action are not tightly bound. |
| Recommendation — Enforce per-action authorization and bound privilege for AI agents. | ||
| OWASP Non-Human Identity Top 10 | NHI-04 — Insecure Authentication | The question centers on why authentication and network trust do not prove the right workload. |
| NHI-05 — Overprivileged NHI | Legitimate credentials can still cause illegitimate traffic when privileges are too broad. | |
| Recommendation — Use stronger workload authentication to bind requests to the correct non-human actor. Reduce standing access and scope NHI permissions to the minimum needed. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Identification and Authentication (Service Organizations) | AI agents and workloads need authentication beyond network location to prove the caller. |
| AC-6 — Least Privilege | IAM gaps matter when credentials can be reused for more access than the task needs. | |
| Recommendation — Require service-to-service authentication that proves the requesting workload. Limit agent privileges to the smallest set needed for each action. | ||
| NIST Zero Trust (SP 800-207) | Zero Trust Architecture | The topic is about verifying the requester and request, not trusting the network path. |
| Recommendation — Treat each agent request as untrusted until continuously verified. | ||
Practitioner Guidance
What to prioritize: focus first on the request paths where AI agents call tools, APIs, or internal services, because that is where firewall and IAM blind spots become operationally meaningful. If the same credential, service, or host can serve multiple agent contexts, the control boundary is too weak.
What to measure: track whether privileged or sensitive agent actions are attributable to a unique workload context, not just to an IP address or account name. If attribution stops at identity or network origin, you do not yet have enough evidence to trust the action.
Common mistake: treating a successful login or allowed connection as proof that the right agent is acting. For AI systems, that is only proof that the channel is open, not that the requester is the intended runtime.
Practitioner takeaway: the practical goal is to make agent actions bound, observable, and revocable at execution time, so a valid credential cannot become a free pass for the wrong workload.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on September 29, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
