Start by treating the AI agent as a dedicated workload identity, not as an extension of the human user. Authenticate that identity at the identity provider, then enforce network policy so the agent can reach only explicitly approved services. The control objective is to separate identity validation from network reachability, which prevents broad east-west access and limits lateral movement across internal systems.
How identity-first connectivity changes the agent access model
Identity-first connectivity treats the agent as the thing you authorise first, then lets network policy follow from that identity decision. That matters because AI agents are not just callers of APIs, they are autonomous workload identities that can accumulate broad reach if connectivity is granted by network location alone. The practical goal is to make every tool or LLM call traceable to a specific agent principal and a specific allowed purpose.
The key design shift is to stop using “the agent is on an internal subnet” as the trust signal. Instead, bind the agent to a registered identity, authenticate that identity at the provider, and attach policy to the authenticated principal so only approved services are reachable. That separates who the agent is from where it sits on the network, which is the difference between controlled delegation and ambient east-west trust. Agentic AI Identity Guide is useful here because it frames the lifecycle of agent identity, including registration, authentication, delegation and retirement.
For teams implementing this pattern, the policy unit should be the agent, not the user who launched it. A human may approve the workflow, but the agent should still authenticate as its own workload identity and present that identity to internal services and LLM endpoints. That makes access reviews, incident response and revocation materially simpler, because the blast radius is attached to a specific agent identity rather than a shared human session.
What the connectivity path should look like in practice
In a workable design, the agent first authenticates to an identity provider, then receives only the credentials or assertions needed for downstream calls, then encounters network controls that allow access only to explicitly named services. This sequence keeps authentication, authorisation and network reachability as separate enforcement points rather than one blended trust decision. It is especially important when the agent needs both internal tools and external LLMs, because those destinations often sit behind different policy and logging stacks.
That separation is strongest when the agent has a dedicated service identity, short-lived credentials and per-action policy decisions. The agent should not inherit the human operator’s standing access, and it should not keep a reusable token that can roam across unrelated systems. AI Agent Authorisation Guide supports this model by focusing on least privilege, task-scoped access and human approval gates where the action is sensitive.
For the network layer, the useful question is not “can the agent reach the internal network?” but “which exact services must this identity reach to complete its task?” That usually leads to allowlists for specific internal tools, egress controls for approved model endpoints, and explicit denial for everything else. Zero Trust for AI Agents is the clearest companion because it aligns agent verification, policy enforcement and no-standing-privilege thinking.
Why this is safer than user-centric or subnet-centric access
User-centric access works poorly once the agent starts chaining tool calls, because the agent can become a proxy for actions the user never directly intended. Subnet-centric access is also weak, because anything on the segment can often talk to anything else until a later control blocks it. Identity-first connectivity narrows both problems by making the agent’s authority explicit and by confining network reach to the minimum required set of services.
This model also supports better containment when the agent is tricked, misconfigured or compromised. If the identity is bound to a narrow policy and the network only exposes approved destinations, then a bad prompt, a poisoned context or a stolen token does not automatically become lateral movement across internal systems. Multi-Agent and A2A Security Guide is relevant because it shows how authentication and containment become harder as agents begin delegating to other agents.
Identity-first connectivity also gives you a cleaner audit trail. When the agent’s identity is distinct from the user’s identity, you can answer who approved the workflow, which agent executed the call, which tool was reached and which policy allowed it. That is the difference between meaningful attribution and a generic “some internal service connected” log line.
Risk and Threat Considerations
Identity-first connectivity reduces the chance that an agent can turn a single approved action into broad internal reach. The main risk it addresses is overbroad east-west access, where a compromised or misused agent can pivot into tools, data stores or model endpoints that were never needed for the original task.
Failure mechanism: If the agent is treated as a generic extension of the user, or if network policy is wider than the agent’s task scope, the agent can inherit standing access to multiple services and use that trust path for lateral movement, token abuse or unintended tool invocation.
Impact: A single agent compromise can then expose internal tools, sensitive data flows or downstream LLM-integrated actions far beyond the original business use case, making containment and incident scoping much harder.
Practitioner Guidance
Decision rule: If the agent must reach a new internal service, make that a separate authorisation decision rather than a new route on the same broad network path. If the service cannot be tied to an explicit business task for that agent, do not add it.
What to measure: Track the number of services reachable by each agent identity, the number of denied connection attempts, and whether revocation of one agent leaves other automation unaffected. A good pattern is one agent, one bounded purpose, one narrow blast radius.
What practitioners underestimate: LLM access often becomes the easiest exception to overextend, because model endpoints feel “external” even when they are part of a sensitive workflow. The safer pattern is to treat model access and tool access with the same identity discipline and the same explicit allowlist logic.
Practitioner takeaway: Identity-first connectivity only works when identity, policy and network reachability stay separate in design and operations, so that authentication of the agent always comes before any trust in its path.
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 MITRE ATT&CK address the attack and risk surface, while NIST SP 800-53 Rev 5, NIST Zero Trust (SP 800-207) and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | AI agents need bounded identity and delegated authority for internal tool access. |
| ASI02 — Tool Misuse | Connectivity must prevent an agent from reaching unapproved tools or endpoints. | |
| ASI10 — Rogue Agents | Dedicated identities and narrow connectivity help contain unauthorized agent behavior. | |
| Recommendation — Enforce per-agent identity and least privilege for every tool and LLM call. Restrict agent tool and model access to explicitly approved destinations. Segment agent identities so unsafe or rogue behavior stays isolated. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Identification and Authentication (Non-Organizational Users) | Agentic workloads authenticate as non-organizational system actors. |
| AC-6 — Least Privilege | The model depends on limiting each agent to only the access it needs. | |
| SC-7 — Boundary Protection | Network policy must constrain which internal and external services an agent can reach. | |
| Recommendation — Use non-organizational authentication controls for each agent identity. Grant each agent only the minimum permissions needed for its task. Enforce boundary controls that allow only approved agent traffic. | ||
| NIST Zero Trust (SP 800-207) | N/A — Zero Trust Architecture | The question is fundamentally about verifying the agent and separating trust from reachability. |
| Recommendation — Apply zero trust principles so every agent request is explicitly verified. | ||
| NIST SP 800-63 | AAL2 — Authentication Assurance Level 2 | Agent access should rely on strong, verifiable authentication before downstream connectivity. |
| Recommendation — Require strong authentication before issuing agent access credentials. | ||
| MITRE ATT&CK | T1021 — Remote Services | Tightly controlling agent reachability reduces abuse of internal remote access paths. |
| Recommendation — Monitor and restrict internal remote service paths used by agents. | ||
Practitioner Guidance
What to prioritise: Issue each agent its own workload identity, then define a small set of allowed tool and model destinations for that identity. If the agent needs to call both internal tools and external LLMs, treat those as separate trust domains with separate policy and logging expectations.
What to verify: Confirm that the agent cannot authenticate with the human operator’s credentials, cannot reuse a broad shared token, and cannot reach any internal service that is not explicitly named in policy. If a service can be reached without an identity decision, the design is still network-first, not identity-first.
Common mistake: Teams often authenticate the agent once and then rely on network segmentation alone. That creates a false sense of control, because the segment may still be far broader than the agent’s actual task requires.
Practitioner takeaway: The right test is whether you can revoke or narrow one agent identity without disrupting other agents, users or workloads; if you cannot, the connectivity model is still too coarse.
Related resources from NHI Mgmt Group
- How should security teams implement an on-prem LLM gateway to control access across internal tools and AI agents?
- How should security teams govern AI agents that use OAuth access?
- How should security teams limit the risk from AI agents that have access to production systems?
- How should security teams govern AI agents that can access enterprise systems?
