Why do GenAI systems complicate identity and access control?

GenAI systems complicate identity and access control because they can turn a single user request into a chain of delegated actions across plugins, APIs, and service accounts. Access decisions are no longer tied only to the user at login. They must also account for what the model can do at runtime and what authority it inherits downstream.

Why This Matters for Security Teams

GenAI changes identity from a login event into a runtime trust problem. A prompt can trigger tool use, API calls, retrieval, code execution, or delegated actions through service accounts, which means access is no longer controlled by the user alone. Security teams that rely on static RBAC often discover that the model inherits more authority than intended, especially when secrets are reused across workflows.

This is why current guidance increasingly points toward workload identity, just-in-time access, and policy evaluation at request time rather than broad standing permissions. The issue is not just “who asked,” but “what is the model trying to do, with which tools, under what context, and with what downstream reach.” NHI Management Group’s research on the Ultimate Guide to NHIs shows how pervasive over-privilege and weak visibility already are in non-human environments, which GenAI tends to amplify.

Security teams also have to factor in secret exposure. NHI governance failures rarely stay isolated, and the State of Secrets in AppSec highlights how sensitive tokens and credentials remain hard to control across software pipelines. In practice, many security teams encounter AI-driven privilege sprawl only after a model has already chained tools, inherited credentials, and touched systems that were never meant to be reachable.

How It Works in Practice

GenAI identity control works best when the model is treated as a distinct workload, not as a human user with a chat interface. That means assigning a workload identity to the agent or application component, then issuing narrowly scoped credentials per task. Standards are still evolving, but the practical direction is clear: bind authorization to runtime context, not just to a preassigned role.

For example, a customer-support agent might be allowed to read case records, but only when a ticket is open, only for the current tenant, and only for the duration of the task. A developer assistant might be allowed to open a pull request or query logs, but not deploy code unless an approved workflow step is present. That kind of decision is better enforced with policy-as-code and real-time evaluation, as reflected in the NIST AI 600-1 GenAI Profile and the OWASP Non-Human Identity Top 10.

Common implementation patterns include:

• Using short-lived tokens or ephemeral secrets instead of long-lived API keys.
• Issuing JIT access only when the agent starts a bounded task.
• Separating model identity from user identity so downstream actions remain auditable.
• Logging tool calls, policy decisions, and secret retrieval events as first-class security telemetry.

For deeper lifecycle guidance, NHI Management Group’s Lifecycle Processes for Managing NHIs is especially relevant because AI systems often fail where credentials are provisioned too early, retained too long, or revoked too late. These controls tend to break down when the agent can spawn sub-agents, call unvetted plugins, or operate across multiple tenants because the authorization chain becomes hard to bound in real time.

Common Variations and Edge Cases

Tighter runtime authorization often increases operational overhead, requiring organisations to balance security with latency, developer velocity, and observability. There is no universal standard for this yet, so best practice is evolving rather than settled.

One common edge case is retrieval-augmented generation. If the model only summarizes data, some teams under-estimate the need for identity controls, but the retrieval layer can still expose sensitive resources if the backing service account is too broad. Another is multi-agent orchestration, where a planner agent delegates to worker agents. In those environments, the identity problem is not just the model itself, but the trust boundary between agents, tools, and approval gates.

Another frequent failure mode appears when organisations keep using human-centric patterns such as shared accounts, persistent tokens, or broad RBAC groups for autonomous workflows. That approach may appear simpler, but it weakens attribution and makes revocation unreliable. NHI Management Group’s Top 10 NHI Issues reinforces that excessive privilege, poor visibility, and weak rotation are already persistent problems in non-human estates, long before GenAI enters the picture.

For governance teams, the practical takeaway is to classify GenAI systems by action capability, not by model label. A read-only assistant, a code-writing agent, and a transaction-executing agent should not share the same identity model or approval path. That distinction becomes even more important in high-trust environments, where the NIST Cybersecurity Framework 2.0 still expects clear access governance, but does not by itself solve autonomous delegation.

Related resources from NHI Mgmt Group

• Why is identity such a critical factor in securing AI agent systems?
• 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?
• Why do AI agents change the way IAM programmes think about access control?