AI agents increase NHI sprawl because every agent often needs multiple credentials, tool connections, and delegated permissions to operate. That multiplies the number of identities, tokens, and review points without creating human-style accountability. Teams should expect sprawl to rise unless agent creation, scope, and retirement are governed as tightly as production workloads.
Why Traditional IAM Breaks Down for Autonomous AI Agents
AI agents increase NHI sprawl because they are not static users with fixed workflows. An agent can call tools, chain requests, spawn sub-tasks, and request new access as its goal changes, which means a single deployment may need several secrets, service accounts, scoped tokens, and delegation paths. That creates more identities to issue, review, rotate, and retire, while also making RBAC less predictive than it is for human users.
The key issue is the autonomous and goal-driven nature of the workload. Traditional IAM assumes access patterns are known ahead of time, but agents behave dynamically and often opportunistically. Best practice is evolving toward intent-based authorisation and real-time policy evaluation, where access is granted for the specific task at hand rather than a broad role. NHI Management Group guidance on the OWASP NHI Top 10 and external standards such as the OWASP Agentic AI Top 10 both point to the same operational reality: agentic systems expand identity surfaces faster than most teams can govern them. In practice, many security teams discover this only after an agent has already been connected to production tools and delegated more access than its original design intended.
How Agentic Workloads Create Identity Sprawl in Practice
Agent sprawl is usually created by convenience. Teams stand up an agent with one API key, then add another credential for retrieval, another for ticketing, another for code execution, and another for a human approval path. Over time, those pieces become a web of NHIs that are difficult to inventory, because each token or workload identity may have a different owner, expiry, rotation rule, and trust boundary.
The control problem is not just volume. It is also volatility. Current guidance suggests that agents should use workload identity as the primary identity primitive, with short-lived credentials issued just in time and revoked automatically when the task ends. That reduces the value of long-lived secrets and limits the blast radius if the agent is compromised. This is aligned with the governance approach described in the Ultimate Guide to NHIs and the NIST AI Risk Management Framework, which both emphasise lifecycle control, accountability, and measured risk treatment.
Practitioners should expect the following patterns:
- Agents need multiple tool grants because they do more than retrieve data.
- Delegated permissions multiply when agents call other agents or services.
- Secret sprawl grows when teams store tokens in code, config, or pipelines.
- Review pressure rises because each tool connection becomes a separate decision point.
NHIMG research shows that NHIs already outnumber human identities by 25x to 50x in modern enterprises, and the same scaling effect becomes sharper when every agent is treated as a mini-orchestrator. These controls tend to break down when agents are allowed to compose new tool chains in production because the resulting access graph changes faster than manual review can keep up.
Where Agent Governance Needs to Be Tightest
Tighter credential scoping often increases operational overhead, requiring organisations to balance security against deployment speed. That tradeoff matters because some agentic use cases genuinely need rapid iteration, but there is no universal standard yet for how much autonomy should be granted before additional controls are mandatory.
For high-risk workflows, current practice is to combine JIT credential provisioning, ephemeral secrets, and policy-as-code so that authorisation is evaluated at request time, not at build time. That means the agent proves what it is through workload identity, then receives only the minimum access needed for the current intent. The approach is consistent with the CSA MAESTRO agentic AI threat modeling framework and the NIST Cybersecurity Framework 2.0, both of which favour continuous governance over one-time trust decisions.
Two edge cases are especially important. First, multi-agent pipelines can hide sprawl because each agent may appear small, while the combined system accumulates broad effective privilege. Second, environments with legacy secrets managers or static service accounts often cannot support short-lived delegation cleanly, so teams keep adding exceptions until the original control model collapses. NHI Management Group has seen that the moment an organisation treats an agent like a human user, sprawl accelerates and offboarding becomes an afterthought.
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 | Agent autonomy and tool chaining drive identity sprawl and excess access. |
| CSA MAESTRO | MAESTRO frames agentic workflows, trust boundaries, and runtime governance. | |
| NIST AI RMF | GOVERN | AI RMF GOVERN addresses accountability for autonomous system behaviour. |
Model each agent flow, define trust boundaries, and require runtime policy checks before tool use.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on May 16, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
