AI agent credential sprawl is outpacing enterprise IAM controls

At a glance

What this is: This article argues that AI agents are multiplying non-human credentials faster than enterprise IAM can centrally govern them.

Why it matters: IAM, PAM, and identity governance teams need to treat AI agents as a credential sprawl problem, not just a new automation use case, because unmanaged machine-scale access expands exposure across the entire identity stack.

By the numbers:

• Over one-third of employees, 38%, have successfully accessed a prior employer’s account, data, or applications after leaving the company, according to 1Password.
• 1 in 4 employees has used AI applications that were not approved by their company, according to 1Password.
• In 2024, GitGuardian found 23,770,171 new hardcoded secrets added to public GitHub repositories, a 25% surge over the previous year, according to 1Password.

Context

Credential sprawl is the spread of secrets, tokens, keys, and accounts beyond centralized visibility and control. In enterprise environments, that now includes API keys, SSH keys, service accounts, SSO tokens, and developer secrets that can be created, copied, or retained outside the normal identity governance workflow.

AI agents make this worse because they create and use credentials at machine scale while operating in ways security teams may not observe directly. That shifts the problem from isolated credential leakage to a governance model that must account for non-human identities, delegated access, and identity drift across tools, workflows, and environments.

Key questions

Q: What breaks when AI-driven workflows use standing credentials instead of ephemeral access?

A: Standing credentials make AI workflows harder to contain because any compromise can persist until the secret is rotated or revoked. This increases exposure to lateral movement, unintended data access, and abuse of tool permissions. Ephemeral access reduces that window by issuing permissions only when needed, then removing them once the task or session ends.

Q: Why do overpermissive app secrets create a bigger risk when AI agents are involved?

A: Overpermissive app secrets create bigger risk because an agent can discover and use them at machine speed, then chain the resulting access into other systems. The danger is not only the secret itself, but the number of downstream services it can unlock before a human can intervene. Narrow scope and storage hygiene both matter.

Q: What are the signs that credential sprawl is getting out of control?

A: Look for departments using separate logins, repeated password reuse, shadow AI adoption, and accounts that cannot be tied back to provisioning records. If teams can name the app but not the owner or lifecycle state, credential governance is already fragmenting.

Q: How should teams govern AI agents that inherit human access rights?

A: Teams should treat inherited access as temporary and bounded to a specific task, owner, and expiry. The key is to govern the delegation chain, not just the agent itself, because the original human authority can persist far beyond the session that created it. If revocation cannot outrun execution, the governance model is already behind the risk.

Technical breakdown

Why AI agents turn credential sprawl into an identity governance problem

AI agents do not merely consume credentials. They can create, reuse, and propagate them across tools and workflows in ways that multiply the number of active non-human identities in the environment. That matters because each API key, OAuth token, service account, or embedded secret has its own issuance, scope, rotation, and revocation logic. When those identities are generated or reused outside centralized governance, the IAM team loses the ability to tie access back to a stable human owner or a clear lifecycle event. The result is credential sprawl with weak attribution and weak control boundaries.

Practical implication: Treat agent-issued and agent-used credentials as governed identity objects with explicit ownership, scope, and lifecycle.

Why traditional IAM and PAM controls miss agent-scale access

Traditional IAM and PAM were built around interactive human access: login, approval, session, review, and offboarding. AI agents break that pattern because they can authenticate programmatically, operate continuously, and persist long after the immediate task is done. That means access review cadences and session-centric monitoring often arrive too late to matter. The control issue is not that IAM is absent, but that the control model assumes access behaves like a person. When access is machine-paced and replicated at runtime, the review model becomes structurally misaligned with the identity behaviour.

Practical implication: Reassess where session-based oversight and periodic access reviews cannot observe or constrain agent activity in time.

How shadow AI and developer workflows expand secrets exposure

AI coding tools, browser extensions, agent platforms, and vibecoding workflows expand the places where secrets can be introduced, copied, or exposed. That includes hardcoded credentials in code, plaintext secrets in collaboration tools, and misconfigured databases or repositories that leak tokens at scale. The technical issue is not just leakage, but uncontrolled credential distribution across development and collaboration surfaces that were never designed to be authoritative identity repositories. Once those credentials are embedded in workflows, they become difficult to discover, classify, and revoke quickly enough to prevent reuse.

Practical implication: Extend secret discovery and revocation beyond code repositories into collaboration tools, endpoints, and AI-assisted development paths.

Threat narrative

Attacker objective: The attacker wants durable, hard-to-trace access through exposed or overprivileged non-human credentials.

1. Entry begins when AI tools, developer workflows, or misconfigured applications introduce credentials into places outside centralized control, including code, chat, and agent platforms.
2. Credential exposure follows as API keys, service accounts, OAuth tokens, and other machine secrets are copied, hardcoded, or retained beyond the intended use case.
3. Privilege abuse occurs when those credentials are overprivileged, long-lived, or reused by agents in ways security teams cannot reliably distinguish from normal activity.
4. Impact is achieved when unmanaged credentials provide unauthorized access paths into applications, data, or automation workflows, expanding the breach surface beyond the original identity boundary.

Breaches seen in the wild

• AI agent retail card theft campaign 2026: AI agents breached 27+ retailers for about $25 each, used cloud keys and a Secrets Manager dump, and stole 600,000+ payment cards.
• Okta support system breach 2023: A support service account credential saved in a personal Google profile let attackers take HAR files and hijack five Okta customers' sessions.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Credential sprawl has become an AI control-plane problem, not just a secrets hygiene problem. AI agents do not simply inherit access, they multiply it by creating and reusing machine credentials across workflows. That changes the governance unit from the individual secret to the delegated access path, because the real risk is now uncontrolled identity propagation. Practitioners should stop treating secrets as isolated artefacts and start treating them as part of the execution model.

Access review processes assume a credential stays stable long enough to be certified. That assumption breaks when agents create, duplicate, and consume credentials at machine speed, often within a single workflow. The implication is not just more review debt, but a failed governance premise: periodic certification cannot govern identities whose useful life is shorter than the review cycle. Teams must rethink what is reviewable versus what must be controlled at issuance time.

Ephemeral credential trust debt is the new hidden liability in agentic environments. The more organisations rely on temporary-seeming access without true lifecycle governance, the more they accumulate undocumented trust relationships that outlive the original task. This is especially visible when AI tools sit outside SSO and when developers embed secrets in collaboration and build systems. The practical conclusion is that unmanaged trust, not just leaked material, is what compounds over time.

Unified identity governance now has to span humans, agents, and machine secrets together. This article shows that the separation between human IAM, NHI governance, and developer secret management is no longer operationally useful. The same workflow can involve a person, an AI agent, and an API key, each with different control expectations but one shared blast radius. Practitioners should align governance around delegated access paths, not product silos.

OWASP-NHI and Zero Trust both apply here, but neither is sufficient alone. OWASP-NHI captures the lifecycle problems of overprivileged, long-lived, and poorly audited machine credentials, while Zero Trust emphasizes continuous verification and constrained access. The gap is that AI agents can still create new trust relationships faster than either control family was originally designed to absorb. Teams need policy, inventory, and runtime governance that can keep pace with machine-scale identity churn.

From our research library:

• Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
• Read next: Ultimate Guide to NHIs — Key Challenges and Risks

What this signals

Credential sprawl now has an agentic acceleration curve. When AI systems create and reuse credentials faster than teams can inventory them, the governance problem shifts from isolated secrets to unbounded identity propagation. That is why access review, vaulting, and offboarding need to be tied to agent runtime behaviour, not just to user lifecycle events.

Our 2026 infrastructure identity data suggests the privilege gap is already material. 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege. For practitioners, that means AI access policy is already drifting away from the least-privilege baseline that governs human identities.

For practitioners

• Map agent credential paths Inventory where AI tools, agent platforms, and developer workflows create or reuse API keys, service accounts, tokens, and embedded secrets. Trace each path back to an owner, a system of record, and a revocation method so hidden machine identities do not escape governance.
• Separate human and machine governance Stop relying on human access review cadences to control AI agent access. Define agent-specific issuance, approval, expiry, and revocation rules so non-human identities are governed at the point of creation instead of after the fact.
• Harden developer secret handling Extend secret discovery into code repositories, collaboration tools, and local developer environments where hardcoded or shared secrets commonly appear. Remove plaintext secrets from chat, tickets, and build artifacts before they become durable access paths.
• Constrain agent runtime access Deliver credentials to agents only when a task requires them and scope those credentials to the minimum viable permissions. Short-lived access should be tied to task boundaries, with automatic revocation when the workflow ends.
• Audit shadow AI adoption Identify unsanctioned AI applications and agent platforms already in use by employees and developers. Unapproved tools often create unmanaged credentials before security teams have evaluated the access model, which leaves a blind spot in both IAM and PAM.

Key takeaways

• AI agents are widening credential sprawl because they create and reuse machine credentials at a scale that traditional IAM workflows do not model well.
• The article ties that risk to broad visibility gaps, unmanaged AI adoption, and overprivileged non-human identities that persist beyond their original task.
• Practitioners need to govern agent access at issuance, not only at review, and extend secrets controls into developer and collaboration surfaces.

Key terms

• Credential Sprawl: Credential sprawl is the uncontrolled accumulation of machine secrets, keys, and tokens across systems, teams, and environments. It usually starts with a single use case and ends with overlapping permissions, unclear ownership, and a larger attack surface than the organisation expected.
• Non-Human Identity (NHI): A digital identity assigned to a non-human entity such as a software application, service account, API key, bot, machine, or AI agent that enables it to authenticate and interact with systems without direct human involvement. NHIs now outnumber human identities in most enterprises by 25 to 50 times.
• Shadow AI: AI agents, copilots, or connected tools operating without full visibility or governance from security teams. Shadow AI becomes an identity problem when those systems authenticate with unmanaged tokens, service accounts, or OAuth apps that can reach production resources.
• Secrets Leakage: Secrets leakage is the exposure of credentials such as API keys, tokens, or certificates in places where they can be discovered and reused. The risk is not just disclosure, but unauthorized authentication that turns a coding or pipeline mistake into active access.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.