Agentic AI identity sprawl is outpacing enterprise governance in 2026

At a glance

What this is: This article argues that agentic AI should be treated as a new class of non-human identity because agents now take actions, hold credentials, and expand identity sprawl across enterprise systems.

Why it matters: IAM and NHI teams need to govern agentic AI through visibility, lifecycle control, and least privilege because these systems can create unmanaged access paths faster than traditional reviews can absorb.

Context

Agentic AI now behaves like an identity problem, not just an automation problem. These systems can provision access, trigger workflows, and interact with third-party applications, which means they create new non-human identity relationships inside SaaS and cloud estates.

The governance gap is straightforward: organisations can discover AI features easily, but they often cannot see which agents exist, what credentials they rely on, or how far their access reaches. That makes identity visibility the starting point for any defensible control model.

Once agents are treated as non-human identities, the relevant questions become lifecycle, ownership, and privilege scope. The article's core argument is that identity security is now the control plane for agentic AI because software is operating with authority inside the enterprise.

Key questions

Q: How should security teams govern AI agent identities in SaaS environments?

A: Treat each agent as a distinct non-human identity with its own lifecycle, access scope, and revocation path. Catalogue the service accounts, tokens, and application permissions behind each agent, then review what the agent can do in each SaaS system. Governance should focus on delegated actions, not just whether the agent can authenticate.

Q: Why does Agentic AI dramatically increase identity sprawl?

A: Agentic AI systems autonomously request and use multiple NHIs for task execution — often creating new identities dynamically. Where a traditional application uses one or two service accounts, a single agent executing a complex workflow may create and consume dozens of NHIs. Multiply across hundreds of concurrent agent instances and the identity creation rate becomes extraordinary. This scale creates conditions for compromise propagating through interconnected agent identities.

Q: What breaks when agentic AI is allowed to act with embedded credentials?

A: The control problem changes from isolated secret protection to governed runtime access. Embedded credentials let agents reach SaaS applications, internal systems, or code execution paths without the usual visibility into who owns the access, what it can reach, or when it should be revoked. The result is hidden privilege accumulation.

Q: When should organisations prioritise visibility over advanced guardrails for agentic AI?

A: Visibility should come first whenever agents are being connected faster than the organisation can inventory them. If the business cannot name the agents, their owners, and their connected systems, more advanced policy layers will rest on unknown identity relationships.

Technical breakdown

Why agentic AI becomes an identity, not just a tool

Agentic AI differs from assistive AI because it can initiate actions across systems rather than merely respond to prompts. In this model, the agent may hold API keys, OAuth tokens, or service accounts that let it authenticate to SaaS and cloud platforms. That makes the agent a non-human identity in operational terms. The security consequence is that the access path is no longer tied to a person sitting at a keyboard, but to software that can execute decisions, call services, and move data across boundaries.

Practical implication: inventory agents as identities, not applications, and assign governance ownership before they spread across business workflows.

How identity sprawl emerges through SaaS and cloud connections

Agentic systems are easy for business teams to connect to enterprise tools, often in minutes. Each connection can create a new token, service account, or delegated permission set, and those artefacts may never be centrally registered. Over time, that produces identity sprawl: many small, distributed non-human identities with unclear ownership and overlapping access. The problem is not just quantity. It is fragmentation, because no single control plane can explain which agent has access to which system, or why that access still exists.

Practical implication: continuously discover and reconcile all agent-created identities against approved systems, owners, and entitlements.

Why manipulated inputs matter when the identity is allowed to act

The article highlights a subtle attack path: an attacker may not need to break authentication if they can influence the input an agent receives. If a malicious instruction is embedded in a document, email, or external source, the agent may treat it as legitimate context and perform allowed actions with harmful outcomes. That makes the issue one of authority misuse rather than outright login failure. Logs may show a valid identity and an authorised action, while the real compromise sits in the decision context that guided the action.

Practical implication: monitor the data and content sources that can steer agent behaviour, not just the credentials the agent uses.

Threat narrative

Attacker objective: The attacker seeks to use an agent's authorised access path and trusted context to trigger harmful actions without having to defeat authentication outright.

1. Entry occurs when business teams connect agentic AI to SaaS platforms, cloud services, and internal systems through newly created tokens, service accounts, or API keys.
2. Credential access is replaced by credential issuance at scale, because each new agent relationship creates another identity able to authenticate and operate across connected services.
3. Escalation happens when manipulated instructions or downstream automation cause the agent to take actions beyond the intended business context while still appearing technically authorised.
4. Impact is enterprise-wide identity sprawl, opaque access paths, and actions executed by software actors that security teams cannot easily trace back to a clear owner.

Breaches seen in the wild

• Salesloft OAuth token breach: hackers stole OAuth tokens to access Salesforce data via Salesloft.
• Klue OAuth Supply Chain Breach: OAuth tokens compromised in Klue integration breach affecting 700+ organisations via Salesforce data access chain.

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

Agentic AI turns identity sprawl into a governance defect, not just an inventory problem: once software can provision access and act across systems, the question is no longer whether the tool exists but whether the organisation can govern the identity it has become. Static access models were built for bounded tools and known operators, not for software that creates new permission edges as it runs. The practitioner conclusion is that visibility must be treated as a control prerequisite, not a reporting nice-to-have.

Least privilege is no longer a provisioning-time decision when the actor can decide at runtime: the old assumption is that access can be defined up front because the request path is known in advance. That assumption fails when the agent selects actions, consults different services, and chains tools during execution. The implication is that identity governance has to account for runtime decision-making, not only for what was granted at creation.

Manipulated context is now an identity-security issue because the authorised identity may still be the wrong actor: the article shows that an agent can authenticate correctly and still be steered into unintended behaviour by hostile input. That collapses the comfortable split between authentication success and security outcome. The practitioner conclusion is that governance must extend beyond credentials to the content and context that shape agent decisions.

Ephemeral trust debt: every newly created agent token, service account, or delegated permission adds short-term convenience while increasing long-term governance debt if it is not discovered, owned, and retired. That debt accumulates silently across SaaS and cloud environments because the lifecycle of the identity is shorter than the lifecycle of the review process. The practitioner conclusion is that lifecycle discipline has to keep pace with deployment speed.

Zero trust only holds for agentic AI when identities are continuously discoverable and bounded: zero trust does not mean every action is blocked, it means every actor is continuously verified and scoped. For agentic systems, that requires knowing which non-human identities exist, what they can reach, and how far their access has expanded over time. The practitioner conclusion is that identity visibility now underpins zero trust enforcement for autonomous software actors.

From our research library:

• 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
• Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
• Read next: Agentic AI Identity Guide

What this signals

The governance challenge is not whether agentic AI will be adopted, but whether organisations can keep identity records ahead of deployment speed. When new agents create tokens, service accounts, and delegated permissions in minutes, conventional review cycles become too slow to explain current access.

Ephemeral trust debt: every agent-created credential looks temporary when it is issued, but it becomes governance debt if no one tracks ownership, purpose, and retirement. That is the practical boundary teams now need to watch, especially as deployment becomes self-service.

Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey. That gap suggests identity visibility, not policy abstraction, will determine whether agentic AI remains governable.

For practitioners

• Inventory agentic AI as non-human identities Classify each agent, token, and service account by business owner, system owner, and intended task scope so governance can follow the identity lifecycle.
• Map every agent-to-system relationship Document which SaaS platforms, cloud services, and data stores each agent can reach, then reconcile those links against approved access records.
• Apply least privilege to runtime access Limit agent permissions to the narrowest task scope possible and remove broad standing access that is not required for the current workflow.
• Continuously rediscover shadow AI Run recurring discovery for unmanaged agents, unsanctioned integrations, and newly created credentials so the inventory stays current as teams deploy faster.
• Review the content sources that influence agents Treat inbound documents, messages, and external content as governance inputs when they can steer agent actions, because malicious instructions can exploit valid access.

Key takeaways

• Agentic AI creates a non-human identity problem because software can now hold credentials, call systems, and trigger workflows inside the enterprise.
• The main failure mode is identity sprawl, where new agents quietly accumulate access across SaaS and cloud services without central ownership or lifecycle control.
• Teams need continuous discovery, runtime privilege scoping, and governance over the content that influences agent decisions.

Key terms

• Agentic AI Identity: The complete set of credentials, permissions, and governance controls applied to an autonomous AI agent, covering authentication, authorisation, action logging, and access revocation. Distinct from traditional NHI because agent identities are often ephemeral, delegated, and multi-hop.
• Identity Sprawl: Identity sprawl is the uncontrolled growth of identities, entitlements, and credentials across an environment. For NHIs, it usually appears when automation creates accounts faster than governance teams can inventory, review, and remove them. The result is hidden access, weak accountability, and a wider attack surface.
• 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.
• Delegated Access: Delegated access is permission granted to one identity to act on behalf of another user, service, or system. In NHI environments, this usually appears in OAuth-connected apps and automation tooling. It is powerful, but it must be tightly scoped and reviewed because it can persist long after the original business need ends.

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 building or maturing an IAM programme, it is worth exploring.