TL;DR: Machine identities and AI agents now authenticate, access data, and execute workflows at machine speed, expanding enterprise attack surface and privilege sprawl, according to Delinea. The governance problem is no longer just access control, but whether identity programmes can keep trust boundaries visible as autonomy and entitlement multiply.
At a glance
What this is: This is a Delinea analysis of how machine identities and agentic AI are expanding enterprise trust boundaries through credentialed non-human actors, over-privilege, and visibility gaps.
Why it matters: It matters because IAM, IGA, and PAM teams now have to govern machine, service, and AI-agent identities that can multiply access faster than human review cycles can keep up.
Context
Machine identities are non-human credentials such as service accounts, API keys, tokens, and certificates. When AI agents inherit those credentials and act on them independently, identity security stops being only a human access problem and becomes a governance problem across humans, machines, and software-driven actors.
Delinea argues that enterprises are already struggling to answer basic questions about inventory, privilege, and ownership across those actors. The practical issue is not only that non-human identities are numerous, but that their entitlements can be created, copied, and left behind faster than teams can review them.
Key questions
Q: How can teams govern machine identities and AI agents in access reviews?
A: Teams should assign ownership, define review cadence, and include machine identities and AI agents in the same certification logic as human access, but with role-appropriate approvers. If a non-human identity can act on sensitive data, it needs a lifecycle owner and a removal path just like any other privileged account.
Q: Why do machine identities and agentic AI complicate traditional identity security programmes?
A: They complicate programmes because the number of non-human actors grows faster than manual governance processes can track. Access may be ephemeral, delegated, or embedded in workflows, which weakens assumptions built around human users. Security teams need policies for lifecycle control, privilege boundaries, and continuous verification across both human and machine identities.
Q: How do security teams know if machine identity governance is working?
A: Look for fewer standing accounts, faster onboarding of automation workflows, auditable role approvals, and visible retention of access records after logout. If teams still depend on manual tracking to explain machine access, governance is only partially effective. Working machine identity governance should reduce both operational overhead and review friction.
A: Organisations should define AI agents as governed identities, not informal automations. That means assigning ownership, scoping access tightly, reviewing delegation paths, and setting lifecycle controls for provisioning and revocation. If an agent can act, delegate, or chain requests, it needs policy boundaries and monitoring comparable to other high-risk identities.
Technical breakdown
Why machine identities become a trust boundary problem
A machine identity is any non-human credentialed entity that authenticates to systems and consumes or transfers access on behalf of a workload, integration, or application. The security problem is not the identity itself, but the trust placed in credentials that are often created automatically and then left with broad, persistent reach. In modern environments, service accounts, tokens, and API keys can accumulate privileges across SaaS, cloud, and on-prem systems without a single owner watching the full lifecycle. That makes trust boundaries harder to define, because access is no longer granted to one person for one task, but to a chain of software actors that can outlive the original use case. Practical implication: inventory and ownership must be tied to the credential lifecycle, not just the application that first requested access.
Practical implication: map every machine credential to an accountable owner and lifecycle state before it becomes an ungoverned trust boundary.
How agentic AI changes identity and privilege
Agentic AI is different from conventional automation because it can select actions, use tools, and execute workflows with far more runtime independence. That matters for identity governance because the actor behind the request is no longer a static application behaving predictably within a predefined workflow. Instead, the same credential may be used across multiple tasks, tools, and timing decisions that are not fully knowable at provisioning time. This creates replicated privilege at machine speed, where one human account can spawn several credentialed digital actors with overlapping reach. The governance challenge is not merely more identities, but less predictability about how entitlements will be exercised. Practical implication: treat agent access as a governed execution surface, not as a normal application account.
Practical implication: govern agent access as a dynamic execution surface, not as a conventional application account.
Why visibility and over-privilege are the real control gap
The article’s strongest operational point is that discovery comes before control. Organisations cannot right-size privileges, rotate secrets, or remove dormant access if they do not know where machine identities and AI-linked credentials exist. Over-privilege becomes persistent because machines do not object when access is excessive, and teams often keep extra permissions in place so workflows do not fail. That produces privilege sprawl across environments, with hidden credentials embedded in code, stored insecurely, or linked to automation frameworks no one actively owns. The result is a control gap, not just a hygiene issue: governance breaks down when entitlement review, ownership, and enforcement happen after the access pattern has already multiplied. Practical implication: build continuous discovery and entitlement classification into identity operations before remediation can be effective.
Practical implication: make continuous discovery and entitlement classification prerequisites for any least-privilege programme.
Threat narrative
Attacker objective: The attacker wants to turn one compromised non-human credential into broad, fast-moving access across enterprise systems and workflows.
- Entry begins when attackers obtain compromised machine credentials, which the article identifies as a common initial access vector in major breaches.
- Escalation follows when over-provisioned service accounts or AI-linked credentials carry privileges broader than the original use case required.
- Lateral movement becomes possible because a single compromised agent or API key can move across environments at machine speed.
- Impact is the rapid expansion of access to sensitive data, systems, and workflows before human teams can detect or contain the misuse.
Breaches seen in the wild
- Dropbox Sign breach 2024: A compromised back-end service account gave attackers Dropbox Sign customer data, including API keys, OAuth tokens and MFA information.
- Microsoft Midnight Blizzard breach: Midnight Blizzard (APT29) exploited legacy test account without MFA to breach Microsoft.
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
Machine identities are now first-class trust actors, not backend implementation details. Delinea’s framing is directionally correct: once service accounts, tokens, and AI-linked credentials can independently access data and systems, identity governance stops being a human-only discipline. The operational problem is not volume alone, but that these actors can exercise trust across multiple environments without a single human review point. Practitioners should treat machine identity governance as core IAM, not as an adjacent engineering task.
Replicated privilege is the new identity sprawl problem. A single human account that spawns multiple AI-driven or automated actors can multiply effective access far beyond what provisioning teams intended. That changes the meaning of least privilege because entitlement is no longer expressed only in one account’s static scope. The practical conclusion is that privilege measurement has to follow execution paths, not just account records.
Discovery is the control plane for NHI governance. If organisations cannot enumerate credentials, keys, tokens, and agents, every other control becomes partial and reactive. This is especially true where secrets are embedded in code or linked to automation frameworks that lack clear ownership. The field should stop treating discovery as a preliminary task and start treating it as the governing condition for every later control decision.
Agentic AI exposes the limit of person-centric access review. Access review processes assume a stable identity and a reviewable entitlement history. When a system can act semi-independently and multiply privileges under a human account, the review artefact no longer reflects the real runtime behaviour. The implication is that governance must shift toward runtime visibility, bounded execution, and continuously validated trust relationships.
Identity and agent are converging into one security problem. The article’s most useful named concept is the trust boundary blur, where identity, automation, and action become inseparable at runtime. That blur means security leaders can no longer cleanly separate workload identity from autonomous behaviour when evaluating risk. Practitioners should adjust programme design so that human, machine, and agent governance are assessed as a connected control surface.
From our research library:
- NHIs outnumber human identities by 25x to 50x in modern enterprises, according to the Ultimate Guide to NHIs.
- Read next: Agentic AI Identity Maturity Model
What this signals
Trust boundary blur: When machine identities, APIs, and AI agents all exercise the same enterprise access paths, the old separation between human user, workload, and automation no longer holds. Governance has to follow the credentialed actor at runtime, not the application label attached at provisioning.
Access review alone cannot keep pace with identities that can be created, copied, and retired faster than manual certification cycles. Continuous discovery, ownership, and privilege validation become the practical signals that an NHI programme is still governing actual runtime trust rather than just recording accounts.
For practitioners
- Discover every credentialed actor Inventory service accounts, API keys, tokens, certificates, and AI-linked identities across SaaS, cloud, and on-prem environments. Assign an owner and usage purpose to each one so dormant or orphaned access becomes visible.
- Classify replicated privilege paths Identify where one human account or workflow can spawn multiple non-human identities with overlapping access. Prioritise paths that reach sensitive systems or can be reused across environments without fresh review.
- Remove standing credentials from high-risk workflows Replace persistent access with short-lived issuance where the workflow can tolerate it, and rotate secrets automatically when long-lived credentials are unavoidable.
- Automate governance for machine and agent access Enforce policy checks in CI/CD and identity workflows so new credentials, entitlements, and agent permissions are validated before they can be used in production.
- Monitor privilege drift continuously Track anomalous changes in permission scope, access frequency, and cross-environment movement so that machine identities and agents do not quietly expand beyond intended boundaries.
Key takeaways
- Machine identities and agentic AI turn identity governance into a runtime trust problem, not just an access administration problem.
- The article points to a scale issue in which NHIs outnumber human identities by 25x to 50x in modern enterprises, which increases the room for privilege sprawl.
- Continuous discovery, privilege right-sizing, and automated governance are the controls that matter when access can multiply at machine speed.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 — Secret Leakage | The article repeatedly highlights embedded secrets, tokens, and exposed credentials across machine identities. |
| NHI-05 — Overprivileged NHI | Over-provisioning and replicated privilege are central risks in the article's governance discussion. | |
| NHI-07 — Long-Lived Secrets | The article recommends rotating secrets and removing standing credentials from machine and agent workflows. | |
| Recommendation — Scan for exposed machine credentials and remove leaked secrets before they expand trust boundaries. Right-size non-human access scopes and eliminate standing excess privilege from machine identities. Replace persistent secrets with shorter-lived credentials wherever the workflow can support it. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The article is fundamentally about governing entitlements for machines, APIs, and AI agents. |
| Recommendation — Apply access permission governance to every non-human identity and review entitlement scope continuously. | ||
| MITRE ATT&CK | TA0006; TA0008 — Credential Access; Lateral Movement | The article ties compromised machine credentials to initial access and cross-environment movement. |
| Recommendation — Map compromised machine credentials to credential access and lateral movement detection logic. | ||
Key terms
- Machine Identity: The digital identity of a machine, device, or workload, such as a server, container, or VM, used to authenticate it within a network. Sometimes used interchangeably with NHI, though NHI is the broader category.
- Agentic AI: Autonomous AI systems capable of planning, deciding, and taking actions, including calling APIs, writing code, and orchestrating other agents, with minimal human oversight. Agentic AI introduces new NHI risks as agents must authenticate to external services.
- Trust Boundary: A trust boundary is the point where one system’s authority should stop and another system’s authority should begin. For internal automation, weak trust boundaries let monitoring, remediation, and execution share privileges that should have remained separate.
- Privilege Sprawl: Privilege sprawl is the accumulation of access rights beyond what is needed for a task or role. It often develops quietly across service accounts, tokens, and delegated access paths, which makes it a major source of hidden risk in both workforce IAM and NHI governance.
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.
Published by the NHIMG editorial team on June 24, 2026.
Updated on October 11, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org