Machine identities and agentic AI are reshaping trust boundaries

At a glance

What this is: Delinea argues that machine identities and agentic AI are expanding the trust boundary beyond human access into credentials, workflows, and autonomous execution.

Why it matters: IAM teams now have to govern service accounts, secrets, and AI agents with the same discipline once reserved for human access, or risk privilege sprawl and lateral movement at machine speed.

👉 Read Delinea's analysis of machine identities and agentic AI trust boundaries

Context

Machine identity security is the discipline of controlling non-human access such as service accounts, API keys, tokens, certificates, bots, and AI agents. Delinea’s central point is that identity governance is shifting from a human-only model to one that must account for machine-speed authentication, workflow execution, and decision-making.

The practical gap is visibility. Many enterprises cannot confidently inventory all non-human identities, map their privileges, or tell which credentials are tied to AI agents and automation frameworks. That leaves over-privilege, dormant access, and embedded secrets as governance problems rather than isolated hygiene issues.

Key questions

Q: How should security teams govern machine identities in cloud and AI environments?

A: Security teams should treat machine identities as first-class governed assets. That means discovering every service account, token, key, and certificate, assigning ownership, scoping privileges tightly, and reviewing lifecycle changes continuously. If the team cannot say who owns an identity, what it can reach, and why it still exists, the control model is already incomplete.

Q: Why do machine identities increase lateral movement risk?

A: Machine identities often carry persistent access that outlives the original task, system, or owner. When one credential is exposed or over-privileged, attackers can reuse it across cloud services, APIs, and automation layers without needing to break human authentication. The result is faster lateral movement and a much larger blast radius than a single user account usually creates.

Q: What do teams get wrong about just-in-time access for non-human identities?

A: Teams often assume just-in-time access is enough on its own. In practice, JIT only reduces exposure if the identity inventory is accurate, the workflow is enforced automatically, and standing access is removed everywhere else. If credentials still exist in code, logs, or legacy automation, the JIT model becomes a partial control rather than a boundary.

Q: How can organisations tell whether machine identity governance is working?

A: Look for fewer standing credentials, complete ownership metadata, and reliable visibility into which non-human identities can access sensitive systems. Effective governance also shows up as faster secret rotation, fewer orphaned accounts, and clearer separation between human access and machine execution. If review teams still cannot answer what an identity can do, governance is not mature enough.

Technical breakdown

Machine identity sprawl and credential blind spots

Machine identity sprawl occurs when cloud services, CI/CD systems, APIs, and automation create more credentials than teams can reliably track. Unlike human accounts, these identities often do not follow joiner-mover-leaver processes cleanly, and they can persist long after the workflow that created them has changed. The technical failure is not just scale, but weak inventory and classification across environments, which makes entitlement review incomplete and secrets exposure harder to detect.

Practical implication: build continuous discovery for keys, tokens, certificates, and service accounts before attempting deeper governance.

Agentic AI and replicated privilege at machine speed

Agentic AI changes the identity model because the agent can log in, call tools, execute workflows, and make operational decisions with the privileges it inherits. Delinea’s framing points to replicated privilege, where one human workflow can produce multiple machine identities with overlapping entitlements. That expands the blast radius of a single compromised secret or poorly scoped token, especially when agents can act faster than review cycles or manual response processes.

Practical implication: treat AI agents as governed identities with explicit privilege boundaries, not as convenience layers over existing accounts.

Least privilege, JIT access, and the problem of standing access

The article’s governance message is that machines do not object to excess privilege, so over-provisioning tends to become permanent. Just-in-time access and secret vaulting are useful because they remove persistent exposure windows, but their value depends on accurate identity mapping and automated enforcement. Without that, standing credentials remain available for abuse even when the underlying workload no longer needs them.

Practical implication: remove standing machine credentials wherever workflows can tolerate ephemeral access and policy enforcement.

Threat narrative

Attacker objective: The attacker wants to turn one compromised machine identity into broad, fast-moving access across enterprise systems and data.

1. Entry begins when attackers obtain exposed machine credentials such as API keys, tokens, or cloud access keys from code, logs, or mismanaged secret stores.
2. Escalation follows when those credentials grant broader-than-needed access, allowing abuse of service accounts, lateral movement across cloud environments, or unauthorized AI agent actions.
3. Impact occurs when the compromised non-human identity is used to access sensitive data, execute workflows, or expand the blast radius across connected systems.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• Sisense breach — unauthorized GitLab access led to exfiltration of access tokens, API keys and certificates.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Machine identity governance is now a core identity programme, not a side channel. Once service accounts, API keys, tokens, and AI agents can all execute business workflows, identity security can no longer treat them as implementation details. The governance model has to cover discovery, ownership, privilege scope, and lifecycle control across every non-human actor. Practitioners should expect machine identity inventory to become a board-relevant control surface.

Replicated privilege at machine speed is the new identity blast radius. The article describes a condition where one human workflow can spawn multiple active machine identities with overlapping entitlements. That means compromise is no longer limited to one account, because the effective blast radius is multiplied by automation and orchestration. The implication is that entitlement review must account for how privileges replicate, not just how they were originally assigned.

Standing machine access is a governance debt, not a convenience. Machines rarely protest excess privilege, so over-provisioning tends to survive longer than it should. This is why persistent credentials become a structural weakness rather than a temporary exception. Practitioners should treat unused or oversized non-human access as a lifecycle failure with direct breach potential.

Identity and agent are converging into one control problem. Delinea’s own framing shows that AI agents are not simply another workload class when they can log in, act, and make decisions. The named concept here is identity boundary blur: the point at which teams can no longer distinguish between an identity that authenticates and an agent that executes. That boundary collapse forces governance teams to rethink how trust, ownership, and accountability are assigned.

Discovery is the control that determines whether every other control can work. If teams cannot find all machine identities, then least privilege, rotation, vaulting, and monitoring remain partial controls. This is especially true in hybrid environments where secrets sit in code, cloud services, and automation layers at once. Practitioners should treat inventory quality as the precondition for every downstream identity decision.

From our research:

• 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
• For a deeper governance lens, compare that deployment pressure with OWASP Agentic Applications Top 10 to map agent risk to control priorities.

What this signals

Identity boundary blur: machine identities and AI agents are converging into the same governance surface, which means many IAM programmes will need to merge discovery, entitlement control, and lifecycle oversight across what used to be separate teams. The practical signal is that identity scope reviews should now include automation paths, embedded secrets, and agent execution rights, not only human entitlements.

With 52% of companies able to track and audit the data their AI agents access, the other 48% are operating with a compliance and investigation gap that can easily become a breach gap. That is why visibility has to be treated as a control outcome, not a reporting feature. Teams that cannot explain agent data access will struggle to defend it.

The next programme pivot is from static account management to dynamic trust management across human, machine, and agent identities. That makes privilege drift, secret sprawl, and ownership ambiguity the leading indicators to watch. Teams that connect those signals to lifecycle governance will be better placed to scale AI adoption without widening the attack surface.

For practitioners

• Inventory every non-human identity continuously Scan for service accounts, API keys, tokens, certificates, bots, and AI agents across cloud, SaaS, code repositories, and automation pipelines. Tie each credential to an owner, system, and business purpose so that orphaned access can be removed quickly.
• Eliminate standing privilege where workloads allow it Replace persistent machine credentials with just-in-time access, short-lived tokens, and vault-managed secret retrieval. Prioritise identities that can reach sensitive systems or that appear in high-frequency automation paths.
• Classify AI agents as governed identities Document which agents can authenticate, which tools they can call, and which decisions they can make without human approval. Set separate boundaries for read, write, and execution actions so agent privileges do not inherit human scope by default.
• Track privilege drift as a lifecycle issue Review changes in service account scope, secret exposure, and AI agent entitlements as part of regular governance cycles. When access outlives the workload or use case, remove it rather than waiting for the next incident.

Key takeaways

• Machine identities and AI agents now behave like governed identities, not just infrastructure components.
• Over-provisioned non-human access multiplies blast radius, and one compromised credential can spread fast across connected systems.
• Visibility and lifecycle control are the decisive controls because least privilege cannot work on identities teams cannot find.

Key terms

• Machine Identity: A machine identity is a non-human credentialed entity that can authenticate and access systems on behalf of a service, application, workload, or automation process. In practice, it includes service accounts, API keys, tokens, and certificates that must be owned, scoped, rotated, and retired like any other identity asset.
• Agentic AI: Agentic AI is AI that can take actions, call tools, and execute workflows with some degree of operational independence. For identity teams, the key issue is not the model itself but the privileges, tokens, and approval boundaries attached to the agent's runtime behaviour.
• Replicated Privilege: Replicated privilege is the multiplication of access when one human workflow or automation path spawns several active machine identities with overlapping entitlements. It increases attack surface because compromise is no longer limited to a single account, but can spread across linked identities and systems.
• Identity Boundary Blur: Identity boundary blur is the point where teams can no longer cleanly separate an identity that authenticates from an agent that executes actions. In that state, governance must cover both credential control and runtime behaviour, because trust is being exercised by software that both possesses access and uses it.

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.

This post draws on content published by Delinea: The rise of machine identities and agentic AI: Securing trust in the next era of digital autonomy. Read the original.