AI agents are becoming workloads, and IAM must adapt

At a glance

What this is: Keyfactor frames AI agents as a workload identity problem, arguing that existing security models need to account for machine-style access, lifecycle, and trust boundaries.

Why it matters: This matters because IAM, PAM, and NHI programmes will need to decide whether AI agents are governed like workloads, humans, or something that crosses both patterns.

👉 Read Keyfactor's analysis of why AI agents are becoming a new workload class

Context

AI agents are increasingly being treated as workloads, which means the core question is no longer whether they can access tools, but how their identities are provisioned, monitored, and retired. For identity teams, that shifts the problem into machine identity governance, with the same pressure points seen in service accounts, certificates, and workload credentials.

The practical gap is that many IAM programmes still assume a stable human operator or a static non-human credential. Once an AI system can act with its own runtime discretion, identity governance has to separate authentication, authorisation, and lifecycle ownership much more cleanly than most current programmes do. That is the framing lens this post uses for the topic.

Key questions

Q: How should security teams govern AI agents as enterprise workloads?

A: Security teams should govern AI agents as workload identities with explicit ownership, approved access, lifecycle controls, and runtime monitoring. That means defining who is accountable, which systems the agent may reach, how its credentials are rotated, and when access is revoked. If the agent can act independently, those controls must exist before production use, not after deployment.

Q: Why do AI agents complicate least privilege in IAM programmes?

A: AI agents complicate least privilege because their action paths can change during runtime. Unlike a fixed service account used for one application flow, an agent may choose tools, sequence actions, and continue execution based on context. That makes static entitlements less reliable as a description of real access, so teams need runtime boundaries and telemetry as well as provisioning controls.

Q: What breaks when AI agents are managed like ordinary automation?

A: Ordinary automation assumes the workflow is predetermined, but AI agents can make decisions at runtime. When teams manage them like scripts, they miss scope drift, overbroad tool access, and hidden privilege accumulation. The result is a governance blind spot where the organisation can authorise the agent but cannot clearly prove what it is able to do in production.

Q: Who is accountable when an AI agent exceeds its intended scope?

A: Accountability should sit with the business owner of the agent, the identity owner for its credentials, and the control owner for the systems it can reach. If those roles are unclear, incidents become hard to investigate and access review evidence becomes weak. Frameworks that expect a single human operator often fail here, so joint ownership is essential.

Technical breakdown

Why AI agent identity looks like workload identity

AI agents that operate inside enterprise systems need identities that can be authenticated, authorised, and governed independently of any human user who configured them. In practice, that makes them closer to workloads than to employees, because the security question is about runtime access to APIs, data stores, and toolchains. The key difference is that agent behaviour can change at execution time, so identity controls must account for dynamic interaction patterns rather than only predefined application calls.

Practical implication: treat agent identity as a workload governance problem first, then map the specific access paths it needs.

Runtime authorisation and tool access for AI agents

An AI agent can become a governance issue when it can choose tools, combine actions, and continue execution without a human approval step. That creates a different authorisation pattern from ordinary automation, because privilege is not only granted at onboarding, it is exercised repeatedly during the session. The security challenge is to define what the agent may reach, what it may chain together, and where human oversight still exists in the decision path.

Practical implication: define explicit tool and data boundaries for each agent, then validate them against actual runtime behaviour.

Lifecycle control for AI agent credentials

Lifecycle management matters because agent identities often depend on secrets, tokens, certificates, or service-style access that can outlive the business need that created them. If those credentials are not tied to a clear owner and retirement process, the agent keeps its access long after the use case changes. That is the same structural failure seen in weak machine identity governance, but with higher operational risk because the agent may continue acting autonomously on stale privileges.

Practical implication: attach ownership, rotation, and offboarding to every agent identity before it is allowed into production.

NHI Mgmt Group analysis

AI agents are forcing identity teams to treat autonomous software as a workload governance problem, not a feature flag. Once the actor can act at runtime, the programme has to account for its identity, access path, and revocation model as if it were another production workload. That places AI agents inside the same governance perimeter as service accounts, API credentials, and certificate-backed systems. Practitioners should classify the identity before they classify the technology.

Runtime discretion changes the meaning of least privilege. Traditional least privilege assumes the access set can be defined before execution and then reviewed after the fact. For AI agents, that assumption weakens because action selection may change during the session, especially when tool use is part of the agent's design. Practitioners should re-evaluate whether entitlement models can describe what an agent is actually able to do once it starts chaining actions.

Machine identity controls become the baseline, but they are no longer sufficient on their own. AI agents inherit the same problems as other non-human identities, including secret handling, inventory, and ownership gaps. They also introduce higher variance in behaviour because the access path is not always fixed in advance. The field should stop asking whether agents are just another NHI and instead ask which NHI controls still hold when runtime decision-making enters the picture.

Identity governance for AI agents will converge with lifecycle governance faster than many teams expect. An agent that can be created quickly, used briefly, and discarded without an offboarding process creates the same governance debt as any unmanaged machine identity, only faster. That makes ownership, retirement, and recertification the points where programmes will either regain control or accumulate hidden access. Practitioners should build lifecycle checks into the first production deployment, not after the first incident.

Agentic identity introduces a new named concept: runtime governance gap. This is the space between a credential being issued and the organisation being able to prove what the agent did with it. The gap matters because static governance artefacts rarely capture runtime discretion, chained tool use, or session-level behaviour. Practitioners should assume that conventional access review evidence will be incomplete unless runtime telemetry is part of the control model.

From our research:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), 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 broader control lens, see OWASP Agentic AI Top 10 for the risk patterns most likely to turn agent behaviour into governance failure.

What this signals

Runtime governance gap: AI agent programmes will need controls that capture what the agent actually did, not only what was approved at provisioning. That means runtime telemetry, ownership metadata, and clear retirement paths become part of the identity control plane rather than optional extras.

With 92% of organisations saying governing AI agents is critical but only 44% having implemented any policies, the market signal is clear: policy maturity is lagging behind deployment speed. Teams that wait for perfect standards will inherit the operational debt first.

Security and identity teams should expect agent governance to converge with workload identity management and lifecycle governance. If the agent can create real business action without stable human pacing, then access review cadences alone will not be enough to establish control.

For practitioners

• Define AI agent identities as governed workloads Assign each agent a clear owner, purpose, and approved access profile before production use. Store those details alongside the credential or workload registration so the identity can be reviewed, rotated, and removed with the same discipline as other machine identities.
• Separate tool permissions from human approvals Document which tools an agent may call autonomously and which actions still require explicit human approval. Review the difference between allowed access and allowed execution so runtime discretion does not quietly expand beyond the original design.
• Tie agent credentials to lifecycle controls Require rotation, expiry, and offboarding for every agent token, key, or certificate. If the agent no longer has an active business purpose, its access should be removed with the same urgency used for abandoned workload credentials.
• Instrument runtime behaviour before scale-out Log which systems the agent touches, what data it requests, and which actions it chains during a session. That telemetry creates the evidence needed for recertification, investigation, and ownership checks when the access pattern changes.

Key takeaways

• AI agents should be governed as workload identities because runtime discretion changes the access problem.
• When 80% of organisations already see agents acting beyond scope, the issue is no longer theoretical or experimental.
• Ownership, runtime visibility, and lifecycle offboarding are the controls that determine whether agent identity remains governable.

Key terms

• AI agent identity: The identity assigned to an AI system that can act on its own at runtime. It covers the credentials, ownership, and access boundaries needed for the agent to operate safely in enterprise systems, including the rules that govern what it may reach and when that access should end.
• Runtime governance gap: The period between granting an identity access and being able to verify what it actually did with that access. In AI agent and machine identity programmes, this gap becomes a control issue when behaviour can change during execution and standard review evidence does not capture the full session.
• Workload identity: A non-human identity used by software, services, or automated systems to prove who they are to other systems. For AI agents, workload identity needs the same lifecycle discipline as other machine identities, but with tighter monitoring because runtime behaviour may shift during execution.
• Lifecycle offboarding: The process of removing access, credentials, and ownership when an identity is no longer needed. For agents and other machine identities, offboarding is the point where hidden access is prevented from persisting after the business purpose has ended.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security 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 governance in your organisation, it is worth exploring.

This post draws on content published by Keyfactor: AI Agents Are the New Workload: What That Means for Security. Read the original.