Runtime identity control for non-human identities in the machine-driven enterprise

At a glance

What this is: This is an independent analysis of how privileged access management is shifting from human admins to machine identities, with the key finding that NHIs and AI agents now drive most privileged activity.

Why it matters: It matters because IAM, PAM, and IGA teams must govern continuous machine access, not just human sessions, or they will miss the identities that now create the largest privilege exposure.

By the numbers:

• Non-human identities now outnumber human identities by as much as 144 to 1.
• the total number of NHIs continues to grow by more than 44% year over year.
• AI service credential leaks increased 81% in a single year.

👉 Read SSH Communications Security's whitepaper on what AI agents and non-human identities mean for PAM

Context

Privileged access management was built around human administrators, where access could be reviewed, approved, and removed on a predictable schedule. That model breaks down when non-human identities such as service accounts, APIs, cloud workloads, automation platforms, and AI agents authenticate continuously and operate at machine speed. The primary identity governance problem is no longer whether a person has the right access, but whether the organisation can inventory, constrain, and audit every machine identity with the same discipline.

The article argues that machine-driven environments have become the dominant source of privileged activity in many organisations. That changes PAM from a periodic control problem into a runtime governance problem, where access decisions must follow the identity, the workload context, and the action itself. For IAM, PAM, and NHI teams, this is a typical rather than edge-case shift in modern cloud and AI estates.

Key questions

Q: How should security teams govern non-human identities that have persistent access?

A: Security teams should treat every non-human identity as a managed asset with an owner, an explicit purpose, a scoped privilege set, and a defined offboarding path. Persistent access should be replaced with time-bound or task-bound access wherever possible, and every credential should be traceable to the system or workflow it supports.

Q: Why do non-human identities create more risk than many human accounts?

A: NHIs often outnumber human users, have broader permissions, and operate with less day-to-day review. That combination increases the chance that a single exposed secret or delegated token can be reused across systems without detection. The risk is not just compromise, but silent persistence inside automated workflows and third-party integrations.

Q: What breaks when organisations keep using standing privileges for machine identities?

A: Standing privilege turns NHIs into persistent trust anchors. If the credential is embedded in code, reused across environments, or left valid after the workload changes, the access outlives the business purpose. That creates a durable attack path and makes it difficult to prove that the identity is still needed.

Q: Who is accountable when an AI agent or workflow executes privileged actions under a forged identity?

A: Accountability sits with the organisation that allowed authority to flow without revalidation. If the identity proof is stale, delegated, or implicit, then the governance failure is architectural, not operational. Frameworks such as Zero Trust and NHI governance both point to the same issue: trust must be continuously asserted, not assumed.

Technical breakdown

Why standing privilege fails for non-human identities

Standing privilege assumes access is assigned to a known subject that behaves in a stable, human-paced way. NHIs do not fit that model. Service accounts, API keys, certificates, and workload identities are often created dynamically, reused across systems, and left active long after the task that justified them has changed. That creates persistent trust paths that are difficult to see in inventory and even harder to retire consistently. Once those identities are embedded in pipelines or cloud workflows, the access model outlives the original operational need.

Practical implication: teams need lifecycle controls that track creation, usage, and retirement for every non-human credential, not just periodic access reviews.

Continuous authorization for workloads and AI agents

Traditional authentication answers a one-time question, then assumes the session remains valid until logout. Continuous authorization is different. It re-evaluates the identity, target resource, and policy during the lifetime of the action. That matters for AI agents and automation platforms because the privilege decision may need to change while activity is in progress. In practice, runtime identity control combines short-lived credentials, context-aware policy, and telemetry so that access is governed during execution rather than only at login.

Practical implication: organisations should design policy checks around session state and runtime context, not only around initial sign-in or token issuance.

How machine identities accumulate hidden privilege

Machine identities often inherit permissions through pipelines, cloud templates, and application dependencies, then silently accumulate more access as environments expand. Because they are not managed like people, their entitlements are rarely recertified with the same discipline. Over time, this creates privilege drift, where an identity retains access far beyond its current function. In NHI governance terms, the risk is not only credential exposure. It is also the persistence of unnecessary authority across systems, regions, and cloud services.

Practical implication: review effective permissions, not just assigned permissions, and remove inherited access that no longer matches operational need.

NHI Mgmt Group analysis

Runtime identity control is now the right frame for PAM because static privilege assumptions no longer match machine behaviour. The article shows that workloads, APIs, automation systems, and AI agents authenticate continuously rather than episodically. That means PAM is no longer just about preventing misuse by people. It is about governing identities that operate constantly, often without a human operator in the loop. Security teams should treat runtime evaluation as the governing model for privileged machine access.

Standing access was designed for identities whose activity windows were observable and reviewable. That assumption fails when an identity is created, used, and retired as part of automated execution. The implication is not merely that controls need to be tighter. It is that the review cadence itself breaks when privilege exists only for the duration of machine execution, leaving no durable state for classic access governance to catch.

Non-human identity governance is becoming the dominant PAM problem, not a side case. The article’s scale claims point to a machine-driven enterprise where NHIs outnumber people and generate the majority of privileged activity. That shifts the discipline from protecting a few high-risk admin accounts to governing a broad, fast-changing identity estate. Practitioners should expect NHI scope, not human user count, to define PAM programme complexity.

AI agents turn authorisation into an execution problem, not just an entitlement problem. Traditional identity controls assume the subject asks for access and then uses it within a known boundary. An AI agent can choose actions, invoke APIs, and sequence work at runtime. That creates an operational question about what the identity is allowed to do while it is already acting. IAM teams should re-evaluate policies that assume intent is fixed at provisioning time.

Runtime Identity Control is a useful named concept because it captures what the old PAM model misses. The issue is not only privileged access, but privilege governance during active machine behaviour across cloud workloads, pipelines, and AI agents. That framing helps separate lifecycle governance from execution governance. Practitioners should use it to decide where the current PAM model stops and continuous authorization begins.

From our research:

• 88.5% of organisations acknowledge that their non-human IAM practices lag behind or are merely on par with their human identity and access management efforts, according to The 2024 Non-Human Identity Security Report.
• 59.8% of organisations see value in a solution that simplifies non-human access management and introduces dynamic ephemeral credentials.
• For a broader breach-pattern view, 52 NHI Breaches Analysis shows how identity gaps become incident paths.

What this signals

Standing trust is becoming the wrong default for machine access. As organisations expand cloud workloads and AI-driven automation, the governance problem shifts from authenticating identities to constraining them continuously. With 88.5% of organisations already saying their non-human IAM lags human IAM, the next programme gap will be runtime control, not password policy.

Privilege will increasingly be measured by execution context, not by account count. That matters for PAM teams because the same service account can become low-risk or high-risk depending on where it runs, what it can invoke, and whether its access is time-bound. The practical signal to watch is whether your identity governance model can explain access at the moment of action, not only at provisioning.

Runtime Identity Control is the right lens for estates where automation, workloads, and AI agents all authenticate on behalf of business processes. Practitioners should prepare for ownership models, recertification flows, and audit trails that operate at machine speed rather than human review cadence.

For practitioners

• Inventory every non-human identity class Build a current register of service accounts, API keys, certificates, workload identities, automation platforms, and AI agents, then map each one to an owner, purpose, and expiry or review rule. Use the inventory to expose identities that exist outside normal lifecycle controls.
• Replace standing privilege with short-lived access Move privileged machine access toward just-in-time or identity-bound sessions so credentials are valid only for the task window. Prioritise the highest-risk identities first, especially those embedded in CI/CD pipelines and cloud operations.
• Re-certify effective machine permissions Review what each NHI can actually reach across cloud, Kubernetes, and SaaS systems, not just what was originally assigned. Remove inherited or stale entitlements that remain active after application, environment, or vendor changes.
• Add runtime policy checks to agentic workflows For AI agents and automation platforms, place policy evaluation at execution time so access can be constrained by target system, context, and task. Do not rely on a one-time approval to govern an action chain that can continue autonomously.

Key takeaways

• Machine identities have become the dominant privileged access problem, and human-centric PAM controls no longer cover the full attack surface.
• The scale gap is already visible in the data, with non-human IAM maturity lagging human IAM and machine credentials growing faster than most governance programmes can absorb.
• Teams that want to reduce exposure need runtime governance, short-lived access, and effective-permission reviews built around how NHIs actually operate.

Key terms

• 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.
• Runtime Identity: Runtime identity is the practice of making identity and authorization decisions at the moment an action occurs. For agents and workloads, it means access is validated against live context, not only against the identity state set during onboarding or provisioning. That makes accountability and scope enforcement possible inside fast-moving workflows.
• Standing Privilege: Standing privilege is access that remains active even when no immediate task requires it. For NHI programmes, it is a common failure mode because long-lived credentials and persistent roles create unnecessary exposure. Reducing standing privilege usually means tighter expiry, on-demand access, and clearer review of who or what still needs access.
• Privilege Drift: Privilege drift is the gradual gap between the permissions an identity was meant to have and the permissions it actually retains. In AI agent environments, drift grows quickly because roles are reused, tasks change, and lifecycle reviews often lag behind deployment velocity.

What's in the full article

SSH Communications Security's full whitepaper covers the operational detail this post intentionally leaves for the source:

• Practical discussion of runtime identity control and how it differs from traditional PAM
• Detailed examples of how AI agents change authorisation decisions during active execution
• Operational framing for short-lived identities, policy-based access, and continuous monitoring
• The source article's broader whitepaper context for teams moving from concept to implementation

👉 SSH Communications Security's full whitepaper covers the shift from privileged access management to runtime identity control

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 identity security programme, it is worth exploring.