Gen 3 PAM reframes privileged access for machines and AI

At a glance

What this is: This is Akeyless's argument that privileged access management must evolve into Gen 3 PAM for human, machine, and AI identities, with zero-standing privilege and ephemeral access at the centre.

Why it matters: It matters because IAM, PAM, and IGA teams now have to govern privileged access across identities that do not fit human-centric control models, especially in cloud and AI-enabled environments.

By the numbers:

• Machines and AI agents now represent up to 85% of identities in many environments.
• Over 50% of organizations have experienced a security incident tied to compromised machine credentials.
• 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job.
• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.

👉 Read Akeyless's analysis of Gen 3 PAM for human, machine, and AI identities

Context

Privileged access management is no longer just a human administrator problem. In cloud and AI-heavy environments, machines, workloads, APIs, service accounts, and AI agents now hold a large share of access, while persistent secrets and standing privilege continue to create the most exploitable control gap. Traditional PAM still assumes access is stable, human-paced, and easy to vault, but that assumption breaks when identities are dynamic and task-scoped.

A Gen 3 PAM model tries to replace that older assumption with ephemeral access, zero-standing privilege, and a unified view of humans, machines, and AI agents. The real issue for identity programmes is not whether access can be granted, but whether it can be made short-lived, attributable, and compatible with modern infrastructure without fragmenting governance across tools.

The article's starting position is typical of the market: most enterprises have moved faster into cloud and automation than they have into identity governance that matches those operating realities.

Key questions

Q: What breaks when privileged access still depends on standing secrets in cloud environments?

A: Standing secrets create a durable attack path because the credential remains usable long after the original task has ended. In cloud and automation-heavy environments, that means one leak can enable reuse, lateral movement, and hidden inheritance across services. The control failure is persistence, not just exposure.

Q: Why do machine and AI identities make traditional PAM governance harder?

A: Machine and AI identities do not follow human working patterns, so access can appear, be used, and disappear faster than review cycles can observe. That breaks governance models built around scheduled certification, manual approval, and durable entitlements. Teams need controls that scope access at runtime, not only at provisioning.

Q: How do security teams know whether zero-standing privilege is actually working?

A: Look for short-lived credentials, reduced secret reuse, fewer persistent privileged accounts, and audit trails that show access was issued and expired for a specific task. If access remains reusable after the task finishes, the organisation still has standing privilege in practice.

Q: What is the difference between secret rotation and zero-standing privilege?

A: Secret rotation changes credentials on a schedule, while zero-standing privilege tries to prevent durable privilege from existing in the first place. Rotation helps limit exposure after a secret exists, but it does not remove the underlying persistence problem. ZSP changes the access model, not just the refresh cadence.

Technical breakdown

Standing privilege in modern PAM environments

Standing privilege means access persists after the task that justified it has finished. In legacy PAM, that was manageable when privileged users were few, environments were static, and sessions were predictable. In cloud infrastructure, the same pattern turns into a durable attack surface because credentials can be copied, reused, or quietly inherited by services and pipelines. The risk is not only exposure, but persistence: once privilege exists by default, every compromise inherits that reach. Modern PAM therefore has to control privilege at the point of use, not only at the point of provisioning.

Practical implication: move high-risk accounts and secrets toward task-scoped access with enforced expiry rather than broad, always-on privilege.

Ephemeral secrets and secretless authentication

Ephemeral secrets are credentials issued for a narrow purpose and destroyed immediately after use. Secretless authentication goes further by reducing the need for long-lived credentials to exist at all, often through federation or short-lived token exchange. This matters because the attacker value of a stolen secret is tied to how long it stays valid and where it can be replayed. In distributed systems, the better control is not a stronger vault alone, but a design where access can be proven without exposing a durable secret in the first place.

Practical implication: prioritise federated, short-lived authentication paths for workloads and admins where replayable secrets create unnecessary risk.

Why AI agents strain human-centric PAM

AI agents change the access model because they can initiate actions across tools, data sources, and infrastructure during runtime. That makes static approval flows and human-only review assumptions brittle. If the control model assumes a person will request access, use it, and then hand it back, it misses the possibility that an agent can chain actions faster than the governance cycle can observe. The issue is not merely that AI systems use secrets. It is that the privilege lifecycle becomes machine-timed rather than human-timed, which changes how access must be scoped, logged, and retired.

Practical implication: treat AI agents as governed identities with explicit runtime boundaries, not as enhanced users inside legacy PAM workflows.

Threat narrative

Attacker objective: The attacker aims to turn one exposed privileged identity into durable reach across infrastructure, secrets, and automation paths.

1. entry via compromised machine credentials or exposed static secrets that can be reused across cloud and automation environments.
2. escalation through standing privilege that remains active beyond the original task and can be inherited by workloads, pipelines, or administrators.
3. impact through lateral movement, unauthorized infrastructure access, and the ability to persist inside modern distributed systems.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• 230M AWS environment compromise — 230M AWS environments compromised via exposed .env files with cloud credentials.

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

NHI Mgmt Group analysis

Gen 3 PAM is really a response to identity scope drift, not just credential hygiene. The article is right to move beyond vaulting and session monitoring, because the control problem has shifted from storing secrets safely to defining access correctly across humans, machines, and AI agents. That makes privileged access a lifecycle and governance issue, not a point-product feature. Practitioners should treat access scope as the primary design variable.

Standing privilege is the failure mode that legacy PAM keeps preserving. Gen 1 and Gen 2 models still assume access can remain available long enough to be useful and reviewed later, but that assumption breaks in cloud-native and autonomous environments. The result is a persistent privilege pool that attackers can reuse, chain, or inherit. Identity teams should read this as a governance problem where default persistence is the bug, not the convenience.

Zero-standing privilege is now a control boundary for both NHI and AI agent governance. The same logic that limits service account blast radius also applies when AI systems trigger infrastructure actions. The article correctly connects privileged access to secrets, certificates, and workload identity because those layers now converge operationally. The implication is that PAM, secrets management, and workload identity can no longer be governed as separate domains.

Zero-standing privilege was designed for access that lasts long enough to be reviewed. That assumption fails when machine and AI identities can obtain, use, and discard privilege inside a task or session window. The implication is that access review models built around durable entitlements must be rethought for actors whose privilege lifecycle is shorter than the governance cycle.

Unified identity governance is becoming the competitive baseline for infrastructure security. When privileged access, secrets, PKI, and workload identity are handled as disconnected controls, the organisation creates hidden privilege paths that no single audit trail can explain. The market is moving toward control planes that collapse those fragments into one policy and one record of truth. Practitioners should re-evaluate whether their current PAM stack can actually govern the identities that run production.

From our research:

• 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
• 52% of respondents see AI security decision-making power shifting toward platform and infrastructure teams rather than the executive suite.
• Forward pivot: If AI access is already being granted more generously than human access, the next control question is whether governance can follow the identity lifecycle fast enough. See Top 10 NHI Issues for the broader control gaps that emerge when machine identities scale faster than oversight.

What this signals

Gen 3 PAM will be judged by whether it collapses access duration, not just whether it stores secrets more safely. For identity programmes, that means the operational question is whether privilege can be made task-scoped across humans, workloads, and AI systems without creating a new sprawl of exceptions. The control target is narrower blast radius, not broader vault coverage.

Identity teams should expect privileged access and workload identity to converge in the same governance conversation. Once machines and AI agents hold most of the active access in infrastructure, a separate PAM strategy for human administrators leaves the highest-risk identities outside policy visibility. The next planning step is to align PAM, secrets, and workload identity under one operating model.

With 69% of security leaders agreeing identity management must fundamentally shift to address agentic AI systems, per the 2026 Infrastructure Identity Survey, the programme signal is clear: legacy access review cadences are too slow for machine-paced privilege lifecycles. Teams should prepare for runtime policy enforcement, not just periodic certification.

For practitioners

• Map every privileged identity class Inventory humans, service accounts, workloads, certificates, API tokens, and AI agents under one privileged access model so you can see where standing access still exists.
• Replace durable secrets with ephemeral access Use federation, short-lived credentials, and immediate expiry for high-risk access paths instead of persistent passwords and long-lived API keys.
• Rework access reviews around task boundaries Review whether entitlements are still meaningful when the access window is shorter than the review cycle, especially for automated infrastructure and AI-driven operations.
• Tie AI agent access to explicit runtime limits Define which tools, data sources, and infrastructure actions an agent may reach, then remove access as soon as the task completes.

Key takeaways

• Legacy PAM models preserve the very persistence that attackers exploit, especially when cloud, automation, and AI systems depend on long-lived access.
• The scale of the problem is already visible in the market, with machines and AI agents accounting for the majority of identities in many environments.
• Identity teams should move toward task-scoped, ephemeral privilege across humans, workloads, and AI agents before standing secrets become the default operating model.

Key terms

• Zero-standing privilege: A privilege model in which access is not kept available by default. The identity receives access only when needed and loses it immediately after the task finishes, reducing the time a compromised credential can be reused.
• Ephemeral secret: A credential or token that exists only for a short, task-specific window. In identity security, ephemeral secrets are used to reduce replay risk, limit blast radius, and prevent long-lived access from becoming a standing attack surface.
• Workload identity federation: A method for giving machines and services access without embedding long-lived secrets in code or infrastructure. The workload proves who it is to a trust provider and receives short-lived access in return, which is easier to scope and retire.
• Privileged access management: The controls used to govern high-risk, elevated access to systems, data, and infrastructure. Modern PAM extends beyond human administrators to service accounts, automation, certificates, and AI-driven workflows that also require lifecycle control.

What's in the full article

Akeyless's full article covers the operational detail this post intentionally leaves for the source:

• The article's Gen 1 to Gen 3 PAM progression and how each generation maps to different infrastructure assumptions.
• The specific Akeyless architecture claims around zero-knowledge design, Distributed Fragments Cryptography, and ephemeral access.
• The vendor's explanation of how its model applies to human users, machines, and AI agents across hybrid environments.
• The enterprise validation example and procurement context behind the referenced customer selection.

👉 Akeyless's full post covers the PAM evolution, zero-standing privilege model, and enterprise validation example.

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 NHI governance in your organisation, it is worth exploring.