Palo Alto Networks Idira reframes identity security for AI enterprises

At a glance

What this is: This is a platform announcement arguing that identity security must expand from human PAM to cover machine and agentic identities with dynamic control and lifecycle governance.

Why it matters: IAM teams need to understand how AI adoption changes privilege boundaries, because human-centric controls no longer match the scale, speed, or composition of modern identity estates.

By the numbers:

• 9 out of 10 organisations experiencing an identity-related breach in the past year.
• Machine and AI identities now outnumber humans 109 to 1.
• 61% of privileged access requests are fulfilled with standing privilege rather than on-demand.

👉 Read Palo Alto Networks' press release on Idira and AI enterprise identity security

Context

Identity security is no longer just a matter of human logins and vaults. As enterprises add machine and agentic identities, the governance problem shifts to how privilege is discovered, assigned, and revoked across actors that operate at machine speed and often outside traditional review cycles.

Palo Alto Networks is using Idira to argue that modern PAM has to cover the full identity estate, not just a narrow set of privileged users. The underlying problem is familiar to IAM teams: static privilege models, fragmented controls, and incomplete visibility do not map cleanly to AI-enabled operations.

Key questions

Q: How should security teams govern machine and agentic identities alongside human users?

A: They should treat each actor type as a separate governance population while keeping one control model for discovery, privilege, and lifecycle. Human access reviews, workload entitlement checks, and agent oversight all need different signals, but they should feed a shared inventory and policy layer. That prevents blind spots and stops non-human access from escaping the same governance standards applied to people.

Q: Why do standing privileges create so much risk in AI-enabled enterprises?

A: Standing privilege creates risk because it leaves elevated access available long after the original task is finished. In environments with machines and agents operating continuously, that means more opportunities for misuse, drift, and lateral movement. JIT controls reduce the exposure window, but only if teams actively remove persistent exceptions and keep expiry logic enforceable.

Q: What breaks when identity discovery is incomplete?

A: Governance breaks first, because you cannot certify, rotate, revoke, or offboard identities you cannot see. Incomplete discovery means some machine accounts, secrets, and agent privileges remain outside the control plane, which undermines review quality and exception handling. Teams end up managing a partial estate while assuming coverage is complete.

Q: Who should own non-human identity lifecycle governance in practice?

A: Ownership should sit across IAM, PAM, cloud, and application teams, with clear accountability for creation, change, and revocation. Non-human identities often span multiple systems, so no single team can govern them effectively from one tool. The practical test is whether every identity has an accountable owner and a revocation path before it is put into production.

How it works in practice

Dynamic privilege controls across human, machine, and agentic identities

Traditional PAM assumes a bounded set of privileged humans and a clear request grant revoke loop. In AI enterprises, that model breaks down because machines and agents can hold access at scale, initiate work without human pacing, and consume privileges across different systems in rapid succession. Dynamic privilege control means access is not treated as a static entitlement but as a state that can be constrained, elevated, and withdrawn in response to task context. The technical challenge is not only approval logic, but identity sprawl, entitlement visibility, and the enforcement point where a session becomes privileged.

Practical implication: Map where privilege is still persistent and replace static grants with task-scoped controls.

Zero standing privilege and just-in-time enforcement

Zero standing privilege removes persistent high-risk access and gives it only when needed. Just-in-time enforcement narrows the time window in which an identity can act with elevated rights, which matters when machine and agentic identities can execute continuously and at scale. The design pattern depends on strong identity proofing, clear policy boundaries, and reliable enforcement at the moment of access rather than at provisioning time. In practice, the control is only as strong as the systems that detect standing privilege, govern exceptions, and prevent privilege from lingering after the task is complete.

Practical implication: Use JIT controls to shrink the duration of elevated access wherever privilege is currently standing.

AI-driven discovery and automated identity lifecycle governance

The platform narrative centers on discovery first because governance fails when teams do not know what identities, entitlements, and access paths exist. AI-driven discovery is meant to surface hidden or fragmented identities, then connect them to lifecycle controls such as onboarding, changes in role, and offboarding. For machine and agentic identities, lifecycle governance is less about user departure and more about credential persistence, entitlement drift, and stale access paths that remain valid after the workload or agent has changed. The architectural issue is whether governance keeps pace with identity creation and reuse.

Practical implication: Inventory every identity type before attempting governance automation, or the control surface will remain incomplete.

NHI Mgmt Group analysis

Identity governance is being forced to evolve from human-centric PAM into multi-actor privilege control. The article reflects a real market shift: identity is now a shared control plane for humans, workloads, and agentic systems. That means teams can no longer treat privileged access as a narrow admin problem. The practitioner conclusion is that governance scope now has to follow the actor type, not the old product category.

Standing privilege is now a structural mismatch, not just a configuration weakness. If 61% of privileged requests are still satisfied with standing access, the issue is not only risk but model drift. Static privilege assumptions were built for stable users and predictable workflows. The practitioner conclusion is that any programme still dependent on persistent elevation will underperform as machine and agent density grows.

Continuous discovery is the control plane, not a supporting feature. The article’s focus on identifying every identity, entitlement, and access path is the right direction because governance fails first at visibility. In a mixed estate, unmanaged machine identities become the blind spots that later invalidate certification, offboarding, and exception handling. The practitioner conclusion is that discovery quality now determines whether identity governance is credible at all.

Zero standing privilege: privilege is no longer an account state, it is a session state. That framing is the most useful named concept here because it captures the shift from who owns access to when access exists. For human, machine, and agentic identities alike, the governance question becomes whether elevated rights are present only during the work window. The practitioner conclusion is that persistent privilege should be treated as an exposure pattern, not an operating model.

AI identity governance will increasingly converge with lifecycle governance. The article points toward a market where discovery, policy, and lifecycle automation are bundled into one operating model. That convergence is logical because machine and agentic identities can be created, reused, and abandoned faster than manual reviews can track. The practitioner conclusion is that IAM and PAM teams need shared ownership of lifecycle state, not separate control silos.

From our research:

• 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage, according to Ultimate Guide to NHIs.
• Only 20% have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them.
• For a broader control baseline, review Top 10 NHI Issues and compare current privilege controls against the gaps this post highlights.

What this signals

Zero standing privilege is becoming the more useful operating assumption for mixed identity estates. When privilege is temporary instead of persistent, IAM and PAM teams can align policy, session control, and lifecycle events around real exposure rather than legacy account ownership.

The scaling pressure is already visible in NHI programmes: NHIs outnumber human identities by 25x to 50x in modern enterprises, which means discovery quality now determines governance quality. Teams that still manage machine access as an exception will struggle to keep pace with AI-driven sprawl.

For readers building out identity strategy, the next planning question is whether lifecycle and privilege controls can be evaluated together across humans, workloads, and agents. That is the only way to avoid three separate governance models that all claim coverage but share none of the same evidence.

For practitioners

• Inventory machine and agentic identities separately from human accounts Build an identity register that distinguishes human users, workloads, secrets, and agentic identities so entitlement reviews do not collapse distinct risk profiles into one list.
• Remove standing privilege from high-risk workflows Target admin, production, and data-access paths where privileged access remains persistent and convert them to just-in-time grants with explicit expiry conditions.
• Tie governance automation to identity lifecycle events Require every non-human identity to have an owner, purpose, and revocation trigger so access does not outlive the workload, service, or agent that uses it.
• Validate discovery against actual access paths Compare vault inventories, directory records, cloud entitlements, and tool-level permissions to expose identities that exist in one system but are missing from another.

Key takeaways

• Identity security is broadening from human PAM into a multi-actor control problem that includes machines and agentic identities.
• Persistent privilege remains common, which means many enterprises are still operating with exposure windows that modern AI-era governance cannot justify.
• The immediate task is to align discovery, JIT controls, and lifecycle ownership so privilege exists only when and where it is needed.

Key terms

• Standing Privilege: Standing privilege is persistent elevated access that remains available without a fresh approval or task trigger. In non-human identity governance, it is a high-risk condition because workloads, secrets, and agent identities can keep privileged reach long after the intended use case has ended.
• Zero Standing Privilege: Zero standing privilege means elevated access is not left on continuously and must be issued when needed. For non-human identities, that usually requires strong enforcement around task boundaries, expiry, and revocation so access exists only for the duration of a defined operational need.
• Identity Discovery: Identity discovery is the process of finding and cataloguing every identity, entitlement, and access path across the environment. In NHI programmes it is foundational because hidden service accounts, tokens, and machine identities create governance gaps that certification and offboarding cannot close.

Deepen your knowledge

Identity discovery, privilege control, and lifecycle governance are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are extending PAM into machine and agentic identities, it is worth exploring.

This post draws on content published by Palo Alto Networks: Introducing Idira, the next-generation identity security platform. Read the original.