PAM for Every Team and Every Agent
At 3 a.m., a process connects to one of your production databases. It runs a cleanup job, writes its changes, and disconnects. No one approved it. No one watched it. The credential it used was issued eight months ago and hasn’t been reviewed since.
Nothing went wrong. That’s the point.
If someone on your team did that same work, it would look different. They’d request access. Someone would approve it. The session would end. On their last day, their account would be shut off. The work is identical. Only the worker changed.
For as long as privileged access management (PAM) has existed, the privileged user has been a person. Someone with a laptop, a badge, and a manager. That assumption is now incomplete. Today, some of that work is done by AI agents that reason, decide, and act on their own.
This guide shows you how to extend the privileged access program you already run to cover the agents working alongside your people.
You’ll learn:
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What agentic PAM means, defined plainly
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Which agent work qualifies as privileged
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Why traditional PAM controls don’t transfer cleanly
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What it takes to govern human, non-human, and agentic identities on one foundation
It all starts with one shift in thinking: privileged users aren’t just people anymore.
People Aren’t the Only Privileged Users
Privileged access was designed around the idea of human workers who log in, do the work, and go home. That worker still exists. But they’re not the only ones taking privileged actions.
What Agentic PAM Actually Means
Agentic PAM is a new(ish) topic for IT teams. Before we get into why it matters, let’s settle on a single definition.
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Agentic PAM is privileged access management where the requester is an AI agent instead of a person.
Same scoping. Same audit trail. Applied to a worker that never sits down at a keyboard and never logs off.
The working definition of a privileged user hasn’t kept up with who’s actually in your environment. Palo Alto Networks’ 2026 Identity Security Landscape, a survey of more than 2,900 cybersecurity decision makers, found that machine identities, including AI agents, now outnumber human identities 109 to 1.
If your definition of a privileged user only covers people, it’s accounting for one identity out of every 110.
The Shape of Today’s Identity Environments
Agents aren’t a “someday” problem.
72% of organizations have AI agents in production, including 31% running in business-critical environments like financial reporting, HR provisioning, and access management according to JumpCloud’s Agentic IAM Pulse Report.
Every one of those agents is an identity. It authenticates, holds credentials, and reaches into systems, the same way an employee does. That means it belongs inside your identity program, not beside it. And there are a lot of them. In the same research, 53% of organizations report more non-human identities (NHIs) than human employees in their environments.
The Agentic IAM Pulse Report
Get your hands on the latest industry data showcasing why governance gaps widen as AI deployments mature.
Governance hasn’t kept pace. Deloitte’s survey of 3,235 IT and business leaders across 24 countries found that only 21% of organizations have a mature governance model in place for agentic AI.
The agents moved into production, but the controls didn’t follow.
Your Agents Are Already Doing Privileged Work
Some agents only read things. A summarizer pulling from a knowledge base isn’t the subject of this guide. This is about the agents that do more than read.
The Work That Qualifies
Generative, web-based AI has its own advantages and challenges. Agentic AI dials up the opportunities and risks significantly.
In practice, an agent taking a privileged action could look like a DevOps agent restarting a server. A finance agent connecting to a production database to reconcile invoices. A platform agent changing cloud infrastructure. An incident agent running an administrative command to investigate an outage.
Every one of these agents is taking a privileged action, one that was classified as privileged long before agents existed. If a person did any of them, they would move through a standard access request workflow. But agents haven’t been grandfathered in yet.
IT leaders already see the risk. In our Q1 2026 IT Trends research, 37% of IT leaders named unauthorized access or privilege escalation by AI agents as a serious security threat.
How Access Was Granted
Nobody made a bad decision here.
Access was granted at setup, by whoever stood the agent up, scoped to whatever made it work on the first try. It was never reviewed because there was no review step. People have a dedicated onboarding flow. Agents do not.
In the Agentic IAM Pulse Report, 66% of organizations said that their AI agents have equal or greater system access than human users.
In business-critical environments, 38% give agents significantly more access than the humans working beside them.
The OWASP Non-Human Identities Top 10 lists overprivileged NHI as a documented failure mode: broad or administrative roles assigned to automated identities to keep development moving.
This isn’t an overt failure. It’s a documented challenge that shows up when organizations move fast, and a natural response to the need for speed. What you do control is what happens next: building a PAM program that supports your AI agents. That starts by questioning agent access the same way you already question human access.
Every privileged access program needs to answer four questions about the users accessing sensitive systems.
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Who’s asking?
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How long do they get access?
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Who said yes?
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What did they do?
Traditional PAM makes these questions easy to answer about your human users. For agents? The data says otherwise.
PAM assumes a human requester who can prove they are who they say they are.
Agents get set up a different way. They’re built to perform jobs autonomously, whether or not the humans who built them are actually at the desk.
These agents work on their own, on their own timelines. So instead of pinging a person to help out with a multi-factor authentication (MFA) prompt at 3 a.m., many creators elect instead to set up their agents with standing privileged credentials and broad permissions, often through a shared service account or an API key in a config file.
The agent never gets an identity of its own. It borrows one.
JumpCloud’s Agentic IAM Pulse Report found that only 37% of organizations have fully integrated AI agents into identity and access management (IAM). The remaining 63% sit somewhere short of that: 42% partially integrated, 17% managing agents entirely outside formal systems, and 4% with no formal policies at all.
Leadership sees a different picture than the people doing the work.
In the same report, 68% of CIOs said they believed their AI agents were fully integrated into formal IAM policies. Among IT managers and team leads, 35% said the same. The people closest to the work know the integration is not finished. That difference is worth raising with your leadership, because it changes what gets funded.
PAM assumes access is temporary. It’s granted for a reason, and then taken away.
Agents get provisioned broadly and then are left that way. In the Agentic IAM Pulse Report, 49% of organizations said they rely on long-lived API keys for agent authentication. Among business-critical deployers, that number climbs to 71%.
Reliance on permanent credentials goes up as the systems get more sensitive.
And a long-lived key stays open whether the agent is working or not.
PAM assumes an approval step. Someone requests, a policy or a person authorizes.
Agents work differently. They reason toward a goal and choose their own next action. If an agent also decides what it’s allowed to do, it’s effectively giving itself permission.
That’s a problem, and the industry has started ranking it as one. The OWASP Top 10 for LLM Applications ranks the most critical security risks in applications built on large language models (LLMs).
Its 2026 edition moved Excessive Agency, meaning an AI system that has too much power to act on its own, from sixth place to third. It’s the first edition to factor real-world incident data into its ranking. OWASP names three root causes: excessive functionality, excessive permissions, and excessive autonomy. All three are access problems.
The MCPTox benchmark shows why an agent can’t be trusted to decide its own permissions. Researchers hid malicious instructions inside the tool descriptions that agents read, then tested 20 agents against 45 live Model Context Protocol (MCP) servers and 353 real tools.
The most susceptible model followed the hidden instruction 72.8% of the time, and the average across all agents tested was 36.5%. Even the model most likely to refuse did so less than 3% of the time.
And more capable models were often more susceptible, because they’re better at following instructions.
The point isn’t that agents get attacked. It’s that the same reasoning that makes an agent useful is what an attacker manipulates, so the agent can’t also be the authority on its own permissions.
Meanwhile, human oversight is thinning. In the Agentic IAM Pulse Report, 46% of organizations said they allow high-risk actions to proceed without prior human approval. Human-in-the-Loop (HITL) approval, meaning a named person signs off before the action runs, falls from 48% in testing to 29% in business-critical environments. Among business-critical deployers, 24% let agents take high-risk actions with no human supervision at all.
Oversight declines precisely where the stakes are highest.
PAM assumes you can reconstruct what happened, and that offboarding eventually closes the loop. Both assumptions were built around people.
A log line reading service-account-42 modified the record does not answer an auditor’s question about who modified what. And when a project ends, the agent keeps its access. Offboarding logic is tied to employment, so nothing is deprovisioned. The agent’s credential stays live and it becomes a zombie agent: still running, still authorized, no longer visible.
The industry ranks improper offboarding as the number one risk on the OWASP Non-Human Identities Top 10. In other words, the top risk for non-human identities isn’t a sophisticated attack. It’s an account nobody remembered to shut off, and that’s hard to catch when you can’t see what your agents are doing.
The Agentic IAM Pulse Report puts numbers to that for commercial IT teams: 59% lack centralized visibility into agent activity and 59% do not maintain full audit trails. More than half, 55%, have no centralized kill switch.
But the most telling figure is the smallest. A third of organizations say their only option is to disable agents manually, system by system.
How to Answer the Four Questions for Agents
You’ve seen where each question breaks down for agents: who’s asking, how long they get access, who said yes, and what they did. The good news is that the questions themselves don’t change. What changes is how you answer them.
Each control below answers one of these questions for AI agents, applying the same access rules you already use for people.
An agent running on a shared service account is invisible. Its activity blends in with every other process using the same credential, so you can’t tell which agent did what, or who’s responsible for it.
The fix is to give each agent an identity of its own: its own record, its own authentication, its own lifecycle state, and a named human owner who is accountable for it.
Three things follow immediately. The agent no longer inherits blanket access from whoever created it, because access is assigned to the agent rather than absorbed from a person. Every action traces back to a specific agent and to the human who authorized it. And you can shut the agent off on its own, without touching anyone’s employee account.
You disable the agent. The agent stops. The owner’s identity is untouched.
This is the control that makes every other control possible. You cannot scope, approve, or audit an actor you cannot name.
It also settles a question of accountability that mostly goes unasked. In the Agentic IAM Pulse Report, 17% of organizations said that a designated security leader is accountable for AI agent actions, while responsibility falls to IT by default in 47% of cases. Ownership lands on whoever runs the adjacent systems rather than being assigned on purpose.
Least privilege by default, with elevation scoped to a specific action for a bounded period. The agent gets what the task requires, when the task requires it, and nothing else.
The pattern is a request, an evaluation against policy, a credential issued for the job, and an expiry when the work is done. No permanent key sitting in a config file.
The difference shows up in outcomes. Teleport’s 2026 State of AI in Enterprise Infrastructure Security found that organizations with over-privileged AI systems reported a 76% incident rate. Among those that limited AI to only the privileges a task needed, it was 17%.
The principle isn’t new. Evaluating access per session, instead of leaving privileges standing, has been a core part of Zero Trust for years (see NIST Special Publication 800-207). Now it’s up to IT teams to extend that principle to AI agents.
The policy that decides what an agent can do has to live outside the agent. A system that reasons over untrusted input cannot also be the authority on its own permissions.
In practice that means two things. First, an external policy check on every privileged action. Second, a HITL checkpoint for the operations you can’t take back: financial transactions, identity changes, access provisioning. For those, a named person approves the action before the agent executes instead of reviewing afterward.
Formal standards are moving in this direction. In February 2026, the NIST Center for AI Standards and Innovation announced its AI Agent Standards Initiative. It focuses on telling agents apart from human operators, bounding what a task can execute, and enforcing attribution for forensic work.
An audit trail has to name the agent, tie it to a human owner, and show the path that granted the access. Compare the two entries.
service-account-42 modified opportunity records at 03:14
RevOps Agent, owned by the RevOps lead, wrote to Salesforce at 03:14 using access granted by the RevOps Agent group policy
The first tells you something happened. The second tells you what to do about it.
Hold your own logs against four checks: which agent, whose authority, what access, and how would you stop it?
A record that doesn’t answer all four isn’t an audit trail. Acting on the record matters as much as having it. Revocation needs to be immediate and complete, across every connected system at once.
One IT Foundation for Every Worker
Step back from the controls for a moment and look at what an agent actually does inside your environment.
An AI agent is a worker. It has an identity. It needs access to systems. It runs on infrastructure you are responsible for. Those are the same three things that are true of every employee you have.
The foundation that already governs identity, access, and devices for your people is the foundation agents belong on too.
One Policy Model Instead of Two
Least privilege, access reviews, approval thresholds, and off-boarding don’t need to be reinvented for agents. They need extending. When one policy engine governs human, non-human, and agentic identities, a rule written once applies everywhere. There’s no seam between two systems or questions about which one is authoritative.
In our Q1 2026 IT Trends research, 85% of IT leaders said secure IAM practices are critical to successful AI adoption, and 70% of organizations planned to govern non-human identities. But only 22% had put the measures in place.
Unification is how you close that distance.
In the same report, almost 90% of IT leaders said unification affects their ability to implement and scale AI securely and effectively, and 46% called the effort critical.
One Record of What Happened
When a human user, the agent they authorized, and the privileged action that followed all live in one system, the audit trail is continuous. Split across multiple platforms, you have a collection of partial records to reconcile by hand, often during the exact moment you can least afford the delay.
One system also makes revocation real. One place to look also means one place to act.
A third of organizations can only disable agents manually, one by one. A split foundation eats up time on your worst day.
Trust the Device, Not Just the Credential
An agent’s identity tells you what it is allowed to do. It doesn’t tell you whether the machine it’s running on is healthy, managed, or compromised.
When identity, access, and device management sit in one platform, you can tie an agent’s trust to the current state of the infrastructure underneath it. Not only to a valid credential.
A platform that issues an agent an identity but has no view of the endpoint can’t make that check.
Consolidation Is What Lets You Scale
Governance isn’t the brake on agent adoption. The absence of it is.
In the Agentic IAM Pulse Report, 92% of organizations reported some limit to scaling AI agents. Security concerns were the single largest barrier at 26%, ahead of integration complexity at 16%, budget at 13%, and skills at 12%.
The organizations that have solved it are the ones still moving. Those with governance in place are three times more likely to scale without limits.
You’re not choosing between speed and control. Control is what buys you the speed.
How JumpCloud Does This
JumpCloud Agentic PAM lets you manage privileged access for your AI agents in the same place you manage it for your human users.
Every agent gets its own identity and a named human owner. Agents receive only the permissions they need, strictly for the task at hand, and that access expires when the job finishes. All of this without standing up a second platform or replacing the systems you already run.
Governing human, non-human, and agentic identities from one platform turns AI from a risk you’re managing into an advantage you’re compounding. That’s Intelligent, Secure IT: one control plane, one audit trail, and one answer to who did what.
You already run a privileged access program. Extending it to cover every agent is a change in scope, not a change in strategy.
Put Privileged Access Into Practice
Bring the controls in this guide to life with JumpCloud Agentic PAM. Manage privileged access for your people and AI agents from one platform, with a named owner for every agent and permissions limited to the task at hand. See how you can answer the four questions for every agent: who’s asking, how long they get access, who approved it, and what they did.
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