Presented by JumpCloud
Identity and access management has traditionally been built around a human actor. AI is changing that assumption.
An employee can now use an AI assistant to access company systems and take actions on their behalf. An autonomous agent can work across those same systems without anyone at the keyboard. Some agents may even need privileged access to infrastructure and production environments.
For IT, this expands the identity problem. AI introduces new actors, new access paths, and new activity that traditional identity systems may not see, particularly when it originates on the endpoint.
What is Agentic IAM?
Agentic IAM extends identity, access, and governance to a workforce that includes both humans and AI agents.
An assistant may act inside a human user’s context while reaching systems the employee never touches directly. An autonomous agent may operate on its own, with its own credentials, permissions, lifecycle, and history. Traditional workforce IAM was not designed for either.
Agentic IAM brings discovery, identity, access management, and governance together so IT can see the AI operating across its environment, establish who or what is acting, control access, and maintain a record of activity.
Discovery is especially important because many agents don’t enter the environment through traditional identity infrastructure.
Discovery starts at the device
A SaaS application shows up in an identity provider when someone federates it. An AI assistant doesn’t necessarily follow that path. An employee can install Claude Desktop or Cursor on a laptop, add an MCP server by editing a local configuration file, put a personal API key in a dotfile, or install a browser extension with access to the pages they open.
None of that produces a SAML assertion or necessarily appears in an identity provider log. The access is real and active, but it can remain invisible to a control plane that only sees authentication events.
An identity-only system sees an agent when it presents a credential to something the identity provider already fronts. By then, IT has only part of the picture. AI activity can already be happening locally, using credentials and connections that sit outside that authentication path.
AI increasingly lands on the endpoint. Discovering it there gives IT visibility into agents and AI tooling before they appear at the identity layer.
Use case 1: Employees using AI assistants
Employees are increasingly using AI assistants such as Claude, ChatGPT, and Cursor in the context of their everyday work.
The person is still initiating the activity, but the AI tool may be calling APIs, accessing applications, or invoking tools through MCP servers on that person’s behalf. The human identity remains important, while the path between that identity and company resources becomes more complicated.
IT needs visibility into which AI tools are installed, which MCP servers they connect to, what credentials exist in local files and keychains, and which applications those tools can reach. Much of that context lives on the device.
Revocation exposes the same asymmetry. Disabling an account at the identity layer closes the SSO path, but it may not invalidate a long-lived API key stored locally. Governing AI-assisted work requires visibility and control across both identity and endpoint.
Use case 2: Autonomous agents doing real work
The identity model changes when an agent operates independently, without a person initiating each action.
Treating an autonomous agent as a human user breaks down quickly. It needs its own identity, defined purpose, named owner, lifecycle, application assignments, audit trail, and independent way to revoke access rather than hiding behind a shared service account or long-lived API key.
Ownership also becomes more useful when it is connected to operational context. An identity record can tell IT who has been designated as the agent’s owner. Endpoint visibility can add the device the agent runs on, the user context it runs in, and the posture of that device.
Together, those signals provide a stronger record of where the agent came from, who is responsible for it, and how it is operating.
Use case 3: AI agents with privileged access
Some agent workflows extend beyond SaaS applications into servers, production databases, cloud infrastructure, and other privileged resources.
Permanent administrative credentials create the same problems for agents that they do for people, with the added challenge that agents may operate continuously and without someone at the keyboard. Privileged access should be scoped, governed, auditable, and revocable, without standing administrator credentials or secrets accumulating on the machine where an agent runs.
The existing principles of privileged access management still apply. Agentic IAM extends them to a new type of actor.
Why this is an IAM category, not an AI feature
AI assistants, autonomous agents, and privileged agent workflows may look like separate product problems. From an IT perspective, they are different manifestations of the same identity problem:
Discover. Register. Manage. Govern.
Those stages form the Agentic IAM lifecycle, and the order matters. Discovery gives IT visibility into what is operating across the environment. Registration establishes an agent as an identity with an owner and purpose. Management determines what it can access. Governance provides the activity history and lifecycle controls required to manage it over time.
This is also where the relationship between identity and the endpoint becomes critical. An identity stack that begins at authentication may miss AI activity already happening on the device. Bringing device and identity management together provides visibility earlier in the lifecycle and connects what is running with the identities, credentials, and resources around it.
At JumpCloud, we manage the device and the identity from one platform. Discovering AI on the endpoint isn’t a new agent to deploy. It’s a new question asked of one already running. Our work spans Agent Identities, human ownership and lifecycle controls, Agent Groups and application assignments, activity visibility, MCP and shadow AI discovery, AI Gateway visibility, and initial privileged-access support.
As AI becomes part of everyday work, identity can no longer stop at the human user. Visibility can’t stop at the identity layer either.
Greg Keller is CTO and Co-founder at JumpCloud.
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.