AI agents resemble human users from an identity-governance perspective. They too need identities, permissions, owners and lifecycle controls. But unlike humans, agents can be created in seconds, can operate indefinitely without supervision, and can rapidly change their intents, abilities and connections.
"
AI agents now follow the same lifecycle pattern as human identities, only faster and at a scale human identities never could," writes Saviynt Chief Product Officer
Vibhuti Sinha in a company blog post.
Because of these factors, simply extending conventional
identity governance to agents is not enough. Not is classifying them as yet another type of
non-human identity. Organizations instead need to treat agents as a distinct identity type, governed throughout their lifecycles and, most significantly, linked to individual humans who can be held accountable for the agents' actions.
"The biggest threat that we see right now is that these agents are without any human owner," says Saviynt Director of Product Management Manish Acharya. "We want to make sure that whatever agents have been doing, there is some person accountable to it."
IDC estimates
1 billion AI agents running worldwide by the end of 2029. Meanwhile, the compromise time for AI-powered attacks
can be as little as 27 seconds. Effective governance begins with knowing the identity of every agent, who owns the agent and what it should be allowed to do.
Why every AI agent should be registered as a managed identity
Registration establishes an agent's identity and creates the foundation for everything that follows. Each agent should be assigned a unique identifier, and its model, hosting environment, purpose and accountable owner should be recorded. Least-privilege access should be provisioned from the beginning.
That isn't the case in many organizations, laments Acharya, in which AI agents are created and deployed without regard to who might be responsible for those agents.
"Who is the actual accountable owner for that particular agent is typically skipped in the enterprises right now, because everyone wants to move fast, everyone wants to show their productivity," he says.
Agents should not simply "belong" to an application or a department. They need designated humans to be responsible for their behavior, access and compliance, along with business sponsorship tying them to an organizational purpose.
This process also requires designated succession of ownership. Otherwise, departing employees might leave behind "orphaned" agents provisioned with high privileges and access, yet with nobody accountable for them.
"If a user who is the owner of an agent is leaving the organization," explains Acharya, "we have a succession-management policy in place that the other person takes his place and starts owning those agents."
That is why lifecycle management is necessary for AI agents, to manage and track them from their creation through their useful lives to their decommissioning. Human "ownership" is a key part of that.
"We want to have at least a human owner accountable for these agents," says Acharya. "Once you do that, then you can start doing all the governance because now we know that if we have to send any governance process to anyone, we know which owner to redirect it to."
How continuous access certification helps prevent AI privilege creep
Registration alone cannot keep an agent secure, because agents and their requirements can change. New tools, APIs and
MCP servers may be added, while permissions needed during development might become unnecessary after a time.
"One of the biggest mistakes I consistently encounter is over-entitlement at creation," writes Sinha. "A developer grants broad access so the AI agent works during initial testing, but nobody remembers to narrow those permissions later."
Traditional access certification, practiced quarterly or semiannually, leaves long windows in which excessive privileges can persist for months. Acharya argues instead for continuous access review, allowing an owner to examine an agent's privileges at any time and remove unnecessary ones rather than waiting months for another certification campaign.
"If we have a typical standard scheduled certification cycle, [you have] to wait for three months for that certificate cycle to trigger," Acharya explains.
"With continuous access review, [you] can go today and see that, 'Hey, my agent does not need access to these many tools anymore. That agent does not need access to these MCP servers. I'm just going to remove it because this is extra privilege that this agent has.'"
This turns lifecycle governance into an ongoing process. Sinha recommends implementing behavioral analytics for entitlement creep, automated alerts if access diverges from an agent's declared purpose, and periodic owner re-attestation and recertification when capabilities or integrations change.
How auditing and provenance-tracking provide accountability and traceability
Autonomy makes traceability particularly important. Organizations need to establish not only what an agent did but also whether it legitimately belongs to the enterprise, who authorized it and how its identity and permissions evolved. Saviynt's platform helps with that, Acharya explains.
"We know the history of the agent, what was done on that, who in addition to the actions which were performed on the platforms," he says. "We also audit the actions which were taken."
Lack of attestation or provenance are
governance weaknesses because organizations may otherwise be unable to prove an agent's authenticity or authorization. Registration should therefore establish a durable record against which subsequent lifecycle events can be tracked.
Auditability must continue through retirement. Decommissioning should revoke credentials and tokens, remove integrations and permissions, block further invocation and preserve an immutable record of who approved retirement and what access was removed.
A new type of identity
AI-agent governance requires adapting familiar identity principles to an unfamiliar kind of actor. Every agent needs an assigned identity, least-privilege access and lifecycle controls — but its autonomy and rapid evolution demand more continuous oversight than conventional identities.
Registration establishes accountability; ownership and succession prevent abandoned agents; continuous certification limits privilege creep; auditing and provenance preserve a defensible history; and
identity security posture management can ensure protection.
Together, these practices transform AI agents from loosely controlled pieces of technology into governed enterprise identities whose actions can be traced back to responsible humans.
"Every AI identity has a birth, life, and retirement," writes Sinha. "If you don't govern all three phases, you're not managing risk, you're multiplying it."
