AI agents, ranging from customer-facing assistants to autonomous digital workers, are becoming commonplace in enterprise environments.
Yet unlike traditional applications, AI agents can reason, make decisions, and act independently. As a
Ping Identity executive brief notes, agentic AI introduces "a new, novel class of non-human identity" that challenges traditional identity and governance models.
Because of this shift, organizations must modernize
identity-security systems originally built for human and machine accounts that used long-lived, static credentials. Enterprises must adopt identity frameworks that provide visibility, just-in-time runtime authorization, delegated access, and human management.
The importance of visibility and human oversight
Adapting identity security for AI agents begins with visibility. As the old saying goes, you can't manage what you can't see. Accordingly, security teams must discover, classify, and continuously monitor every AI agent in the environment.
Agents that mimic human behavior through desktop browsers or graphical interfaces also require detection and tagging. Each AI agent, no matter its manner of interface, should be given a distinct identity, linked to an owner or custodian, and governed through lifecycle controls similar to those of workforce identities.
Human oversight is equally important because some actions are too sensitive to execute autonomously. In a
recent overview of upcoming identity trends, Ping Identity recommends human-in-the-loop (HITL) controls for high-risk transactions such as financial approvals, changes to privileged access, or sensitive data operations.
Modern identity systems must support step-up authentication, out-of-band approvals, and
continuous auditing. Keeping humans in the loop means keeping accountability, maintaining compliance, and preventing agents from exceeding their authority.
How to authenticate and authorize AI agents
Modern identity architectures should treat agents as first-class digital identities with scoped permissions, runtime authorization, and policy-driven controls. In an
online guide to managing AI, Ping Identity recommends using OAuth 2.0 delegation, Dynamic Client Registration (DCR), assertion grants, and mutual TLS (mTLS) to authenticate and authorize agent actions securely.
Short-lived access tokens and just-in-time (JIT) entitlements are critical to enforcing the principle of least privilege. Instead of granting permanent access to AI agents, organizations should evaluate permissions continuously based on context, task intent, and risk signals.
Organizations must also facilitate secure interoperability between agents, APIs, and external tools, which can be provided by standardized frameworks such as the
Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol, which also can help enforce in-house policies.
Why humans should never share credentials with AI agents
In
a recent explainer, Ping Identity warns that one of the most dangerous practices in agentic AI environments is credential sharing, as it destroys auditability and creates significant risks around impersonation, fraud, and unauthorized access.
Gartner predicts that by 2028, organizations that let humans share credentials with AI agents will triple their rates of account takeover and fraud.
Instead, organizations should use authenticated delegation, in which agents receive scoped, temporary tokens that define exactly what they can do and on whose behalf they can act. This preserves accountability while preventing agents from inheriting unrestricted human privileges.
Delegated authorization also enables full audit trails. Security teams can trace every action back to the specific agent identity, delegated authority, and policy context that permitted the transaction. Combined with runtime monitoring and automated revocation controls, this reduces the blast radius of compromised or misconfigured agents.
"Being AI-ready is no longer aspirational — it's operational," says
a Ping Identity guide. "This means having the governance, security, and infrastructure to safely scale AI agents across customer and workforce environments."
