For years, identity security has been designed to secure an organization’s human users. But as agentic enterprises take shape, the identity equation is shifting. Artificial intelligence (AI) agents and AI-powered builders – software tools used to develop websites and applications without coding – are increasingly participating in how access is configured, governed and used.
Field CTO for EMEA at Ping Identity.
AI agents are effectively new digital employees, so organizations need a way to know they exist and control what they do throughout their lifecycle. They are becoming operators, helping to administer and secure identity environments through machine-native interfaces.
To add another layer of complexity, desktop agents and AI assistants are also beginning to interact with enterprise applications and resources on behalf of users.
For an agentic enterprise to succeed, these agents need trusted access to do useful work but should not be given direct exposure to secrets they have no meaningful reason to access. To achieve this, organizations need a unified, AI-first identity model, centered on end-to-end visibility, governance and controls which strike a balance between security and appropriate access.
AI agents are reshaping identity
AI has created a new category of digital identity. Like human employees, autonomous agents must be discoverable and managed and governed so organizations can understand what systems and data they can access and who is responsible for their actions.
Traditional identity and access management (IAM) systems relied on static, one-time verification methods in response to access requests made by humans. But in the agentic enterprise, requests also come from autonomous software acting on behalf of human users. Organizations therefore need to know exactly who or what is accessing a system continuously, and if they have the correct permissions to access given information.
At the same time, AI is increasingly managing identities and access. Machine-native interfaces allow agents to help manage human users’ access, troubleshoot issues and support security workflows. While these capabilities can help organizations cut costs and improve efficiency, they are only successful when strong access guardrails are put in place.
AI has created a new category of digital identity. Like human employees, autonomous agents must be discoverable and managed and governed so organizations can understand what systems and data they can access and who is responsible for their actions.
Traditional identity and access management (IAM) systems relied on static, one-time verification methods in response to access requests made by humans. But in the agentic enterprise, requests also come from autonomous software acting on behalf of human users. Organizations therefore need to know exactly who or what is accessing a system continuously, and if they have the correct permissions to access given information.
At the same time, AI is increasingly managing identities and access. Machine-native interfaces allow agents to help manage human users’ access, troubleshoot issues and support security workflows. While these capabilities can help organizations cut costs and improve efficiency, they are only successful when strong access guardrails are put in place.
Building a unified identity model for AI
Mechanisms for securing AI cannot simply be bolted onto identity systems designed for humans. It requires a complete rethink of the identity management model, where human and machine identities are governed through a single framework to prevent tool sprawl and unintentional security blind spots.
As organizations adopt AI tools throughout multiple operational layers, enterprise identity needs to evolve and become easier to manage and automate. Identity can no longer rely solely on human administration.
Tools designed specifically for autonomous agents, such as AI-first headless interfaces, allow builders and AI alike to perform identity-related tasks. Autonomous operators must also be trained to configure access, troubleshoot workflows and apply governance controls within approved policies and guardrails.
Visibility and governance across the entire AI agent lifecycle are also critical. As more agents are deployed, businesses must have complete visibility into their agents and actions.
Every AI should be treated as a first-class identity, with a designated human owner, as well as clear policies and full auditability throughout its entire lifecycle. As these agents operate across the enterprise, their actions should be traceable to a human user responsible.
Finally, AI agents need trusted ways to interact with enterprise resources without being given direct access to the credentials or secrets that enable them. Coding and desktop agents increasingly interact with systems on behalf of users, but exposing them to credentials or long-lived secrets creates unnecessary risk. Instead, access to enterprise resources should be brokered through just-in-time privileged controls.
This allows enterprises to maintain oversight of how permissions are granted, governed and audited without exposing the underlying secrets behind that access. Together, these capabilities create a unified identity model which extends governance across human and AI identities without creating a parallel identity stack.
The future of the agentic enterprise
AI agents cannot operate as intended and deliver meaningful value without access to enterprise systems. But granting unrestricted access or exposing sensitive information creates an entirely new risk to organizations.
The future of the agentic enterprise depends on maintaining governance, visibility and control across both human and digital identities. This means identity must become programmable, AI agents should be governed throughout their lifecycle and agent access needs to be given without unnecessary exposure to sensitive data.
A unified identity strategy provides the means to operate AI agents more safely and efficiently while maintaining centralized governance, accountability and control.
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