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Covering the latest thought leadership, events, and news about identity security
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Most identity programs were built around a relatively simple assumption: people access systems and identity controls determine what those people can do. Those programs were built around predictable relationships among users, applications, permissions, and approval processes. Over time, they expanded to govern service accounts, workload identities, APIs, certificates, automation platforms, and other non-human identities. Yet many of those identities were treated as technical dependencies rather than governed identities requiring the same ownership, oversight, and accountability applied to human users.
Agentic AI introduces a new challenge. As organizations move beyond copilots and assistants toward autonomous agents capable of interacting with applications, workflows, and business processes, the assumptions underpinning traditional identity programs begin to break down.
Preparing for agentic AI means extending identity governance beyond access to include ownership, delegated authority, lifecycle, and accountability for agents that can take action.
Many enterprise AI deployments still follow a familiar pattern: a user submits a prompt, the system provides an answer or recommendation, and the human decides what happens next.
Agentic AI changes that relationship. An agent may be assigned an objective rather than a single task. It can gather information, interact with applications, invoke APIs, and complete multiple steps in pursuit of a business outcome. In some cases, it may coordinate with other systems or agents with limited human involvement.
As organizations move from AI assistants to autonomous agents, the nature of risk changes as well. Identity programs have traditionally focused on controlling access to applications, systems, and data. Agentic AI introduces a new challenge because agents are increasingly participating in business processes rather than simply retrieving information.
The question is no longer limited to whether an agent can access something. Organizations must also understand what it can do with that access, what authority it has been granted, and how its actions are governed.
Service accounts, workload identities, automation accounts, and the credentials they use — such as API keys, tokens, and certificates were already difficult to govern. Agentic AI does not recreate those challenges, but it makes the consequences harder to ignore.
As organizations deploy agents, those agents often operate through existing identities, inherited permissions, or shared credentials. The challenge is no longer whether the agent functions as intended. It's whether the organization can clearly identify the identity it uses, the permissions it inherited, who owns that access, and how those permissions are reviewed, monitored, or revoked.
These questions become more difficult as agents interact across applications, APIs, business processes, and other systems. Without clear ownership, visibility, and accountability, organizations risk granting autonomous systems more authority than intended while reducing their ability to govern the actions those systems perform.
Identity governance for agentic AI should begin by treating every agent as a distinct, governed identity with its own access, entitlements, and privileges rather than an invisible extension of a person, application, or service account. Every agent or agent workload should be uniquely identifiable, associated with an accountable owner, and governed according to its purpose and authority.
Traditional IAM has long governed identity, authentication, authorization, lifecycle, and accountability. Agentic AI introduces a new requirement: authority.
What decisions and actions has the agent actually been empowered to perform?
This should include several core requirements:
These are extensions of familiar identity practices, but agentic AI raises the stakes. An agent can operate continuously, interact with multiple systems, and execute a sequence of actions much faster than a person. Periodic certification alone may not be enough. Governance must become more dynamic and capable of responding as agent behavior, authority, data sensitivity, and risk conditions evolve. Agent access may need to change when the task, data sensitivity, runtime context, or risk level changes.
Human oversight also remains important, particularly for high-risk or irreversible actions. Approval points, escalation paths, containment mechanisms, and the ability to revoke an agent’s access should be designed into the operating model rather than added after deployment. The goal is not simply to govern what an agent can access. It is to govern what it is allowed to do with that access.
One of the biggest shifts introduced by agentic AI is the need to govern decision authority.
Traditional IAM programs have focused heavily on application access and data entitlements. Agentic AI requires organizations to go further by defining not only what an agent can access, but also what authority it has been granted.
Can the agent observe, recommend, initiate, approve, or complete an action?
Those distinctions have direct governance implications.
An agent that summarizes a transaction does not carry the same risk as one that approves it. An agent that recommends an entitlement change is different from one that modifies access directly. An agent that retrieves a customer record requires different controls from one that can update or delete it.
Before granting authority, organizations should determine:
Where possible, agents should act through delegated, purpose-specific authority rather than impersonating a user or inheriting broad standing privileges. Time-bound access, scope-limited tokens, ephemeral privilege, and per-action authorization create a clearer chain of trust while reducing the potential for unintended access.
The objective is not simply to manage entitlements. It is to govern authority.
As AI agents become more autonomous, organizations must be able to prove that actions are authorized, governed, and traceable. A useful audit trail must connect an action to the agent, the initiating identity, the authority granted, and the policy that permitted it.
An AI-driven process may involve a user request, an agent, an API, a service account, and multiple downstream applications. A log showing that an API key executed an action rarely explains who initiated it, why it was permitted, whose authority was used, or who ultimately owns the outcome.
Organizations should be able to answer questions such as:
Trust in agentic AI depends on visibility, attribution, and accountability. Organizations need a defensible chain of evidence that links actions to identities, authority, policies, and owners. Without that evidence, they cannot reliably explain who authorized an action, under whose authority it was performed, and why it was permitted. As AI adoption expands, the inability to answer those questions makes it increasingly difficult to defend, audit, and govern AI-driven activity at scale.
Before placing an AI agent into a production workflow, leadership teams should be able to answer a practical set of questions:
If those answers are unclear, the organization may be able to deploy an agent, but it may not be prepared to govern that agent at scale.
Many of these same governance, visibility, and accountability challenges are explored in Is Your Organization Ready for Enterprise AI?, which focuses on assessing AI readiness and establishing the controls needed to scale AI securely. Agentic AI builds on those same foundations, but extends governance beyond access to include authority, accountability, and decision-making.
The first question for agentic AI is not which model to deploy. It is whether the organization can identify, authorize, govern, monitor, and revoke the agents acting across its environment. Addressing those requirements early creates a stronger foundation for expanding agentic AI without sacrificing accountability, security, or trust.
IAM has traditionally governed access. Agentic AI forces it to govern authority as well.
Ready to Prepare for Agentic AI?
MajorKey's Identity-First AI Advisory evaluates identity, access, ownership, and governance gaps that affect AI-agent deployment and identifies the controls and operating changes needed before broader rollout. The engagement connects business priorities with identity strategy, access governance, technical controls, and an operating model designed to move from readiness into execution.
Next in the series: How to Evaluate an Identity-First AI Advisory Partner. After establishing the foundations of identity, governance, and agent authority, the next step is choosing a partner that can help translate strategy into scalable operational controls.
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Covering the latest thought leadership, events, and news about identity security
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