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Enterprises should govern AI agents before giving them access to sensitive data or operational systems. Unlike a tool that only generates text, an agent may make decisions and take actions with limited human supervision. That makes identity, delegated authority, permissions, oversight and accountability core parts of deployment—not paperwork to add afterward.
Why AI agents change the governance problem
An AI agent can pursue a goal by making decisions and acting across systems, sometimes with limited human supervision. The risk is therefore not limited to whether an answer is accurate: an agent may also access data, change a record, trigger a workflow or affect another system. Governance needs to address both what the agent produces and what it is allowed to do.
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NIST’s National Cybersecurity Center of Excellence (NCCoE) describes this challenge in a February 2026 concept paper on agent identity and authorization. It proposes exploring how identity standards and practices could help organizations distinguish agents from people, authorize their rights and entitlements, and link delegated work to users where appropriate. The paper outlines proposed work and seeks stakeholder feedback; it is not a completed technical standard or finished practice guide.
How NIST’s AI RMF can structure governance
NIST released AI Risk Management Framework (AI RMF) 1.0 on January 26, 2023. It is voluntary guidance for incorporating trustworthiness considerations into AI design, development, use and evaluation—not a binding regulation. Its four functions organize risk work across an AI system’s lifecycle:
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- Govern: Set responsibilities, policies, oversight and accountability, and establish how risk decisions are made and reviewed.
- Map: Describe the system’s purpose and context, its components and dependencies, potential impacts, and the people or groups affected.
- Measure: Assess and monitor relevant risks using appropriate evaluation methods and evidence.
- Manage: Prioritize risks and decide how to address them, including whether to deploy, change, restrict or retire a system.
Governance is cross-cutting rather than a one-time opening step. NIST’s AI RMF Core says, “Attention to governance is a continual and intrinsic requirement for effective AI risk management over an AI system’s lifespan and the organization’s hierarchy.” Organizations can apply the functions in ways suited to their needs and resources. NIST says AI RMF 1.0 is being revised as part of the White House AI Action Plan, so it should not be treated as an unchanging rule. See the NIST AI RMF overview and AI RMF Core.
What to decide before an agent receives access
Use the lifecycle framework to turn broad governance principles into concrete decisions about each agent. The appropriate controls depend on the agent’s purpose, the data and systems it can reach, the consequences of its actions, and the organization’s risk tolerance; there is no universal approval threshold.
1. Inventory the agent and assign accountable owners
Record the agent as part of the organization’s AI system inventory. Document its purpose, owner, operator, connected tools and data sources, dependencies, and the people responsible for approving changes and reviewing its operation. NIST’s Govern outcomes include inventory mechanisms, clear roles, monitoring and review, and safe decommissioning. Apply those lifecycle practices to agents as well: a system should have an accountable owner while it is active and a controlled path for retiring its access when it is no longer needed.
2. Map context, affected parties and dependencies
Before deployment, describe the task the agent is meant to perform and the context in which it will act. Identify affected people, potential impacts, connected systems, and relevant third-party software or data. NIST’s Map function is intended to inform an initial decision about whether to design, develop or deploy an AI system—not just to document a decision already made. If the organization cannot explain what the agent can affect or which dependencies its work relies on, its risk assessment is incomplete.
3. Give the agent a distinct identity and limited authority
Access controls should distinguish the agent from the human who delegated a task. Define the agent’s identity, the rights and entitlements it needs, and the systems where those permissions apply. Preserve enough linkage between the agent, the delegating user and consequential actions to support accountability. The NCCoE concept paper identifies agent identification, authorization, and links between user identities and agents as areas for project work; it does not prescribe a finished implementation.
As a practical design principle, grant only the authority needed for the assigned task, and make the permitted scope explicit. Avoid treating a user’s broad access as a reason to give an agent the same broad authority. Review access when the task, connected systems or agent change, and revoke it when the work ends.
4. Separate autonomous actions from actions requiring approval
Specify which actions an agent may take on its own and which must pause for human approval. A low-impact recommendation may be suitable for independent processing, while a consequential change to a sensitive system may require a person to review the proposed action first. Define the boundary using the likely impact and reversibility of an action, the sensitivity of accessed information, and the consequences of an error. NIST’s project identifies authorization and access delegation as areas of focus and recognizes a range from human-in-the-loop approval to autonomous action; it does not establish one threshold for every organization.
5. Monitor performance, incidents and changes
Set up review appropriate to the agent’s role: evaluate whether it is operating as intended, whether its access remains justified, and whether its actions or recommendations create unexpected effects. Establish how staff can report concerns, how incidents are identified and handled, and what contingency processes apply if the agent or a dependency fails. NIST’s governance outcomes also cover third-party software and data risks, workforce responsibilities and training, human-AI oversight, testing, incident identification and feedback. These controls make oversight part of normal operation rather than a response reserved for a serious failure.
Enterprise use cases—and why their controls differ
The NCCoE concept paper names three areas for its initial consideration, emphasizing enterprise settings where organizations may have greater control and visibility over agents and the systems they access. These are potential use cases in a concept document, not evidence of universal adoption or proof that every organization should deploy them.
| Potential use case | Examples named by NIST | Governance issue to examine |
|---|---|---|
| Workforce efficiency and decision support | Calendar management, assessing or creating policy documents, and generating decision recommendations | Whether delegated access across multiple data sources is necessary, limited to the task and attributable to a responsible user. |
| Security operations | Analyzing security information and recommending or taking actions | Security data is sensitive; determine which actions can be autonomous, which require approval and how actions are reviewed. |
| Software development and deployment | Automated processes in deployment pipelines | Define how entitlements and authorization apply when an agent participates in pipeline steps or can affect deployed software. |
The same agent capability can carry different risks in different settings. A recommendation about a policy document, access to security information, and an action in a deployment pipeline do not have identical consequences or permission needs. Assess each system in its actual operational context rather than approving an agent category in the abstract.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the frameworks and approaches differ
These resources address related but distinct questions. The NIST AI RMF supplies a voluntary lifecycle risk-management structure; the NCCoE paper proposes identity and authorization-focused implementation work; and SANS describes a maturity model for AI security. They are not interchangeable, and the sources do not establish a universal winner.
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| Approach | Purpose and scope | Status and authority |
|---|---|---|
| NIST AI RMF 1.0 | Organizes AI risk management across govern, map, measure and manage. | Voluntary NIST guidance released January 26, 2023; NIST says it is being revised. NIST overview |
| NIST NCCoE agent identity and authorization project | Explores applying identity standards and practices to agent identification, authorization and delegated access. | February 2026 concept paper describing proposed work and seeking feedback; desired implementation-oriented guidance and a possible practice guide are not yet completed deliverables. NCCoE concept paper |
| SANS AI Security Maturity Model | Provides an operational maturity-staging approach for AI security. | SANS announced its model on May 12, 2026, and says it has five maturity stages. SANS says an appropriate target depends on adoption pattern, industry, regulatory environment and risk tolerance; this is SANS’s description of its model, not a binding standard. SANS announcement |
When choosing how to use these resources, compare the operational detail they provide: whether they help assign owners, define controls, produce evidence and set a review cadence. Then fit the approach to the organization’s sector, jurisdiction, system risk, deployment pattern, staffing and existing governance requirements. A maturity target should reflect the organization’s circumstances, not simply the highest available stage.
What responsible deployment looks like in practice
A deployable governance plan translates the decisions above into artifacts and operating routines that staff can use. For each agent, the organization should be able to answer:
- What task is the agent authorized to perform, and who owns that use?
- Which identity does the agent use, who delegated its work, and how are consequential actions linked to that delegation?
- What data, systems and permissions can it access, and why are they necessary?
- Which actions may run autonomously, and which require approval?
- How will the organization detect problems, review performance, respond to incidents and safely remove access or retire the agent?
These are governance questions, not a substitute for applicable law or sector-specific obligations. Because requirements vary by jurisdiction, industry and use, organizations must determine which rules apply to their own deployment rather than infer a legal duty from a voluntary framework or concept paper.
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