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Before connecting an AI agent to enterprise apps, establish what identity it uses, limit its access to the task, set clear rules for acting on a person’s behalf, decide which actions need approval, and plan how to detect and contain mistakes or misuse. Treat the connection as an ongoing security decision—not a one-time setup. NIST is developing practical guidance in this area; its current publications raise important design questions but do not amount to a finished, universal agent-security checklist.
What to check before connecting an AI agent to enterprise apps
Start with a written description of the proposed connection: its business purpose, owner, operating boundary, connected applications, and actions it may take. Use that description to decide whether the agent should have access at all, what evidence you need before approving it, and who is responsible for reviewing it later.
- Identify the agent and its owner. Record the agent’s purpose, organizational owner, and identity in your enterprise architecture.
- Inventory its reach. List each application, data set, tool, and action it could access, including access inherited through connected systems.
- Define authority. For each action, specify whether the agent acts under its own identity or with authority delegated from a person.
- Set approval and safety boundaries. Decide which actions may run automatically, which require human approval, and how the agent can be stopped.
- Require evidence and review. Confirm that identity, access, approvals, and resulting actions can be investigated, then assign an owner for ongoing review.
NIST’s February 5, 2026 concept paper on software and AI agent identity and authorization frames many of these points as open design questions. Use them to guide your assessment, not as finalized requirements that settle every implementation choice.
What identity and credentials will the agent use?
Do not let “the agent” remain an ambiguous label in access records. Determine whether it has a distinct enterprise identity, uses a person’s delegated identity, or uses both in different circumstances. Make the identity’s owner and purpose clear enough that administrators can distinguish the agent from a person, another agent, or an ordinary application integration.
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Decide how the identity works
- Specify whether the agent’s identity is fixed or varies by task or user context, and what metadata is needed to identify it and its organization.
- Choose how the agent authenticates and what evidence a connected application receives about its identity and authority.
- Define who issues credentials, how they are updated or rotated, and how they are revoked when the agent, integration, owner, or business need changes.
- Keep tokens and assertions under the same security scrutiny as other credentials; decide how they are protected, verified, and invalidated.
NIST’s September 15, 2026 token and assertion implementation recommendations address key management, token verification and lifecycle controls, and protection of single sign-on, federation, and API access. Apply that guidance where relevant to the credentials in your integration; it is not a complete design for agent authorization or governance.
What permissions should an AI agent have?
Grant only the application, data, and action access needed for approved tasks. Review the full path from the agent to each resource, including tools or integrations that can expand its reach. A connection that looks read-only at one boundary may still expose sensitive information when the agent can combine data from several sources.
Make the permission boundary specific
- Inventory tools, applications, data sets, and permitted operations individually rather than granting a broad role simply because it is convenient.
- Check whether adding a data source, tool, or cross-system aggregation changes the sensitivity of the information the agent can return.
- Decide whether access should change with the user, task, or other context, and how that change will be enforced.
- Set a process to suspend or revoke access when context changes or monitoring indicates the agent is acting outside its approved purpose.
NIST’s concept paper asks how least privilege, dynamic authorization, and data sensitivity should work for agents. It does not establish one authorization model as correct for every enterprise; the organization must define the boundary for its applications and risk.
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When should an agent act for a user, and when should a person approve?
For each operation, distinguish the agent’s own authority from authority delegated by a person. Decide what evidence establishes that the person authorized the specific action, and whether that authorization remains valid for the task and context. Do not treat a user’s general access to an application as automatic permission for an agent to perform every action available to that user.
The following is a policy-design aid, not a universal NIST rule. Choose the approval boundary according to the impact, reversibility, and reach of each operation.
| Action class | Example | Approval policy to consider |
|---|---|---|
| Read-only retrieval | Finding a document or summarizing permitted records | May be suitable for automation if the agent’s data scope and user authority are explicit. |
| Reversible change | Editing a draft or updating a low-impact internal record | Consider limits, a review step, or a reliable way to undo the change. |
| Consequential change | Sending an external communication or changing a production system | Consider requiring human approval before execution, with the approving authority recorded. |
NIST identifies delegated authority and human-in-the-loop authorization as design questions in its February 2026 concept paper. Set a clear boundary for your organization rather than assuming that all actions should be autonomous or that all actions require identical approval.
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How will you test for prompt injection and unsafe actions?
Assume that content the agent reads may contain instructions designed to manipulate it. Email, documents, web pages, tickets, and other connected data can carry hostile text even when the system hosting that content is normally trusted. Test whether such content can cause tool use or changes the requesting user did not intend.
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- Use realistic test cases with malicious or conflicting instructions embedded in retrieved content.
- Check whether those instructions can trigger tool calls, reveal information beyond the task, or alter data without the intended authorization.
- Limit available tools and operations so an unsafe instruction cannot turn into an unrestricted change.
- Monitor access and action patterns, and make sure someone can halt activity when behavior is suspicious.
- Repeat the assessment when integrations, tools, models, or permitted data change.
NIST’s January 12, 2026 request for information on securing AI agent systems describes indirect prompt injection, insecure models, and harmful agent behavior that may occur without adversarial input. In a January 17, 2025 technical blog, NIST staff described agent hijacking as malicious instructions placed in ingested data that can cause unintended actions. That dated warning identifies a threat mechanism; it does not establish how prevalent the vulnerability is across current systems. NIST’s blog on strengthening agent-hijacking evaluations discusses the issue.
What records do you need to investigate an agent’s actions?
Decide in advance what evidence would let an investigator reconstruct an action and the authority behind it. Depending on the integration and risk, records may need to show the agent identity, the user or delegating authority, task context, tools invoked, resources accessed, approvals, and resulting changes.
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- Define which team can review activity and how unexpected actions are escalated.
- Protect the integrity of records so reviewers can assess whether the evidence is complete and trustworthy.
- Establish how access will be halted and credentials revoked during an incident.
NIST’s concept paper raises auditable actions and intent, verifiable logging, non-repudiation, and links between actions and human authorization as open questions. Do not assume that logs are tamper-proof or that a record alone proves who authorized an action; those properties depend on the implementation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare deployment options?
Compare configurations against the same operational questions, rather than choosing on the basis of a feature label. For each option, document what it can actually enforce and what evidence it produces.
| Decision area | What to verify |
|---|---|
| Identity model | Whether the agent has a distinct identity, uses delegated user authority, or combines both; whether ownership and lifecycle are clear. |
| Permission scope | Whether access is limited by application and action, and whether it can adapt to relevant context. |
| Approval boundary | Which operations may proceed autonomously and which require a person’s approval. |
| Credential controls | How authentication, credential issuance and revocation, token protection, and API or federation access are handled. |
| Audit evidence | Whether actions, intent, approvals, and delegated authority can be reconstructed and their integrity assessed. |
| Failure containment | How tool use is constrained, activity monitored, and access stopped or revoked after suspicious behavior. |
These are decision axes drawn from NIST’s identity, authorization, credential, and security questions—not a tested vendor scorecard. NIST’s Agentic AI Identity and Authorization project resource hub and project page describe work to develop practical implementation guidance; consult their current status rather than treating the project as a completed standard.
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What is established about enterprise agent security guidance?
NIST announced its AI Agent Standards Initiative on February 17, 2026, describing work on standards, protocols, security, and identity. On May 18, 2026, NIST published a summary analysis of responses to its AI-agent security RFI. It reports that commenters widely agreed agent security presents novel risks and that established cybersecurity practice may need adaptation. That is a summary of respondents’ views, not a measured incident rate or proof that a particular control is effective.
The practical implication is to use established identity, authorization, credential, monitoring, and audit practices while assessing agent-specific risks—especially delegation, model-driven actions, and hostile instructions in retrieved content. Review the relevant NIST material as it evolves, but make the connection decision based on your own threat model, applications, and accountable approval process.
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