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Choose an enterprise AI agent security solution by testing what identity the agent uses, what authority it receives, which tools and data it can reach, and whether you can observe and control its actions at runtime. Do not treat a connector list, a standards reference, or a vendor’s security claim as proof that the agent is safe. Turn each requirement into a test in your intended deployment, then compare candidates against the same evidence-based criteria.
This guide explains how to scope an evaluation, assess agent identity and integrations, test runtime controls, and use current NIST and OWASP material without mistaking guidance for product certification.
What to require from an enterprise AI agent security solution
An AI agent can act through tools, APIs, enterprise applications, and data sources. That makes its security depend on more than the model or the prompt: the agent’s identity, delegated authority, integrations, runtime behavior, and audit trail all matter. NIST’s February 5, 2026 concept paper frames the central concern as giving agents access to diverse datasets, tools, and applications, and calls for appropriate identification and authorization controls.
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- Identity: Can you identify the agent as a principal and understand how it authenticates to each connected system?
- Authorization: Can you bound and review its permissions, including authority delegated from a user or another agent?
- Control: Can policy constrain consequential actions while the agent is operating?
- Evidence: Can you trace what happened, who or what initiated it, and which policy and permissions applied?
These are evaluation dimensions, not a checklist that by itself guarantees security. Fit them to the sensitivity and impact of the use case, then verify each claim in the environment where the agent will run.
Scope the use case before evaluating products
Security requirements are difficult to compare if candidates are evaluated against different assumptions. Define the intended task and its boundaries first. For example, an agent that drafts an answer from approved documents has a different action surface from one that can modify records, send messages, or execute transactions. The example is a scoping distinction, not a claim that one category is inherently safe.
Build an access and action inventory
List every dataset, tool, API, enterprise application, and agent protocol the proposed system would touch. For each item, record the business purpose, the data it exposes, the operations the agent needs, and the person or team responsible for approving access. Include both read and write operations; do not infer the permitted actions from a connector’s name.
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- Separate actions that are read-only from actions with external or durable effects.
- Record whether an action requires a human decision, a second authorization check, or another safeguard in your design.
- Include indirect access, such as an agent calling a tool that can reach additional systems.
This inventory turns “supports our systems” into a set of concrete integration tests. A product logo or compatibility statement does not establish which operations are available, how permissions are propagated, or how access is revoked.
Set the risk and oversight level
Assess the use case’s potential impact, data sensitivity, and need for human oversight. Decide which actions are acceptable without review and which require confirmation or a separate authorization step. Document who can approve exceptions and how changes to the agent, its tools, or its permissions will be reviewed.
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NIST AI RMF 1.0 is a voluntary framework for considering trustworthiness across AI design, development, use, and evaluation. NIST says the framework is under revision. Use it as broad risk-management context, not as an agent-security product certification or a substitute for implementation tests.
Evaluate identity, credentials, and authorization
Ask whether each agent has an identifiable principal, how that principal authenticates to downstream systems, and how its authority is established. Identity and authorization are related but distinct: knowing which agent is acting does not by itself establish whether that action is permitted.
Questions to ask vendors
- Is each agent represented as a distinct principal, or does it use a shared service identity? If identities are shared, how are individual agents and actions distinguished?
- How does the agent authenticate to each connected application, and where are its credentials or tokens managed?
- Can credentials be scoped to the required systems and operations, rotated, expired, and revoked? Ask for a demonstration of each lifecycle action.
- How is authority delegated from a user, task, or other agent? Can the organization see the delegation chain and place limits on it?
- Can authorization be checked again before a privileged or consequential action, rather than relying only on permissions granted earlier?
- Can an administrator identify and disable one agent’s access without unintentionally removing unrelated identities or disrupting other workloads?
Request a clear explanation of whether access is associated with a user, an agent, a task, or a combination. Then test the actual behavior. For example, compare what the agent can access when acting for users with different permissions, and verify that the result matches the intended policy. The test should include a denied action, not only a successful one.
Make accountability testable
For a consequential action, require evidence that lets an investigator attribute it to the relevant agent and understand the authority used. Check whether records show the actor, action, target system, time, outcome, and relevant authorization or policy decision. Confirm that your organization can retrieve and review those records in its normal operating process.
NIST’s February 2026 concept paper raises identification, authorization, auditing, and non-repudiation as relevant concerns. The NCCoE’s Agentic AI Identity and Authorization project is developing implementation-oriented resources; its planned SP 1800-series practice guide is not yet a completed guide. Its project hub reports more than 600 responses to the February 2026 concept paper. That is a consultation count reported by NIST, not an adoption or incident statistic.
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Test integrations as security boundaries
Every integration is a place where identity, permissions, data boundaries, logging, and failure behavior must work together. Evaluate the behavior of each integration in the proposed deployment rather than treating connector availability as sufficient evidence.
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| What to verify | Evaluation question | Evidence to request or observe |
|---|---|---|
| Authentication | How does the agent authenticate to this system, and which principal does the system see? | Configuration walkthrough and a record from the downstream system showing the authenticated actor. |
| Permission propagation | Do permissions reflect the intended user, agent, or task context? | Tests with different permission levels, including a denied access attempt. |
| Allowed operations | Can the integration restrict available actions to the operations required by the use case? | Demonstration of permitted and disallowed operations, including a write action if relevant. |
| Data boundaries | Can the agent retrieve or transmit only the data allowed by policy? | Tests using in-scope and out-of-scope records, with resulting logs. |
| Revocation | What happens when a credential, permission, or integration is withdrawn? | Observed revocation test and the agent’s behavior after access is removed. |
| Failure handling | What happens when authentication, a tool call, or a policy service fails? | Controlled failure test showing whether the action stops, retries, or proceeds. |
Run these checks end to end: from the agent’s request, through any policy or orchestration layer, to the downstream system and its records. A test that inspects only a product console may miss what the connected application actually authorized. Document the expected result before the demonstration so that an ambiguous or partial result is not scored as a pass.
Include the protocols and intermediary tools in your inventory. OWASP AISVS 1.0 includes a chapter on MCP security, alongside chapters on access control and identity, orchestration and agentic security, and monitoring and logging. If a protocol is in scope, select relevant, versioned requirements and verify the implementation rather than assuming that protocol support implies secure configuration.
Assess runtime controls and prompt-injection handling
Runtime controls matter because an agent’s actions unfold during execution. Ask whether policy can constrain tool calls and consequential actions as they occur, and whether operators can inspect, trace, or stop those actions. NIST identifies controls to prevent or mitigate prompt injection as a topic in its agent identity and authorization work. Treat a protection claim as a test objective, not a guarantee that prompt injection has been eliminated.
What to demonstrate
- How policies are expressed, reviewed, versioned, and applied to the agent’s actions.
- Whether a policy can distinguish allowed from disallowed tools, operations, targets, or data categories in the use case.
- What the system does when a tool call conflicts with policy, and what information appears in the audit trail.
- Whether operators can pause or stop an agent and whether that intervention takes effect before further consequential actions.
- How policy changes are tested and deployed, and whether the organization can identify which policy version governed an event.
Use controlled test cases that include an ordinary allowed action, an action outside the agent’s intended scope, and a prompt or retrieved content that attempts to induce an unauthorized action. Record what the agent attempted, what the control layer allowed or blocked, what the connected system received, and what the logs show. A blocked action is only useful evidence if the downstream system did not perform it and the control’s decision can be reviewed.
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OWASP’s Agent Control Standard, published September 1, 2026, describes middleware hooks and portable declarative safety policies enforced at runtime. This provides a useful way to frame questions about runtime enforcement and policy portability. It does not establish that a particular product implements those controls correctly; verify the product’s behavior and the policy coverage you need.
Use OWASP AISVS to structure verification
OWASP AI Security Verification Standard (AISVS) 1.0 was released in June 2026. It contains 191 requirements across 12 chapters and includes verification levels. OWASP describes it as a testable resource suitable for procurement. Its agent-relevant coverage includes access control and identity, orchestration and agentic security, MCP security, and monitoring and logging.
Use AISVS to make a security evaluation more systematic: select requirements related to the actual system and risk, record the standard version and requirement identifiers, choose an appropriate verification rigor, and attach evidence to each result. Do not assume that every requirement applies to every use case or that citing the standard proves conformance.
Create a requirement-to-evidence matrix
| Field | What to record |
|---|---|
| Requirement | The selected AISVS requirement and the version of the standard used. |
| Use-case relevance | Why the requirement matters for the agent, data, integrations, or action under review. |
| Verification method | Document review, configuration inspection, demonstration, or a controlled test. |
| Expected result | The observable behavior that counts as passing for this deployment. |
| Evidence | Test result, configuration example, relevant log, or change history, tied to the evaluated product version and deployment. |
| Outcome | Pass, fail, or unverified, with any scope limitation or unresolved gap. |
Ask vendors to map their evidence to the selected requirements. Confirm that materials describe the product version and deployment model being evaluated rather than a different release or a generic reference architecture. If the vendor cannot supply evidence, record the claim as unverified instead of awarding credit based on an assurance statement.
AISVS is not a replacement for verification of the wider application, infrastructure, and software supply chain. OWASP explicitly calls for those areas to be addressed in parallel. Keep them in the overall procurement review, but do not present general application or infrastructure checks as agent-specific tests.
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Check monitoring, logging, and operational response
Logs are useful only when they answer operational questions. During evaluation, determine whether the organization can reconstruct an agent’s action path: what initiated the action, which tool or integration was invoked, what authorization decision applied, whether a policy intervened, and what outcome the downstream system recorded.
Test the audit trail
- Run a permitted action against a test resource and capture the records available from the agent platform and the downstream application.
- Run a denied action and check whether the denial, reason, and actor are recorded.
- Trigger a policy-controlled action and compare the policy decision with the downstream system’s result.
- Have an operator locate the event using the organization’s normal monitoring workflow and confirm whether the records can be exported or reviewed there.
- Make a controlled configuration or policy change, then establish whether the resulting history identifies what changed and when.
Ask about retention, access to logs, export formats, and how records relate across components. Requirements depend on your organization’s policies and applicable obligations, so set the needed retention and access rules before scoring a candidate. A console screenshot alone may not show whether event data can be used in the organization’s operating process.
Evaluate interoperability and portability
Consider how the solution fits the identity provider, policy engine, logging or SIEM environment, data permissions, agent framework, and protocols that are actually in scope. Test those connections in the intended architecture; a statement that a product integrates with a category of system is not evidence of end-to-end behavior.
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NIST’s AI Agent Standards Initiative, created February 17, 2026 and updated August 14, 2026, describes work on voluntary guidance, open protocols, authentication and identity research, and security evaluations. These work areas indicate that interoperability is an active standards topic, not a settled assumption. Do not infer that a protocol or common format guarantees portable authorization or consistent enforcement.
Compare candidates with a consistent scorecard
Use the same criteria, deployment assumptions, test cases, and evidence standard for each candidate. Score demonstrated behavior separately from documentation and unsupported assertions. A simple scale can be useful if its meanings are fixed in advance:
- Demonstrated: The control passed the agreed test in the target environment, with reviewable evidence.
- Partially demonstrated: Some required behavior worked, but a defined use case, integration, or evidence element remains incomplete.
- Unverified: The claim is documented or asserted but has not been tested, or the evidence does not map to the evaluated version and deployment.
- Not met: The control failed the agreed test or the candidate cannot support the requirement.
| Comparison axis | Evidence to compare |
|---|---|
| Agent identity and credential lifecycle | Distinct principal behavior, authentication paths, credential scope, revocation, and attribution. |
| Least-privilege authorization and delegation | Permission boundaries, delegation behavior, re-evaluation for privileged actions, and denied-action results. |
| Tool and data integrations | Tested authentication, permission propagation, allowed operations, data boundaries, logs, revocation, and failure handling for each in-scope integration. |
| Runtime policy controls | Observed enforcement on tool calls or consequential actions, operator visibility, intervention behavior, and policy history. |
| Auditability and monitoring | Ability to attribute actions, inspect decisions, reconstruct outcomes, and use records in the organization’s workflow. |
| Interoperability and portability | Verified fit with required systems, policy and record export, and identified migration dependencies. |
| Quality of verification evidence | Mapping to selected, versioned requirements and evidence tied to the evaluated product and deployment. |
Weight the criteria according to the use case rather than treating every dimension as equally important. For an agent with access to sensitive data or consequential actions, an identity or authorization failure may be a gating issue; for another use case, a different requirement may be decisive. Record the rationale for weights and any minimum threshold before scoring, so the result is explainable and comparable.
Run a procurement evaluation in stages
- Define the use case and boundaries. Document the intended task, data sensitivity, systems, required actions, prohibited actions, human oversight, and accountable owners.
- Set requirements. Select agent-specific checks, including identity, authorization, integrations, runtime controls, monitoring, and portability. Use relevant versioned AISVS requirements and keep general application, infrastructure, and supply-chain checks in parallel.
- Request evidence before demonstrations. Ask each vendor for current documentation, configuration examples, test evidence, and an explanation of the product version and deployment to which the material applies.
- Prepare identical test cases. Define expected outcomes for allowed, denied, revoked, policy-controlled, and failure scenarios. Use equivalent assumptions for every candidate.
- Test in the target environment. Validate the complete path from agent to integration to downstream system, including authorization results and logs. Avoid scoring a slide presentation as a test.
- Score evidence and gaps. Mark unsupported claims unverified, distinguish partial from complete results, and document remediation, ownership, and any conditions for deployment.
- Make approval conditional on closure. For unresolved gaps, decide whether the use case can be narrowed, additional controls can be implemented, or the candidate should not proceed. Record who accepts any remaining risk and what must be retested after a change.
Common procurement mistakes to avoid
- Equating integration availability with security. A connector says little by itself about authentication, permission propagation, allowed operations, or revocation.
- Treating a standards reference as certification. AISVS is a verification resource; NIST AI RMF is a voluntary framework. Neither reference alone proves that a commercial product meets your requirements.
- Accepting prompt-injection protection as a guarantee. Test the behavior and downstream outcome for the scenarios relevant to your deployment.
- Scoring claims instead of evidence. Map proof to a requirement, product version, and target deployment. Label missing or mismatched proof unverified.
- Ignoring broader security scope. Agent-focused tests complement, rather than replace, application, infrastructure, and supply-chain verification.
- Assuming the standards landscape is settled. NIST’s initiative is examining open protocols and agent identity infrastructure, while implementation resources are still in progress. Evaluate interoperability as a present requirement, not a future guarantee.
Standards and guidance to include in the evaluation
| Resource | What it contributes | How to use it |
|---|---|---|
| OWASP AISVS 1.0, June 2026 | 191 testable requirements across 12 chapters, with verification levels; includes agent-relevant identity, orchestration, MCP security, and monitoring/logging coverage. | Select and version the requirements that fit the use case; attach test methods and evidence. Do not treat it as a product endorsement. |
| NIST AI agent identity and authorization concept paper, February 5, 2026 | Frames the access risks of agents using diverse data, tools, and applications; raises identification, authorization, auditing, non-repudiation, and prompt-injection concerns. | Use its themes to shape identity and access questions and test cases. |
| NCCoE Agentic AI Identity and Authorization project | Work in progress toward implementation-oriented resources; the planned SP 1800-series practice guide is intended to provide example implementations, architectures, build details, and lab lessons using commercially available technologies. | Track it as developing implementation guidance, not as a completed guide or product approval. |
| NIST AI Agent Standards Initiative, created February 17 and updated August 14, 2026 | Describes voluntary guidance, open protocols, authentication and identity research, and security evaluations. | Use as context for interoperability and evolving standards work. |
| OWASP Agent Control Standard, September 1, 2026 | Describes middleware hooks and portable declarative policies enforced at runtime. | Use to formulate runtime-control questions, then verify implementation behavior. |
| NIST AI RMF 1.0, January 26, 2023 | Voluntary, broad risk-management context for AI design, development, use, and evaluation; NIST says it is under revision. | Use alongside testable agent-security requirements, not as a security certification. |
| NIST SP 1800-35, final June 10, 2025 | Zero Trust implementation guidance with 19 examples developed with 24 collaborators; it is not agent-specific. | Use for enterprise implementation context, not as proof of agent-specific controls. |
| OWASP Securing Agentic Applications Guide 1.0, July 27, 2025 | Practical technical recommendations for secure agentic application design, development, and deployment. | Use as technical context alongside product-specific verification. |
No named vendor or product comparison is established here. For any candidate, obtain current vendor documentation and test the controls in the intended environment. Product features, pricing, certifications, regional availability, and deployment models must be verified directly before they are used to make a purchasing decision.
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