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Choose an AI agent security platform by proving that it can discover the agents your organization actually uses, connect each agent to an accountable owner and identity, restrict what it can access, monitor its live actions, and cover the places agents run—including those outside centrally managed tools. Use the same representative scenarios to test every finalist. Vendor descriptions can help form a shortlist, but the available sources do not provide independent, comparable product testing that supports naming a universal winner.
Why agent security requires more than monitoring AI-generated text
A chatbot that produces an answer and an agent that takes action create different security problems. Microsoft describes autonomous agents as systems that plan, execute and adapt toward goals. An agent may call APIs, use tools, access company data or coordinate across services. Those capabilities can make it useful, but they also create routes to unintended actions, data exposure and abuse of delegated access.
The practical security question is therefore not only, “Is this response safe?” It is also, “What can this agent do, using which identity, with access to which resources, and can we stop it when it behaves outside policy?” An agent can produce a seemingly ordinary response while invoking a tool or service behind the scenes. Controls that only inspect text may not give security teams enough visibility or authority over those actions.
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Define what counts as an agent in your environment
Before comparing platforms, agree on the systems and components the organization wants to find and govern. A narrowly defined inventory of approved, centrally managed agents can miss the less visible systems that make shadow AI a concern. Include the agents and dependencies that users, teams and developers can create or connect, even if they are not registered in a central AI platform.
- First-party and internally developed agents: Include agents built by employees or engineering teams, whether they run on a managed platform or in a custom application.
- Platform-hosted and embedded agents: Include agents provided within business applications and cloud platforms, not just standalone AI applications.
- No-code applications: Include agentic workflows created with no-code tools or third-party services. Cisco characterizes no-code and third-party agentic applications as a high shadow AI risk area in its vendor-authored material.
- Agent components and connections: Inventory MCP servers, tools, integrations, connected services and the identities used to reach them. An agent record without its dependencies can conceal the access path that matters during an incident.
- People and business accountability: Record the responsible human or business owner, not only the technical deployment or platform that hosts the agent.
Decide what constitutes an agent for inventory and policy purposes, but do not use a narrow definition to exclude the tools, identities or services through which it acts. Cisco notes that agents can be created dynamically without registration or decommissioning; an inventory that is only updated when a team formally registers a deployment may therefore become stale.
Evaluate the capabilities that matter most
Use these questions to structure demonstrations and a proof of concept (PoC). Ask vendors to show the same scenarios against the same parts of your environment. A feature description is a starting point; the useful evidence is what the product can discover, control and report in your deployment.
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| Capability | What to establish | Evidence to request |
|---|---|---|
| Discovery and inventory | Can it find sanctioned and unsanctioned agents and applications, including MCP servers, tools, integrations and owners? How and how often is the inventory refreshed? | A view of what was found, where it runs, what it connects to and how the product handles an unknown or newly created agent. |
| Identity and lifecycle | Can each agent be associated with an accountable person or business owner? Are non-human identities and credentials visible, attributable, scoped, rotated or revocable? | A demonstration of identifying an orphaned or overprivileged identity and showing the steps to limit or revoke its access. |
| Least-privilege authorization | Can permissions vary by agent, user, task, resource and context? Can access be time-bound? Can enforcement operate outside the model’s generated output? | A test in which an agent is allowed to perform an approved action but denied access to a different resource or operation. |
| Runtime protection | Does the product observe requests, responses and tool calls? Can it flag or block unsafe, anomalous or unauthorized actions before impact? | A live demonstration of detection, intervention and an auditable record of what happened. |
| Environment coverage | Which network, endpoint, browser, SaaS, cloud and agent-framework paths are covered? What traffic or activity falls outside the inspected path? | A coverage map for your actual deployment, including known blind spots and the conditions under which visibility is lost. |
| Governance evidence | Can activity, ownership, policies and lifecycle decisions be exported for security operations and audit? | A sample export showing whether records are attributable and useful for investigation, rather than only a high-level dashboard. |
| Operational fit | Can your teams operate, tune and investigate the controls? What happens to approved use cases when a policy flags activity? | A customer-specific exercise that includes investigation, policy adjustment and a check for disruption to legitimate work. |
Start with discovery, then test its blind spots
Discovery is foundational: an organization cannot reliably govern agents it does not know exist. Ask the vendor to identify both approved and unsanctioned AI applications and agents, then inspect whether the result includes the associated tools, services, identities and owners. Find out how the inventory is refreshed and how it reflects agents that appear, change or disappear over time.
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Do not assume that a single collection method sees every interaction. The Cloud Security Alliance’s May 2026 research note says conventional CASB inspection can miss activity through browser extensions, AI features embedded in SaaS applications and personal accounts used on managed devices. It discusses browser-native visibility as an additional layer. Treat that as a coverage question to validate in a PoC: which activity does the product observe, through what path, and what happens when that path is bypassed?
Map the answer to your own environment rather than accepting a generic claim of broad coverage. For each relevant route—such as a managed network connection, browser, endpoint, embedded SaaS feature or cloud-hosted agent—record whether the platform can see the agent and its actions. Ask for a demonstration on the route itself. If an action is invisible because it occurs outside an inspected path, document that gap and decide whether another control or deployment approach is needed.
Check agent identity, ownership and lifecycle controls
An agent’s effective permissions depend on the identity and credentials it uses, not just the model or application that hosts it. Ask whether the product can tie an agent to a responsible human or business owner and make its non-human identities visible. Determine whether credentials can be scoped, rotated and revoked, and whether the platform can identify identities that are orphaned or overprivileged.
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Test the lifecycle end to end. For a representative agent, show how an owner is recorded, how a change in ownership is reflected, and how access is removed when the agent is retired or no longer authorized. An inventory that shows an agent but cannot answer who is accountable for it may help with discovery without solving the governance problem.
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Separate attribution from authorization. Knowing which identity an agent uses does not itself restrict that identity’s actions. Ask how the platform supports least privilege and whether controls can be applied to the agent, user, task, resource and context. Where it claims time-bound access, test the expiration and what happens when the agent attempts an action after the permitted window.
Prove that controls apply to actions, not just model output
OWASP’s AI Agent Security Cheat Sheet identifies practices to avoid, including unrestricted tool access or wildcard permissions, arbitrary code execution without sandboxing, sensitive memory without protection, high-impact decisions without human oversight, and reliance solely on model output for authorization. Use these as prompts for testing the controls around an agent’s tools, data and decisions.
For example, configure a representative agent with a legitimate task and then attempt an action outside that task’s authorization. Ask the vendor to show whether the platform can distinguish the approved operation from the unauthorized one, where the decision is enforced, and whether the event is recorded. A model-generated refusal alone does not demonstrate that an external authorization control prevented access.
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Run a proof of concept against realistic scenarios
Keep the PoC small enough to operate, but representative enough to expose gaps. Select an internally developed or managed agent, a hosted or embedded agent, and a less centrally controlled workflow if those exist in your environment. Include relevant tools, connected services and identities rather than testing an isolated model interaction.
- Write down the expected inventory: List the agents, applications, MCP servers, tools, integrations and owners that should appear in the test environment. Include one item that is not formally registered if your organization can safely test that case.
- Check discovery and refresh: Ask the platform to find the listed items and show how it refreshes the inventory. Introduce or change a test component through an approved process and observe whether the inventory reflects it.
- Trace identity and access: For a test agent, identify its owner, credentials and accessible resources. Verify whether an overbroad or unowned identity can be identified and whether access can be narrowed or revoked.
- Exercise an allowed action: Have the agent perform a normal, approved task that uses a tool or connected service. Confirm that the product observes the relevant request and tool action without blocking the use case unnecessarily.
- Exercise a denied action: Attempt a representative action that should not be allowed, such as reaching a resource outside the agent’s task. Verify whether enforcement occurs outside the model’s own response and whether the intervention is recorded.
- Test a visibility boundary: Run a safe test through the browser, embedded SaaS feature, network path or other route where visibility matters. Compare what the platform records with what you expect it to observe.
- Review evidence and operations: Export or inspect the records, then have the security team investigate the event and adjust a policy. Note what information is available and whether the adjustment affects the approved task.
Use a common scorecard for all finalists, but do not collapse materially different evidence into a single feature count. Record whether a capability was demonstrated, what environment and configuration it required, what was not visible, and whether the team could investigate and act on the result. The reviewed sources provide no independent comparative measurements of product performance, false positives or deployment effort; those questions require customer-specific testing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use governance frameworks as a completeness check
Microsoft’s governance model groups the work into control-plane governance, data governance and compliance, and security. It emphasizes knowing which agents exist, who owns them, what they can access and how to intervene when behavior falls outside policy. Use those dimensions to check whether your evaluation has omitted a governance responsibility, while recognizing that Microsoft’s documentation uses its own ecosystem components as examples.
OWASP’s framework crosswalk connects 51 vulnerabilities across four source lists to controls in 25 frameworks, including NIST, ISO, MITRE ATLAS and the EU AI Act. Such mappings can help teams organize a control review and discuss evidence with governance stakeholders. They do not certify compliance, prove that a platform is effective, or replace validation of the control in the environment where agents run.
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Interpret vendor examples without mistaking them for a ranking
Vendor-authored material can show the kinds of capabilities a buyer should ask about, but it is not independent proof that competing products perform equally or that any one product is best for every organization.
- Cisco: Its Zero Trust for Agentic AI material describes inventory for agents, MCP servers and tools; agent-to-owner mapping; least-privilege and time-bound access; and real-time monitoring and enforcement of requests, responses and tool calls. It also highlights no-code and third-party applications as a shadow AI risk area.
- Palo Alto Networks: Its white paper emphasizes agent identity, privileges, access and continuous discovery that includes shadow agents.
- Microsoft: Its governance documentation describes control-plane, data and security layers, with examples from Microsoft services.
These descriptions are useful as prompts for a demonstration: ask each shortlisted provider to show discovery, identity, authorization, runtime enforcement and exportable evidence in the same scenarios. Product functions and availability can change, so verify the current offering and relevant integrations directly with the provider as part of selection.
Make the decision based on coverage and proof
Choose the platform that best fits the actual routes through which your organization’s agents are created and used, and that demonstrates the controls your security and operations teams can run. A shortlist should not advance solely because it has a broad framework map, a polished inventory screen or a convincing description of runtime protection. Tie each requirement to a demonstration and record the evidence and limits.
Before committing, make sure stakeholders agree on what the platform will cover, what remains outside its visibility, who owns agent lifecycle decisions, and how the team will respond when it blocks or flags an action. No independently verified, directly comparable statistic on platform effectiveness or shadow AI prevalence is established in the cited vendor and OWASP sources; selection should rest on your own coverage and control tests rather than an unsupported market-wide claim.
Quick Recap
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