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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteChoose an AI agent security platform by testing whether it can discover your organization’s agents, tie each agent to an accountable owner and identity, limit what it can access, monitor and control its actions at runtime, and cover the ways agents actually enter your environment. Use the same proof-of-concept scenarios across shortlisted products. Vendor descriptions can help shape a shortlist, but the available evidence does not establish a head-to-head winner.
Why AI agents need controls beyond ordinary AI output filters
An AI assistant that only drafts text presents a different security problem from an agent that can plan, call tools, access company data, invoke APIs, or coordinate work across services. With those capabilities, a model’s output can become an action: a tool call, a change to a record, or a request for data. That creates risks involving unintended actions, data exposure, and indirect prompt injection.
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Microsoft describes autonomous agents as systems that plan, execute, and adapt toward goals. OWASP’s December 2025 announcement of its Top 10 for Agentic Applications identifies behavior hijacking, tool misuse and exploitation, and identity and privilege abuse among the risks. The practical selection implication is that a platform should help govern what an agent can do and what it can reach—not only what it says.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Security starts with knowing which agents exist. An organization may have centrally managed deployments, agents hosted by platforms, no-code applications, internally developed agents, or tools connected through protocols such as MCP. Some may be created dynamically without registration or removed without a formal decommissioning process. If discovery only covers deployments that employees registered with IT, it may miss part of the estate.
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OWASP’s Keren Katz, co-lead of its Top 10 for Agentic AI Applications, put the visibility problem plainly in the December 2025 announcement: “Companies are already exposed to Agentic AI attacks – often without realizing that agents are running in their environments.” Treat that as a reason to test discovery, not as a prevalence statistic: the available sources do not establish an independently verified rate of shadow-agent use.
Start by defining what “coverage” means in your environment
Before comparing products, map how employees and systems can interact with AI. The relevant paths may include network traffic, managed endpoints, browsers, SaaS applications with embedded AI features, cloud services, and agent frameworks. List the tools and connected services that agents can use, too. A platform’s visibility depends on how those paths are observed; do not assume a single inspection method sees every interaction.
A May 2026 Cloud Security Alliance research note says conventional CASB inspection can miss interactions through browser extensions, AI features embedded in SaaS applications, and personal accounts used on managed devices. It discusses browser-native visibility as another layer. Use this as a proof-of-concept question: which specific traffic and interactions can the product see in your deployment, and which bypass the inspection path?
Build a representative inventory of paths and agent types
- Managed agents: Include agents deployed through platforms already governed by IT or security.
- Shadow and employee-facing tools: Include unsanctioned AI applications, browser extensions, and AI features embedded in SaaS products.
- Developer and automation paths: Include internally built agents, no-code applications, MCP servers, tools, APIs, and connected services.
- Identity and ownership: Record the human or business owner, the identity an agent uses, and the permissions and resources available to it.
This is a scoping checklist, not a claim that every platform supports every environment. Cisco’s vendor-authored material describes discovery and inventory spanning agents, MCP servers, and tools, while Palo Alto Networks’ white paper emphasizes discovery that includes shadow agents. Ask vendors to demonstrate coverage for your own paths and agent types.
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Evaluate the five controls that determine whether a platform is useful
A selection should test the whole chain from discovery to intervention. An inventory without ownership leaves unclear who can correct a risky deployment. A runtime alert without authority to block or contain an action may arrive too late. A strong model-level policy cannot compensate for credentials that give an agent broad access outside the model.
1. Discovery and continuously maintained inventory
Ask the vendor to show how it identifies sanctioned and unsanctioned AI applications and agents, and whether the inventory includes MCP servers, tools, integrations, connected services, identities, and owners. Test how the inventory is refreshed, how newly created agents are surfaced, and what happens when a previously seen agent disappears. Cisco notes that agents can be created dynamically without registration or decommissioning; a one-time scan is therefore not enough to establish ongoing visibility.
2. Identity, ownership, and lifecycle
Each agent should be attributable to a responsible person or business owner, and its credentials should be visible, scoped, and revocable. Test whether the platform can identify orphaned or overprivileged non-human identities, and whether an agent can be disabled or its access withdrawn when an owner leaves, a project ends, or a credential is compromised. A display name or inventory entry alone is not proof that the underlying identity is controlled.
3. Least-privilege authorization
Test whether access can be limited by agent, user, task, resource, and context, and whether permissions can be time-bound. Ask where authorization is enforced. If a model is merely prompted not to use a tool, the policy relies on generated output; OWASP’s AI Agent Security Cheat Sheet explicitly warns against relying solely on model output for authorization. The control should be enforced where the agent’s access or action is actually mediated, not just expressed as an instruction to the model.
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4. Runtime monitoring and intervention
Determine whether the product observes agent requests, responses, and tool calls, and whether it can flag or block unsafe, anomalous, or unauthorized actions before they produce an impact. For each intervention, ask what the operator can see afterward: the action attempted, the identity used, the policy applied, the outcome, and who approved or overrode it. An alert that cannot be tied to an agent and its action is difficult to investigate; an intervention without an audit trail is difficult to govern.
5. Coverage, integrations, and operational fit
Compare the product’s supported network, endpoint, browser, SaaS, cloud, and agent-framework paths with the environment you mapped. Ask what visibility is lost when traffic does not traverse an inspected path and which integrations are needed to restore it. Then have the security team operate the product against realistic use cases: can analysts tune policies, investigate activity, and respond without disrupting approved work? False positives and operational effort need customer-specific testing; the reviewed sources provide no independent comparative measurements for them.
Use a proof of concept that tests actions, not just dashboards
A useful proof of concept (PoC) gives every shortlisted vendor the same scenarios and expected outcomes. A guided dashboard tour may demonstrate a feature, but it does not establish that the product finds your agents, sees the relevant activity, or enforces your policies in your architecture.
- Define the test boundary. Select representative browser, SaaS, network, cloud, and developer paths, plus the agent types your organization uses. Record which traffic or identities are deliberately outside the test boundary so that a gap is not mistaken for coverage.
- Seed the estate with known cases. Include a sanctioned agent, an unsanctioned AI application, a no-code or platform-hosted agent, an internally developed agent, and a tool or MCP connection where applicable. Keep a separate list of what the test team introduced; compare it with what the product discovers.
- Test owner and identity attribution. Check whether each finding maps to a person or business owner, a non-human identity, its credentials, connected tools, and accessible resources. Include an orphaned or overprivileged test identity if your procedures allow it.
- Exercise least privilege. Give a test agent a permitted task and a separate request for an action or resource outside its allowed scope. Verify that restrictions apply to the actual access path and are not just advisory instructions to the model. Where supported, test context-specific and time-bounded permissions.
- Trigger runtime detections and controls. Run controlled scenarios for an unexpected tool call, an unauthorized action, and an attempt to access sensitive data. Observe whether the platform detects the request, response, or tool action; whether it blocks or contains it in time; and whether the result is recorded for investigation.
- Check coverage gaps deliberately. Repeat a scenario through a browser extension, an embedded SaaS AI feature, or another path that might not traverse the primary inspection point. Compare what is visible with what is not, and ask what deployment change or complementary control would close the gap.
- Evaluate evidence and workload. Export activity, ownership, policy, and lifecycle records. Have the team investigate an event, tune a policy, and handle an approved-use case that could otherwise be interrupted. Record what required manual work and what the product could not establish.
Set acceptance criteria before the demonstrations. For example, specify the agent types and paths that must appear in inventory, the identity fields that must be attributable, which out-of-policy actions must be denied, and what evidence an investigator must be able to export. These are organization-defined requirements, not universal performance thresholds. The available sources provide no independent product test results or comparable figures for discovery rates, blocking performance, false positives, price, or deployment effort.
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Compare products with a consistent scorecard
Use a shared scorecard so each vendor answers the same operational questions. Record demonstrated results separately from claims in a product description or white paper. If a capability is not demonstrated in your test, mark it as unverified rather than treating a framework mapping or roadmap statement as proof.
| Evaluation area | What to verify | Evidence to retain |
|---|---|---|
| Discovery and inventory | Sanctioned and unsanctioned apps and agents; MCP servers, tools, integrations, owners; refresh and change detection. | Known test cases found, fields populated, time to surface a new case, and identified blind spots. |
| Identity and lifecycle | Owner attribution; credentials and non-human identities; identification of orphaned or overprivileged access; revocation. | Agent-to-owner and agent-to-identity records, plus a demonstrated disable or access-withdrawal action. |
| Authorization | Access scoped by agent, user, task, resource, and context; time-bound permissions; enforcement beyond model-generated output. | Policy configuration and observed allow/deny outcomes for permitted and prohibited test actions. |
| Runtime protection | Inspection of requests, responses, and tool calls; alerting, blocking, containment, and auditability. | Event records showing the attempted action, decision, result, identity, and relevant policy. |
| Coverage and integrations | Network, endpoint, browser, SaaS, cloud, and framework paths relevant to your environment. | A path-by-path account of what was observed, what was missed, and required integrations or deployment changes. |
| Governance evidence | Exportable activity, ownership, policy, and lifecycle decisions; framework mappings and their supporting evidence. | Sample exports and a clear distinction between observed evidence and a vendor’s mapping or assertion. |
| Operational fit | Analyst investigation, policy tuning, handling of approved use cases, and response workflow. | Results from the security team’s own PoC operation; do not substitute vendor claims for customer-specific testing. |
Use governance frameworks as a checklist, not a certification shortcut
Microsoft’s governance model organizes agent governance into control-plane governance, data governance and compliance, and security. It recommends knowing which agents exist, who owns them, what they can access, and how to intervene when behavior falls outside policy. Those are useful completeness questions for any organization, but Microsoft’s documentation uses components from its own ecosystem as examples; it is not evidence that one product covers every environment.
OWASP’s Agent Control Standard, dated September 1, 2026, describes middleware hooks and portable declarative safety policies enforced at runtime. 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. These references can help organize a control review and build test cases. A mapping does not, by itself, certify compliance, demonstrate that a product implements the control, or prove the control works in your deployment.
Use OWASP’s AI Agent Security Cheat Sheet to challenge designs that grant unrestricted tool access or wildcard permissions, allow arbitrary code execution without sandboxing, store sensitive memory without protection, make high-impact decisions without human oversight, or rely only on model output for authorization. Turn each relevant warning into a scenario tied to your data, tools, and approval requirements.
How the representative approaches differ—and what they do not prove
Public vendor material offers examples of how providers frame this problem, but the descriptions are vendor-authored, not independent comparative tests. Use them to ask better demonstration questions rather than to infer a market ranking.
| Source and approach described | What to test in your environment |
|---|---|
| Cisco’s 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 identifies no-code and third-party applications as a high shadow AI risk area. | Show discovery across the organization’s actual agent paths, owner mapping, enforcement of scoped access, and intervention on the same controlled tool-call scenarios used for other vendors. |
| Palo Alto Networks’ white paper emphasizes agent identity, privileges, access, and continuous discovery that includes shadow agents. | Demonstrate how the product finds shadow agents in the relevant paths, ties them to identities and owners, and detects or corrects excessive access. |
| Microsoft’s governance documentation groups the work into control-plane governance, data governance and compliance, and security, with examples from Microsoft services. | Verify which governance functions apply to your estate and which depend on the Microsoft ecosystem or other integrations in your deployment. |
Product functions and availability can change. For a purchase decision, ask vendors to demonstrate the same discovery, authorization, enforcement, and evidence scenarios in your environment, and distinguish an observed PoC result from a product statement.
Turn the evaluation into an implementation decision
Choose the platform that closes the material gaps in your own agent estate and can be operated by the people responsible for security and governance. A feature list matters less than whether the relevant agent is discovered, its owner and identity are clear, its access is constrained, its actions are visible, and a policy violation can be contained with an auditable record.
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Use these decision gates
- Discovery gate: Reject a fit assessment based only on centrally registered agents if your risk includes employee-facing or unsanctioned use.
- Control gate: Require proof that permissions are enforced at the access or action boundary, not merely described in a prompt or policy document.
- Coverage gate: Identify unobserved browser, SaaS, endpoint, network, cloud, or agent-framework paths and decide whether another layer or deployment change is needed.
- Operations gate: Confirm that your team can investigate and intervene, and that approved use cases remain workable under the proposed policies.
- Evidence gate: Keep ownership, activity, access, policy decisions, and lifecycle records usable for security operations and audit.
No independently verified, directly comparable statistic about platform performance or shadow AI prevalence is established by the sources described here. The decision should therefore rest on your own environment-specific PoC, documented coverage boundaries, and verified controls—not an unattributed market number or a vendor’s framework mapping alone.
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