Choose an AI agent security platform by the actions it can actually inspect and control—not by a broad “runtime protection” label. Check whether it can discover agents and their reachable resources, inspect prompts and tool calls before execution, constrain permissions, require approval for consequential actions, and show evidence from tests that resemble your workflows. Vendor materials document different scopes, and the features described here have not been independently compared or tested head to head.
Why AI agents need more than harmful-output filtering
An agent may read untrusted content, retain information in memory, use an identity, call tools, and take actions in other systems. That creates risks beyond a model producing an unsafe answer: an injected instruction in a document could redirect a task, a tool could be misused, or an agent with broad permissions could expose data or make a consequential change.
OWASP’s AI Agent Security Cheat Sheet describes risks including direct and indirect prompt injection, tool abuse and privilege escalation, data exfiltration, memory poisoning, goal hijacking, excessive autonomy, high-impact action abuse, decision or approval manipulation, cascading failures, developer-console misconfiguration, denial of wallet, sensitive data exposure, and supply-chain attacks. These are useful threat categories for evaluating controls; a platform’s support for one category does not establish protection against all of them.
Which protections should a buyer compare?
Start with the control points in the agent’s path. Ask what the product can inspect, when it can intervene, and whether it can block an action or merely report it. Then map coverage to the agent’s permissions and consequences.
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| Control area | Questions to ask | What meaningful coverage looks like |
|---|---|---|
| Discovery and inventory | Which cloud, SaaS, low-code, custom, and endpoint agents are found? Are owners, identities, connectors, and reachable resources visible? | An inventory tied to devices and users, plus a view of the resources an agent’s identity can access—not just a count of deployed agents. |
| Runtime enforcement | Can the platform inspect user input, agent prompts, tool requests before execution, and tool responses? At which points can it block? | Clearly specified inspection and enforcement points, with a distinction between blocking, auditing, and alerting after an event. |
| Identity and least privilege | Does the agent act within the user’s authorization? Can the product identify excessive permissions, and can downstream systems enforce them? | Narrow permissions and authorization checks in the systems that execute the actions, rather than relying only on a gateway or model instruction. |
| Human approval and action integrity | Can policies require an independent approval for deletion, external messages, financial operations, or other high-impact actions? | A defined approval boundary for consequential operations, with auditable decisions and no assumption that an authenticated request is automatically authorized. |
| Supply chain and configuration | Can it inspect agent code, MCP servers, skills, plugins, and configuration before deployment? Does it explain how to remediate findings? | Pre-deployment checks with actionable findings, not only runtime monitoring. |
| Testing and evaluation | Are tests adversarial, task-specific, repeatable, and refreshed as attacks change? Can you test your own workflows? | Evidence at task level, including repeated attempts and realistic indirect injection or tool misuse scenarios. |
| Operations and audit | What events are recorded? Can investigators see the action path, identity, policy decision, and response? | Logs and alerts that fit existing investigation and incident-response workflows. |
| Deployment and coverage | Which frameworks, endpoints, protocols, cloud providers, and network paths are supported? What instrumentation or network placement is required? | Documented constraints and a deployment design that covers the actual agent paths without assuming every integration is equivalent. |
| Availability and economics | Which capabilities are generally available versus preview? What are licensing, regional availability, and data-handling terms? | Written confirmation of current release state and commercial terms for the intended deployment. |
How OWASP’s excessive-agency guidance translates into controls
OWASP’s LLM06:2025 guidance groups excessive agency’s root causes into excessive functionality, excessive permissions, and excessive autonomy. It recommends limiting extensions and their functions, avoiding open-ended extensions where practical, minimizing permissions, executing actions in the user’s context, requiring human approval for high-impact actions, and enforcing authorization in downstream systems.
- Limit functionality: Remove unneeded tools and narrow what each extension can do.
- Limit permissions: Give agents only the access needed for the task, and check whether that access can be scoped to the user or task.
- Limit autonomy: Require approval for high-impact actions rather than allowing an agent to execute them solely on its own judgment.
- Authorize downstream: Validate permission in the system that performs the action. OWASP’s Cheat Sheet states: “A valid message signature does not grant permission to perform the requested action.” Authentication establishes who sent a message; it does not by itself authorize the requested operation.
Monitoring and rate limits can limit impact, but OWASP notes that they do not themselves prevent excessive agency. Treat them as supporting controls, not substitutes for least privilege and action authorization.
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What the documented Microsoft and Palo Alto offerings cover
The following is a comparison of capabilities described in official vendor materials, not an independent efficacy assessment. The products cover different parts of the problem, so the table should not be read as a feature-parity scorecard.
| Area | Microsoft Defender | Palo Alto Networks Prisma AIRS |
|---|---|---|
| Discovery and visibility | Microsoft documents local AI agent discovery on onboarded endpoints, a central inventory, device and user associations, an exposure map connecting agents to identities and resources those identities can reach, and advanced hunting. | The product page describes discovery across SaaS, cloud, low-code, and custom environments. A March 23, 2026 announcement also described discovery across cloud, SaaS, and endpoint environments. |
| Runtime inspection or protection | Endpoint runtime protection is documented for prompts, pre-tool requests, and post-tool responses through agent-native event interfaces where supported. It can audit or block at supported event points. Microsoft also describes network inspection for some agents without event interfaces. | The product page describes runtime security against prompt injection and tool misuse. The March 23, 2026 announcement described the AI Agent Gateway as in limited preview at that time. |
| Supported agents or environments | Agent-native inspection documentation lists Claude Code, Codex CLI, GitHub Copilot CLI, and GitHub Copilot app. Network inspection has limitations: it does not support certificate-pinned or HTTP/3 agents. | The cited product materials describe broad environment categories, but do not establish a like-for-like supported-framework list in the information summarized here. |
| Pre-deployment and access controls | The endpoint and discovery materials summarized here do not establish coverage for scanning agent code, MCP servers, or skills before deployment. | The product page describes scanning agent artifacts including code, MCP servers, and skills; behavior testing with attack libraries or dynamic red teaming; identifying excessive access; and validating agent identities. |
| Release status in cited documentation | Microsoft marks endpoint runtime protection Preview. | AI Agent Gateway was described as limited preview in Palo Alto Networks’ March 23, 2026 announcement. Confirm current status with the vendor. |
Microsoft’s local discovery and endpoint runtime-protection descriptions are distinct capabilities; do not assume that inventory alone means an agent’s actions are being blocked. Likewise, Palo Alto’s described feature set is a vendor account of product capabilities, not independent verification of effectiveness. The materials above do not establish comparable pricing or a universal ranking.
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How to evaluate protection against prompt injection and unsafe actions
Indirect prompt injection occurs when malicious instructions embedded in ingested data influence an agent to take unintended actions. NIST’s Center for AI Standards and Innovation (CAISI) evaluated agent hijacking using AgentDojo environments and additional attacks. Its results illustrate why a single successful demo or aggregate score is not enough to judge protection.
- 11% to 81%: In one NIST CAISI 2025 evaluation on held-out Workspace tasks, the strongest new red-team attack raised measured attack success from 11% for the strongest baseline attack to 81%. This is a result for that setup, not a general platform benchmark.
- 57% to 80%: Across five injection tasks in the same NIST CAISI 2025 write-up, repeating each attack 25 times raised average attack success from 57% to 80%. Repeated attempts can therefore change measured risk.
Use those findings to shape a buyer evaluation, not to predict how a particular commercial platform will perform. Ask vendors to demonstrate controls against your own task flows and failure consequences.
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- Build realistic scenarios. Include untrusted documents or web content, indirect instructions, tool misuse, and attempts to exfiltrate data. Include the actual identities and connectors used by the agent.
- Define task-level outcomes. Record whether the agent completed the intended task, whether an unsafe action was attempted, whether the platform blocked it before execution, and whether sensitive data left the intended boundary.
- Repeat attacks. Test multiple attempts and variations rather than relying on a single run; preserve the configuration and policy state so results can be compared.
- Test each enforcement point. Verify what happens at input inspection, pre-execution tool checks, tool-response inspection, downstream authorization, and approval gates. A post-event alert is not equivalent to pre-execution blocking.
- Retest as attacks change. NIST’s AI Agent Standards Initiative page, created February 17, 2026 and updated August 14, 2026, says NIST is researching agent authentication and identity infrastructure and developing security evaluations for protocol development and consumer comparison. This work is evolving; ask vendors how they refresh their tests and mappings.
What to confirm before selecting or deploying a platform
Turn the comparison into a deployment-specific acceptance checklist. Vendor labels and broad environment claims are not substitutes for confirming that your agents, identities, and action paths are covered.
- Inventory agents across the environments you use, including locally run agents and custom or low-code deployments.
- Map every agent to its owner, identity, tools, connectors, and the resources that identity can reach.
- Document whether each control inspects, blocks, audits, or alerts, and at what stage in the action flow.
- Verify protocol, framework, endpoint, and network limitations. For Microsoft’s documented network inspection, specifically ask how certificate pinning and HTTP/3 affect the agents you run.
- Separate generally available features from preview or limited-preview features, and verify current status before basing a production control on them.
- Ask for licensing, regional availability, data handling, retention, and deployment requirements in writing; comparable current terms are not established here.
- Run repeatable adversarial tests against your own tasks and retain task-level results for acceptance and later regression checks.
How to interpret market maps and vendor claims
OWASP’s Q3 2025 AI Security Solutions Landscape maps open-source and commercial solutions across the agentic lifecycle, is peer-reviewed, and is updated quarterly. It is a market landscape, not a test result or endorsement. Use it to identify categories or products to investigate, then validate actual controls, supported environments, release status, and operational fit directly with vendors.
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