Best AI agent security tools in 2026: 5 platforms compared
Nolan Sullivan
Published · 13 min read
AI agent security tools differ in where they see an agent's activity and what they can do when something goes wrong. A security team needs to know which agents are running, what each one can reach, and whether a control can stop a risky action before it happens.
This guide compares Speakeasy, Noma Security, Zenity, Cisco AI Defense, and SentinelOne Agentic AI Security (Prompt). It looks at identity and access, runtime protection, and the questions to test in a trial, including access to Model Context Protocol (MCP) servers and tools.
Speakeasy approaches agent security from the access side. Its AI Control Plane holds each agent's identity, the MCP gateway its tool calls pass through, and the guardrails that act on those calls in one product. An agent can never do more than its human owner can do right now, and an administrator can narrow it further to chosen servers and tools.
NOTE
We build Speakeasy, which appears in this comparison. The descriptions of other products are based on their vendors' published material, not independent tests.
Two ways agent security tools reach agent activity
Every platform in this guide can act on agent activity at runtime. They differ in how they get into the agent's path.
- Runtime protection added to existing agent platforms. Zenity and Cisco AI Defense attach to agents where they already run, through platform hooks, integrations, or an SDK in the agent's code. Coverage follows the platforms and frameworks each vendor supports.
- A governed path to tools. Speakeasy puts an MCP gateway between agents and the servers they use, and ties every call to an agent identity and its owner. Noma and SentinelOne also document gateway-based access control alongside their other integrations.
The distinction matters when an agent runs somewhere a vendor has no hook. A gateway-enforced policy still applies to any MCP client that reaches the server through it, while a hook-based policy depends on the client supporting hooks.
How do AI agent security tools compare?
The first table highlights a documented use for each platform. The product sections explain the controls behind each fit.
AI agent security tools at a glance
The second table compares capabilities. A ✅ means the capability is documented in the product, ⚠️ means it depends on another product or integration (named in the cell), and — means we did not find it in the vendor's published material.
Capability comparison
What should buyers test in AI agent security tools?
Bring a representative agent workflow to each evaluation. Include an agent, its owner, the tools it can reach, and a session that uses those tools. Ask the vendor to show the controls on that workflow and identify any paths that need additional integration.
- Environment and coverage. Identify the agent platforms, applications, and MCP servers in scope. Ask where the product observes activity and which integrations put it in the execution path. An inventory drawn from observed traffic is useful, but its coverage depends on the traffic the product can see.
- Identity and access. Check how an agent is associated with a responsible person and how its permissions are set. Test a request to a permitted tool and a request to a tool the agent should not reach. The agent access management guide covers the identity decisions behind these tests.
- Detection and response. Present a scenario the policy should identify and ask what happens at the point of detection. A flag gives a reviewer a signal; a deny or session freeze can interrupt activity where the product can intercept it. Check whether each detector supports the response you need.
- Session context. Ask what context an evaluator can use and what the security team sees when reviewing a result. Use a multi-step workflow to see whether the finding is understandable alongside the agent's activity.
- Policy operations. Ask how a team tests a proposed policy, changes it, and handles a legitimate action that triggers it. Agree on who can release or investigate a stopped session before enforcement is enabled.
The agent guardrails guide explains guardrail layers and terminology. For the broader risks and controls, see AI security.
Speakeasy: Owner-bounded access and guardrails at the MCP gateway
Speakeasy is a fit for teams that want agent access tied to a human owner and want to manage which MCP servers and tools agents can use. Its agent identity documentation says each agent has one human owner, a policy limiting its actions, and credentials that can be revoked without affecting anyone else's access. The owner's current access sets the ceiling for the agent, while an administrator can narrow its policy to selected servers and tools.
That ceiling is checked live. The agent's policy and its owner's permissions are read on every request, so removing a permission from the owner takes effect for their agents immediately. When the owner leaves the organization, the agent is blocked until someone reassigns it.
An identity provider confirms who a person is, and the admin controls built into each AI client apply to that client only. Neither decides which MCP tools a given agent can call across every client. Speakeasy fills that gap at the gateway. The gateway hosts the servers agents use, whether built from an API, connected as a remote server, or tunneled from a private network, and its Team Access controls govern who can connect, see a server in the catalog, and manage it, down to individual tools.
Speakeasy's guardrails scan agent session interactions for secrets, sensitive information, and prompt-defined risks. Depending on the policy and interception path, a matching request can be flagged or denied, or the session can be quarantined until an administrator releases it. MCP-scoped policies run at the gateway itself and check each tool request before it reaches the server and each result before it returns, so they cover any MCP client without agent hooks. Three detectors only flag, because they see activity after it happens: destructive tools, destructive CLI commands, and non-corporate accounts.
Before enforcing a prompt-based policy, teams can replay recent agent sessions and review the verdicts it would produce. Policy owners can use those results to tune the policy against real traffic. For incidents, a killswitch stops one person's MCP tool calls on chosen servers without ending their sessions, and suspending an agent stops every request made with its credentials.
For discovery, Speakeasy's Shadow MCP inventory includes unmanaged servers observed in agent traffic and servers raised in access requests. The shadow MCP guide explains how to review that inventory.
Where it fits: Platform and security teams that want agent identity, MCP server hosting and access, and guardrails in one product, with enforcement on the path every tool call takes.
Where it stops: Session guardrails for prompts and assistant output need Speakeasy hooks or chat capture on the agent, and MCP-scoped policies are enabled per organization on request.
Noma Security: Context-aware runtime protection
Noma Security evaluates agent actions using session context, identity, data access, and intent.
Its documented response options include blocking an action, masking sensitive data, steering an agent toward another path, involving a human, and triggering a killswitch.
Noma also offers agent access control that can approve or block individual tools, with policies scoped by tool, agent type, user, team, or environment. It applies policies through supported gateways, hooks, and APIs.
Where it fits: Teams that want runtime decisions based on an agent's intent and context, with a wide range of responses beyond allow or block.
Where it stops: Enforcement depends on a supported gateway, hook, or API integration for each agent platform, so confirm coverage for each environment in scope.
Zenity: Inline boundaries across agent platforms
Zenity offers runtime security controls for agents on several commercial agent platforms, including an integration in the Microsoft Foundry agent execution path.
Zenity says the integration can help teams detect, disrupt, and prevent threats during execution. Its Boundaries policies return an allow, redact, or block decision and enforce inline through hooks on platforms such as Claude Code and Cowork, as well as Microsoft Copilot Studio, Azure Foundry, and others.
Where it fits: Teams running agents across several commercial agent platforms that want one set of runtime policies enforced inside those platforms.
Where it stops: Zenity says its Boundaries policies currently detect rather than block activity in ChatGPT and Gemini, so enforcement depends on each platform's hook support.
Cisco AI Defense: Inspection through a Python SDK integration
Cisco AI Defense offers agent runtime protection through a Python SDK integration. The integration sends requests for inspection before LLM or MCP calls and inspects responses after they return.
For MCP interactions, Cisco says the integration inspects tool arguments and results, prompt templates, and data read from resources. Cisco also offers agent identity through Duo and MCP access enforcement through Secure Access.
Where it fits: Engineering teams building their own Python agents, especially in organizations already using Cisco security products.
Where it stops: Runtime protection requires adding the SDK to each agent's code, and agent identity and MCP access enforcement come from separate Cisco products.
SentinelOne: Agent interaction and MCP server protection
SentinelOne Agentic AI Security (Prompt) includes prompt injection blocking for agent interactions and tool calls, as well as malicious MCP server prevention.
SentinelOne also describes discovering MCP activity, including shadow deployments and local processes, and assessing server risk before agents act.
Where it fits: Organizations on the SentinelOne Singularity Platform that want agent and MCP security in the same console as endpoint and identity protection.
Where it stops: SentinelOne combines an MCP gateway, a browser extension, security plugins, and API integrations, so test which agent and server paths each component covers in your environment.
Which AI agent security tool should you choose?
Start with the workflow where a security decision would change an outcome. Map the agent's owner, its tool access, and the point at which a policy needs to act. Then run the same scenario with each finalist and record what was observed, what was prevented, and what needed a human decision.
- Owner-bounded access and guardrails in one product. Speakeasy connects an agent identity to a human owner, hosts and scopes the MCP servers and tools it can reach, and enforces policies at the gateway and in instrumented sessions.
- Context-aware runtime protection. Noma Security evaluates agent actions using session and identity context, with tool-level access policies and several response options.
- Inline boundaries on agent platforms. Zenity enforces runtime policies through hooks on platforms such as Claude Code, Copilot Studio, and Azure Foundry.
- Python SDK integration. Cisco AI Defense inspects model and MCP interactions through its agent runtime protection SDK.
- MCP server and interaction protection. SentinelOne offers prompt injection blocking and malicious MCP server prevention.
A demonstration should include a permitted action, an action that should be denied, and a case that calls for review. Ask each vendor to explain where the control applies and what happens when the same workflow takes another path.
To run that test on Speakeasy, sign up for the AI Control Plane, create an agent identity, and connect it to an MCP server through the gateway. To test owner-bounded access and guardrails against your own workflows with our team, book time with us.