Most security stacks were built to watch people. But the fastest-growing actor in your enterprise now is an AI agent that no security tool was built to watch. 

Agents log in with credentials no human owns, reach into tools like Jira, GitHub and Salesforce, and act faster than any human reviewer can follow. 

The market has responded with a sprawling set of security tools for AI agents, each covering different areas, from catching risky tool calls to controlling what each agent is allowed to do.

Heading into 2026, 48% of cybersecurity professionals expected agentic AI to become the number-one attack vector (Dark Reading, 2026). Yet most enterprises still lack AI-specific security controls.

Below are 8 AI agent security tools spread across those five core categories. Each is strongest at one job, whether that's spotting shadow AI, screening prompts for injection, or scoping what an agent can do inside a tool. 

TL;DR

• AI agent security splits into five categories, and no single tool covers them all.

• Match the tool to the gap you actually have.
• Identity and app-aware permissions are the layer most stacks still miss.

What does "AI agent security" mean in 2026?

AI agent security means controlling what an autonomous, tool-using agent is allowed to do.

Doing it well takes real engineering work. Policy documents alone won’t satisfy regulators or security needs. 

A human has to be able to see what the agent is doing and step in to stop it.

It is broader than older "LLM security," which mostly inspected prompts and responses within AI chatbots. 

An AI agent that can call tools, build its own memory, chain steps together, and even collaborate with other agents fails in ways a chatbot never could. 

The top new risks, as identified in the OWASP Top 10 for Agentic Applications 2026 (released December 2025), are: 

• Tool misuse
• Rogue agents
• Agent goal hijacking
• Cascading failures across multi-agent systems

The OWASP agentic list addresses what the older LLM Top 10 never covered: what an agent does with its access (deleting a production database, approving a payment, or changing another user's permissions).

The potential attack surface for agentic applications is both broad and deep. Non-human identities outnumber human identities 45:1 on average, and up to 144:1 in cloud-native environments (Cloud Security Alliance, May 2026). That’s the breadth. The depth comes from how far agentic AI can reach into your systems.

Each of those machine identities is a live login that can reach tools and data, and most carry more access than the job needs.

In IBM's 2025 report, 13% of organizations had a breach of an AI model or app. An additional 8% were uncertain whether they had been compromised, which is what happens when the identities involved were never visible in the first place. 

Strikingly, of organizations who were compromised, 97% of them did not have AI access controls in place (IBM, “Cost of a Data Breach Report 2025”).

The most common issue we see, and the one we built Willow to cover, is that AI agent identities were never set up through formal access management, permissions are not tied to real human identities, and there is no clear process to review or revoke permissions when a project ends or a team member changes roles/leaves the company. 

The guiding principle for AI agent security is that you cannot secure an agent you cannot see or control. This leaves you two main problems to solve: visibility and control. 

What are the categories of AI agent security tools?

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The market can be broadly divided into five core categories. A tool that is excellent in one category of work may do little for another. So it’s important to build a stack that covers everything your organization’s AI agent workloads truly need. 

1) MCP gateway and tool-call governance

Such tools sit between every agent and tool. They inspect and enforce each call. 

MCP (Model Context Protocol) is the connection standard agents use to reach tools. The gateway sits on that path and checks every call.

2) Runtime detection and response

Watches the agent's live execution (tool calls, memory reads, retrievals) and flags or blocks abnormal behavior. 

It is the agentic equivalent of endpoint detection.

3) AI security state management and discovery

Finds every agent and AI tool already running, maps how they connect, and scores the risk. This is how you surface shadow AI before it surprises you.

4) Inference firewall and red teaming

Inspects prompts and responses for injection and data leakage, and attacks your own agents on purpose to find holes first.

5) Identity and access governance

Gives each agent a real, human-linked identity and app-aware permissions (granting an agent a specific set of actions inside a tool (read, write, or delete) scoped to what it needs).

This is the layer most stacks still skip.

Every category matters, though few teams need all five on day one. Start with the category that matches your biggest exposure and read those tools first.

The 8 AI agent security tools enterprise teams should evaluate in 2026

Several were acquired by larger security vendors in 2025, which changes how you buy them.

Tools were selected based on (1) category leadership in at least one of the five AI agent security disciplines, (2) documented enterprise production deployments, and (3) publicly verifiable security controls. Emerging research projects without enterprise deployability were left out. 

1) Prompt Security: broadest GenAI surface coverage

Prompt Security covers a wide GenAI surface, with broad visibility into how AI enters a company.

You’ll find employee GenAI usage, homegrown apps, code assistants, and agentic AI in one platform. 

It discovers shadow AI through a browser extension and network-level visibility, and inspects every prompt and response for injection and data leakage (Prompt Security).

Gartner named it a Cool Vendor in AI Security. 

SentinelOne acquired Prompt Security in September 2025 for about $180 million, so it now ships inside a public-company security portfolio. 

For agent control, it runs an MCP gateway that enforces allow and block policies on each tool call, stopping a disallowed or shadow-MCP call in real time before it reaches the server. 

What it does not do, though, is bind each action to a directory-provisioned employee identity, or enforce read-versus-write permissions inside the connected tool.

2) Lasso Security: behavioral intent detection

Lasso Security leads on behavioral intent. It reads what an agent is trying to do across its full execution trace. Something prompt scanners never see. 

Its Intent Security engine claims sub-50ms behavioral analysis at a vendor-stated 99.83% detection accuracy, and Lasso ships the first open-source security gateway for MCP (Python, MIT-licensed), so teams can audit the code and self-host it (Lasso Security). 

Lasso is SOC 2 Type II certified and was listed by Gartner as a Cool Vendor for AI Security in 2024. 

Beyond detection, Lasso enforces inline, stopping or quarantining a risky or hijacked action at the proxy layer before it executes (rather than merely alerting after the damage is done). It also covers discovery, risk assessment, and red teaming. 

As of mid-2026, though, it does not enforce granular read-versus-write permissions inside a tool, and agent identity is not tied to a human directory.

3) Lakera: real-time inference firewall

Lakera is built for the inference path. Its Guard API is a real-time firewall that catches prompt injection, jailbreaks, and data leakage before they reach the model. 

It pairs that with Gandalf, a public AI red-team community with 1M+ users and 80M+ adversarial prompts. 

The Lakera team is known for its sharp research, too, including a zero-click remote-code-execution exploit through MCP and agentic IDEs (Lakera). 

Its API-first, low-latency design suits teams hardening live request paths, because a sub-50ms REST call adds negligible latency to production traffic and drops in front of any LLM without forcing you to re-architect the app. 

Lakera holds both SOC 2 Type II and HIPAA attestations. 

Check Point announced its acquisition of Lakera in September 2025, and the platform now anchors Check Point's Global Center of Excellence for AI Security. 

The platform's gaps are agent identity and MCP action-governance on live production traffic. 

As of mid-2026 it provides no agent-identity model. Additionally, while it screens MCP interactions for injection risk, it does not run an action-governance gateway on live MCP traffic. This means it flags a poisoned tool description but cannot stop an agent from invoking a tool it should not. An important distinction. Teams still need a separate policy gateway to allow or block each call.

4) Protect AI: deepest model supply-chain security

Protect AI’s Guardian scanner reads 35+ model formats for backdoors and deserialization attacks.

Its Layer product adds runtime tracking of conversation flow and tool calls, letting teams catch multi-turn prompt injection, jailbreaks, and data leakage as they unfold and block unsafe actions at runtime. Alerts can be routed to Splunk or Datadog.

One unusual aspect of Protect AI's capabilities, though, is its Recon product. Recon runs automated red teaming from a 450+ attack library. 

Additionally, its huntr platform is the first AI/ML bug bounty, with 17,000+ researchers and 2,520+ CVE submissions (Protect AI). 

For a poisoned model or a compromised training pipeline, Protect AI is among the strongest options here. 

Palo Alto Networks completed its acquisition of Protect AI in July 2025, and it now lives inside Prisma AIRS. 

But while Layer blocks malicious actions inline, as of mid-2026 it does not scope permissions to a specific action inside a connected SaaS tool. It can stop an injected or clearly malicious call but does not prevent a normal-looking agent that reads, writes, or deletes beyond what its task actually requires. 

It adds no agent-identity or self-serve provisioning layer, either, so buyers lack (1) an accountable human behind an agent, and (2) the ability to grant agents scoped access.

However, Palo Alto's broader Prisma AIRS platform, where Protect AI now sits, has since added agent-security capabilities of its own.

5) Zenity: agent-native discovery and monitoring

Zenity is purpose-built for agentic AI security, and Forrester included it in its AI Governance Solutions Landscape for Q2 2025. 

Observe discovers agents across SaaS (Salesforce Agentforce, Microsoft Copilot Studio), homegrown stacks (Bedrock, LangGraph, Vertex AI), and endpoints (Cursor, Claude Desktop). 

Govern applies secure-by-design policy to agent configurations, permissions, and memory before deployment.

Defend then analyzes an agent's full execution path at runtime (tool calls, memory access, and data flows) to catch prompt injection and intent hijacking. Blocking unsafe actions inline rather than only alerting on them (Zenity).

Zenity has SOC 2 Type II, ISO 27001, and ISO 27701 attestations. 

As of mid-2026, Zenity's own materials describe its attribution as agent- and application-level. 

Zenity blocks unauthorized tool calls and API invocations at runtime, but does not bind each action to a directory-provisioned employee's in-app permissions the way an identity and access platform does. 

6) CalypsoAI (F5 AI Guardrails): government and regulated inference defense

CalypsoAI brings deep national-security pedigree. It has worked with U.S. federal agencies including the Department of Defense and the Department of Homeland Security, and reaches FedRAMP and IL5 environments through Palantir's FedStart, though it is not itself FedRAMP-authorized. It holds SOC 2 Type I and Type II attestations. 

It defends the inference layer in real time against injection, data exposure, and policy violations. It also runs agentic red teaming and centralizes audit logging for compliance (F5 AI Guardrails). 

F5 acquired CalypsoAI in September 2025, and it now ships as F5 AI Guardrails. 

As an inference firewall, as of mid-2026 it has no agent-identity model and applies role-based (not app-aware) permissions. 

It surfaces AI usage inline through the network rather than through endpoint agents. That catches every prompt and response routed through the inference gateway, but it is blind to AI a user reaches outside of that path, like a public model called straight from a personal or off-network device.

7) Operant AI: runtime, endpoint, and gateway defense in one

Operant AI is the rare tool combining a dedicated MCP gateway, agent runtime protection, and endpoint discovery in one platform.

Gartner names Operant a Featured Vendor across five AI-security research notes, none of them a Magic Quadrant. 

• Endpoint Protector finds shadow AI and MCP servers on employee machines. 
• Agent Protector adds action-level tracing, inline blocking, and automatic redaction (PII, PCI, and PHI) across roughly 100 data types for cloud agents.

Operant provisions its own platform users through Okta and Entra ID (SSO, SCIM), but as of mid-2026 its agent permissions are governed by Operant's own controls rather than inherited from your identity provider the way an access platform does. 

It scopes which tools and intents an agent can use, although it cannot be verified through its public materials (as of mid-2026) whether it allows for per-operation read-versus-write limits inside a single tool. 

8) Willow: agent identity and app-aware permissions governance

Willow is the Agentic Access Platform for AI agents. It enables scaling AI agents across your organization, with agent identities tied to real employees and app-aware permissions enforced at the moment of action. 

It powers companies like Wix, Innovid, and Riskified. At Wix, nearly 5,000 weekly users are enabled with secure, governed AI agent use across ~600 connected tools and MCPs, amounting to 300,000+ tool calls every week (Wix case study).

Willow’s agent identity implementation means that every AI agent inherits its identity from a real person. All through your existing identity provider. Roles and permissions (down to granular in-app action-level permissions) flow through to every action the agent takes (Willow Identity & Access). 

This works through the Okta, Entra ID, and JumpCloud identity providers your org already runs. 

While other tools only control which apps an AI agent can access, Willow adds an additional layer, enabling control of what an agent can do once inside apps like Jira (read tickets, create them, reassign, or delete) and in which projects (Willow Identity & Access). 

Willow also ships with: 

• Native shadow AI discovery
• Integrations with Splunk, Loki, and Grafana
• Immutable audit trails (a tamper-proof log of every agent action) 
• One-click revocation across every agent touching a system 

(Willow Governance & Compliance). 

It is SOC 2 Type II certified and can be deployed as SaaS, self-hosted, or on-prem with full isolation for regulated industries. 

It’s the best fit for teams that need least-privilege enforcement (each agent gets only the access it needs) on what agents can do. 

When something goes wrong and security asks, "Could that agent have touched our customer data?" you pull the Audit Trail. 

Willow does not cover model supply-chain scanning or pre-release red teaming. Pair it with a model-security platform like Palo Alto's Prisma AIRS, which now includes Protect AI, to fill that gap.

How these AI agent security tools compare by category

Compare them one category at a time. 

The table below places each tool in the category it leads and names what it primarily secures. 

Use it to spot which categories your current stack already covers and which it leaves open.

Most tools cluster around detection, inference defense, and discovery. The layers that watch and react. 

Identity and action-level permission enforcement is the thinnest column, which is why a full stack usually pairs a detection tool with an identity and access layer like Willow.

How should an enterprise team choose an AI agent security tool?

Choose by your loudest, highest-risk gap. 

Run your stack against the five categories and buy for the empty column. 

(1) If you don't know what agents are running

If you want to surface an agent your team spun up six months ago, still running on a developer's personal API key, start with discovery and monitoring (Zenity, Operant AI, or shadow AI discovery with Willow). 

(2) If agents act dangerously inside tools they can reach

If the idea of "delete config" and "drop the database" sitting behind the same open door worries you, then you need identity and app-aware permissions. For that, choose Willow. 

The damage almost always comes from an authorized agent doing an unauthorized thing inside a tool it was allowed to reach.

(3) If your exposure is the live request path

This is where zero-click attacks like EchoLeak land. EchoLeak (CVE-2025-32711) exploited Microsoft 365 Copilot to exfiltrate data through a crafted email, with no user interaction.

An inference firewall or runtime detection tool (Lakera, Lasso, CalypsoAI) hardens prompts, responses, and execution traces in real time. 

(4) If you ship homegrown models or AI apps

Catch poisoned models and injection flaws before release, before a bug bounty researcher or an attacker does it for you.

For model supply-chain scanning and red teaming before release, choose Protect AI, now delivered through Palo Alto's Prisma AIRS. 

Several leaders here were acquired in 2025, so "buying the tool" increasingly means buying into a larger security platform. 

Weigh that cost against a best-of-breed specialist for the one category you most need. 

Most teams end up with two: a detection or discovery tool, plus an identity and access layer like Willow that controls what agents can do.