Best AI Agent Security Platforms in 2026 (8 Compared)

Every vendor in this category now says it secures AI agents. Read the product pages closely and most of them answer one question about an agent, rarely the one a CISO asks first. A SaaS posture tool lists OAuth grants. A guardrail blocks a prompt. An endpoint product sees a process. None of them can tell you that the Claude Code session on Alice's laptop delegates through her Google identity, holds drive.readonly, can reach 193 sensitive files, and read payroll.xlsx at 10:04 this morning.

The best AI agent security platforms answer four questions for every agent, bot and AI-enabled automation in the business: what is acting and which identity it uses, what that identity can do, what it actually did, and what sensitive data it can reach and touched. We laid out that four-pillar model in our guide to AI agent security. This page applies it to the market.

We compare eight platforms. Each gets the same treatment: what it is by public category, how it covers seven evaluation questions, who it is best for, and where it stops. We built Strac, so we put it first and say exactly why. For the other seven we describe the category each competes in and leave specific feature claims to the vendor.

✨ How we evaluated: seven questions to ask any AI agent security platform

Agent risk is an identity problem, a data problem and a runtime problem at once, joined. So the evaluation has to test the joins. We reduced the four pillars to seven yes-or-no questions a buyer can put to any vendor on a demo call.

1. Does it discover agents across SaaS and cloud? Agents live in Google Workspace OAuth grants, Slack app installs, GitHub Apps and Copilot sessions, Salesforce connected apps and Agentforce bots, AWS Bedrock AgentCore runtimes, Claude and ChatGPT workspaces, and Cursor. SaaS-only or cloud-only coverage leaves half the inventory dark.
2. Does it resolve identity for all six patterns? Delegated OAuth, app or bot identity, cloud workload identity, static API keys and PATs, a human's already-open browser session, and shared or human logins. Patterns A through D leave a footprint in provider admin APIs. Patterns E and F do not, and they are growing fastest because browser and computer-use agents are the easiest to deploy.
3. Does it map effective permissions? Not the scope on the grant, but what the scope reaches once intersected with group membership, folder inheritance, repository selection and IAM policy. drive.readonly means nothing until you know which files Alice can read.
4. Does it attribute real activity? Authorization says what could happen. Audit logs, compliance APIs and runtime telemetry say what did. Store them separately, join them only through evidence, and never infer "did" from "can."
5. Does it join to sensitive data? Every permission should answer what does this expose, and every data finding should answer who or what can touch it. That requires a classified inventory of the resources behind the permissions, which most agent security tools do not have.
6. Does it see browser and local agents? The agent driving Alice's Chrome tab, the local Claude Code or Cursor process, the MCP server configured in a dotfile on a workstation. API-only products cannot attribute any of these.
7. Does it remediate in a bounded, reversible way? The platform should state exactly what will change, whether it can be undone, and whether it applies the change itself or hands you a console link.

Here is how the categories land. SaaS security posture management (SSPM), non-human identity (NHI) and CIEM tools cover questions 1 through 3 for API-visible patterns and stop at "can." AI guardrails, LLM firewalls and agent observability answer part of question 4 for traffic or agents they instrument. DLP and DSPM alone answer question 5 and have no concept of an agent.

✨ 1. Strac: the AI agent security platform that answers all four questions in one graph

What it is. Strac is an AI agent security platform built on a data security platform. It discovers every agent and AI-enabled automation across Google Workspace, AWS and Bedrock AgentCore, GitHub and Copilot, Slack, Salesforce and Agentforce, Anthropic Claude, OpenAI ChatGPT and Cursor, resolves the identity each one runs as, maps what that identity can do, observes what the agent did, and joins all of it to Strac's classified inventory of sensitive data. The result is one graph: Agent → Principal → Permissions → Resources → Sensitive data, with every observed action joined to the same chain.

Discovery across SaaS and cloud (question 1). Strac pulls agents from provider control planes and audit logs before it proxies anything: Google OAuth token inventory and audit, AgentCore runtime and workload identity listings, GitHub App installations and the enterprise audit log with its agent-session fields, Slack admin app inventory, Salesforce connected apps with Event Monitoring, the Anthropic Compliance API (which covers Cowork and local Claude Code sessions), the OpenAI Compliance Logs Platform, and Cursor's admin APIs. Every integration ships a capability manifest stating what Strac can discover, resolve, attribute and remediate for that provider, so coverage is a measured number. "Is an agent" and "is a non-human identity" are separate flags: an NHI inventory of 4,112 principals might contain 214 used by AI agents, and conflating the two buries the real risk.

Identity for all six patterns (question 2). Every agent record carries the pattern it uses and an attribution confidence: provider-native, Strac-correlated or inferred. Patterns A through D come from provider APIs. Browser-session and shared-login agents, patterns E and F, come from Strac's own sensors. The UI never presents weak attribution as fact, and the finding "shared human credential used by an agent" fires whenever attribution depends on a human login instead of a dedicated identity. For the identity side of the program, start with AI agent identity governance.

Effective permissions (question 3). Strac resolves access in layers: direct grants, then delegated user access, then group and folder inheritance, then targeted resource policies for AWS. For cloud roles it simulates a curated set of high-risk actions (S3 get and put, Secrets Manager read, KMS decrypt, Lambda invoke, PassRole) against Strac-known resources instead of proving the whole IAM universe. Granted and used are shown side by side over a policy window. For Claude Code on Alice's machine: 9 permissions granted, 5 exercised in 30 days, 3 unused with sensitive reach, 3 reversible reductions.

Real activity (question 4). Activity comes from provider audit logs, the Anthropic and OpenAI compliance APIs, CloudTrail, and Strac's MCP, browser and endpoint sensors. Each line in the timeline is tagged with its evidence source, and every normalized record points back to an immutable raw evidence record. A shared IAM role used by two AgentCore runtimes stays principal-attributed until session evidence disambiguates it. See monitor AI agents for the full activity model.

Sensitive data (question 5). This is the pillar the rest of the category lacks, and the reason Strac leads the list. The platform already classifies PII, PHI, PCI, secrets, source code and financial data across Drive, Slack, GitHub, Salesforce, S3 and the other systems agents reach. Agent security is a new set of principals pointed at that inventory. So the finding is not "agent has drive.readonly." It is "agent can reach 193 sensitive Drive files: 42 financial, 8 with SSNs, 4 with secrets, and it read payroll.xlsx this morning." The same data layer powers AI DLP in the browser and MCP DLP on tool calls, so redaction at the boundary and agent governance share one classification engine.

Browser and local agents (question 6). This is Strac's structural advantage over API-only platforms. Its browser, endpoint and MCP sensors are already deployed at customers for data loss prevention, and they feed the same graph as optional evidence adapters. The endpoint sees local Claude Code, Codex and Cursor processes, MCP configuration files, shell and tool execution, browser control, and credentials in the execution environment. The browser sensor sees a computer-use agent take control of Alice's open Salesforce tab and export a report, an event Salesforce's audit log attributes to Alice. The MCP sensor records server, tool, session, downstream principal, resource, and sensitive data in every tool call it observes. In the reference inventory, 17 of 41 shadow agents were found only by endpoint or browser evidence, and 26 local MCP servers sat on 19 workstations no provider API could see. See shadow AI detection tools and discover AI agents.

Findings with proof. Severity is deterministic and visible: sensitivity × action capability × breadth × sanctioning × observed use × attribution confidence. There is no opaque ML risk score to defend in an audit. The day-one findings are the ones a CISO already asks about: unsanctioned agent can access sensitive data, agent observed accessing sensitive data, sensitive data sent externally, agent can read secrets and execute, overprivileged agent, ownerless live agent, stale credential, shared human credential used by an agent, and wildcard access.

Bounded, reversible remediation (question 7). Discovery credentials and remediation credentials are separate. You deploy read-only on day one; write access is opt-in per provider and per action. Where a provider exposes a bounded admin write API, Strac applies the change: revoking a user's third-party app tokens by client_id in Google Workspace, restricting or uninstalling an app in Slack. Where it does not, Strac generates the exact change and links to the console: a policy delta for an AWS role, a repository-selection recommendation for a GitHub App. Every option states its scope, blast radius, reversibility, and what Strac will verify after resync. The policy side is covered in AI agent governance and protect AI agents.

Best for. Security teams that need the full answer rather than a posture report: identity, authorization, activity and data for every agent, with evidence attached. Especially strong where Google Workspace, AWS and GitHub are the center of gravity, and for any team already running browser or endpoint DLP.

Where it stops. Strac prioritizes depth on its seven P0 providers over catalog breadth. Microsoft Entra, Copilot Studio and Azure agents, and GCP Vertex agents, are deferred until a customer pulls them forward. AWS remediation in V1 is a generated policy delta and console link rather than an automated IAM mutation, by design. And attribution for browser-session and shared-login agents is labeled correlated, with confidence factors shown, rather than claimed as provider-native. Strac treats that honesty as a feature.

2. Microsoft (Purview, Entra and Security Copilot)

What it is. Microsoft governs AI agents natively across the Microsoft 365 estate through Purview for data, Entra for identity, and Security Copilot for investigation.

Against the seven questions. Inside Microsoft 365 and Azure, coverage is broad: Entra knows the app registrations and consents, Purview classifies the data, and the audit log records the activity. Outside that estate, questions 1, 3 and 4 depend on what other providers expose to it, and browser-session and local agents (question 6) are out of scope. The strongest controls generally sit in the higher license tiers.

Best for. Organizations standardized on Microsoft 365 that want to govern Copilot and Graph-connected agents with tools they already own.

Where it stops. Most companies run agents across Google Workspace, Slack, GitHub, Salesforce, AWS and non-Microsoft AI vendors too. Many teams run Microsoft for native governance inside its estate and a cross-provider agent security platform for everything else.

3. Zenity

What it is. Zenity is publicly positioned as a security and governance platform for enterprise AI agents and copilots, with a particular focus on agents built on low-code platforms such as Microsoft Copilot Studio.

Against the seven questions. The agent posture category is built around questions 1 and 3 for the agent-building platforms it connects to: inventorying the agents teams have created and assessing their configuration and connections. Coverage of the six identity patterns, the sensitive-data join, and browser or local agents depends on the platforms in scope; the category is platform-API driven rather than sensor driven.

Best for. Organizations whose primary agent risk is internally built copilots and workflows on a low-code platform, and who want AppSec-style posture for those.

Where it stops. Posture for the agents you built is a different problem from discovering the agents you did not know you had: the Claude Code session on a laptop, the Zapier automation on a shared login.

4. Prompt Security

What it is. Prompt Security is publicly positioned as a GenAI security platform at the prompt and LLM layer: a gateway that inspects prompts and responses, with prompt-injection defense and visibility into employee GenAI usage.

Against the seven questions. The AI guardrails and LLM firewall category answers part of question 4 for traffic that passes through its enforcement point, and part of question 5 by detecting sensitive content in that traffic. It does not inventory agents across SaaS and cloud control planes, resolve identity patterns, or map effective permissions. Remediation is blocking or modifying a prompt, a different operation from narrowing a grant.

Best for. Teams that want a control in front of LLM usage for prompt-injection and content inspection, as one layer of defense in depth.

Where it stops. A guardrail cannot tell you that a stale automation still holds a live token, that a Copilot coding agent has contents:write on a repository with 17 active secrets, or that an Agentforce bot has no owner, because none of those facts pass through a prompt.

5. Cyberhaven

What it is. Cyberhaven is publicly positioned as a data detection and response platform built around data lineage: tracing where data originated, where it moved and who moved it, with an emphasis on insider-risk analytics.

Against the seven questions. The DDR category answers question 5 through lineage, and parts of questions 4 and 6 because it observes data movement on endpoints. It is not an agent inventory, does not resolve the six identity patterns, and does not map effective permissions on SaaS or cloud principals.

Best for. Security teams whose priority is understanding data flows and investigating insider risk, with endpoint-level visibility into where a file went.

Where it stops. Lineage reconstructs the path a piece of data took. Agent security needs to know, before any data moves, which agent can reach it and what identity it would use, and then narrow that path.

6. Palo Alto Networks (Prisma AIRS)

What it is. Prisma AIRS is publicly positioned as Palo Alto Networks' AI runtime security offering, part of its broader enterprise security platform, addressing the security of AI applications, models and agents at runtime.

Against the seven questions. As an AI runtime security product inside a large suite, the category's center of gravity is question 4 for AI application traffic. How far it reaches into SaaS OAuth inventory, the six identity patterns, effective permissions, and the sensitive-data join depends on which other suite modules are deployed alongside it.

Best for. Large enterprises already standardized on Palo Alto Networks that want AI security consolidated under an existing vendor relationship.

Where it stops. A suite brings the rollout and procurement cycle of a suite. If you need an agent inventory this quarter and your sensitive-data classification lives elsewhere, check whether the runtime layer alone answers the questions a CISO asks first.

7. Lakera (Check Point)

What it is. Lakera, now part of Check Point, is publicly positioned as an LLM security company: guardrails, prompt-injection detection and red-teaming for teams building AI applications, delivered through SDKs and APIs.

Against the seven questions. This is the AI guardrails category in its purest form, focused on the model and application boundary. It addresses part of question 4 for applications instrumented with it. Agent discovery, identity resolution, effective permissions, the sensitive-data join, browser and local agent visibility, and grant-level remediation are outside the category's scope.

Best for. Engineering teams building their own AI applications that need adversarial testing and runtime guardrails at the model layer.

Where it stops. Guardrails bound what a model will say. They do not bound what the credential behind the agent can do. A prompt injection that succeeds against an agent with secrets-plus-execute on one principal is a critical incident regardless of how good the filter was.

8. CrowdStrike

What it is. CrowdStrike is publicly positioned as an endpoint security and EDR leader, extending its platform into identity protection, cloud security, and AI and data protection modules.

Against the seven questions. The EDR category's natural strength is question 6: it has an agent on the endpoint and sees processes and local activity, which covers part of question 4 as well. Coverage of SaaS and cloud agent discovery, identity patterns, effective permissions, and the sensitive-data join depends on which additional modules an organization licenses.

Best for. Existing CrowdStrike customers who want AI-related endpoint telemetry consolidated into the sensor they already run.

Where it stops. Seeing the Claude Code process on a laptop is the start of the chain. The agent security question is what that process delegates through, what the resulting principal can reach in Drive and GitHub, and which classified files it touched. An endpoint sensor is one input to that graph.

📊 AI agent security platforms compared

The two tables below score each platform against the seven questions. Strac's row reflects the product as specified. For the other seven, "Partial" and "No" appear only where the public category clearly implies the answer; "Varies" means it depends on modules, platforms in scope or deployment, so ask the vendor directly. The remediation column measures grant-level, reversible changes (revoke, narrow, restrict), not prompt blocking.

Categories as publicly positioned in 2026. Roadmaps move quickly; validate every cell against your own environment and ask each vendor to demonstrate it live.

✨ How to choose an AI agent security platform by team type

The right platform depends less on feature lists and more on where your team is starting from.

Security team with existing DLP or DSPM. You already have the hardest part: a classified inventory of sensitive data. The question is whether the agent security platform can join to it. If the DLP and the agent graph are separate products, you will spend months exporting classifications from one into the other, and the join will be stale on arrival. Weight questions 5 and 6. Ask the vendor to show, on your tenant, "agent can reach N sensitive files" with the files listed and the classification evidence attached. Our data security platform guide covers what the combined platform looks like.

Platform team on AWS. Your agents are AgentCore runtimes, Lambda-backed tools and IAM roles. Weight questions 1, 3, 4 and 7 for cloud: can the platform enumerate runtimes and workload identities, simulate the high-risk actions that matter, and attribute CloudTrail events to the assumed-role session and from there to a specific agent? Ask how it handles two runtimes sharing one IAM role, and whether it tells you when S3 data events are not enabled instead of silently reporting "no sensitive reads." A generated policy delta with a console link is the right remediation for a first deployment; an automated IAM mutation on day one should make you nervous.

Identity team. You have a large NHI inventory and you are being asked which of those identities are AI agents. Weight question 2. An NHI tool lists 4,112 service accounts, keys and bots; the agent security platform should tell you that 214 are used by AI agents, 41 have no owner, 9 are stale, and 12 are shared human identities an agent hides behind. Ask how it discovers patterns E and F, which create no new principal and never appear in an identity inventory. If the answer involves only provider APIs, the inventory will be short by exactly the agents that matter most.

Whichever team you are, run the same test. Pick one agent you know about and ask the vendor to show its owner, the identity it runs as, its effective permissions, the sensitive resources behind them, and its last ten actions with the evidence source on each line. Wherever the answer is a theory instead of a record, that is where the platform stops.

✨ Agentic AI security platforms: where to start

Deploy read-only. Every platform on this list should run discovery without write scopes, and the one you choose should produce an inventory on your Google Workspace, AWS or GitHub tenant within a session. The first inventory is usually the most uncomfortable meeting of the quarter: 187 agents where you expected 40, 41 with no owner, 17 that can reach sensitive data, 6 critical. It is also the artifact every audit will ask for first. From there, follow the sequence in our AI agent security guide: inventory, identity, permissions, the data join, activity, findings, policy.

👉 Book a demo and we will run the agent inventory read-only against your own Google Workspace, AWS or GitHub tenant on the call, and show you the first ten findings with their evidence.

🌶️ Spicy FAQs on AI agent security platforms

What is the best AI agent security platform?

The best AI agent security platform is the one that answers all four questions for every agent (identity, authorization, activity, sensitive data) with the evidence for each. Strac is the only platform on this list built to answer all four in one graph and to see browser and local agents through sensors it already deploys. Category tools like SSPM, NHI, guardrails and DLP each answer one question well and belong in a program as components.

What are AI agent security tools supposed to do that identity tools do not?

Identity tools, including SSPM and NHI products, tell you what an agent's credential can do. AI agent security tools add two things: what the agent actually did, from audit logs, compliance APIs and runtime sensors, and which sensitive data sits behind each permission and action. A credential with drive.readonly is a fact about authorization. A credential that read payroll.xlsx containing SSNs is a finding.

Do AI agent monitoring tools replace guardrails and LLM firewalls?

No. Guardrails bound what a model will say. Agent security bounds what the agent can do and shows what it did, which is where the blast radius lives. A prompt injection that succeeds against an over-permissioned agent is still an incident; a least-privilege agent with no secrets-plus-execute on one principal limits how bad it gets. Run both, and make sure the agent security layer owns the credential and data questions.

How do these platforms handle AI agents that run in a browser or on a laptop?

Most do not. An agent that drives an employee's already-authenticated browser session creates no OAuth grant and no service account, so the SaaS audit log attributes its actions to the human. A local Claude Code or Cursor process with an MCP server in a config file never touches a provider admin API. Attributing those agents requires browser and endpoint evidence joined with provider activity, and the platform should label that attribution as correlated and show its confidence factors.

What does remediation look like in an AI agent security platform?

Bounded and reversible, or it is a report. Where a provider exposes a well-understood admin write API, the platform can apply the change itself, such as revoking a user's third-party app tokens in Google Workspace or restricting an app in Slack. Where it does not, the platform should generate the exact change and link to the console. Every option should state its scope, blast radius and reversibility, and discovery should never require the write scopes that remediation needs.

How long does it take to deploy an AI agent security platform?

Read-only discovery against SaaS control planes and cloud accounts should take an afternoon, with a first inventory within a session where provider quotas allow. Sensor coverage for browser and local agents depends on whether you already run those sensors; if browser or endpoint DLP is deployed, agent security attaches to it without a second rollout. Ask each vendor for time-to-first-finding rather than time-to-install.