What each is for, where Lakera is strong, and how the two differ, capability by capability.
Check Point Software Technologies · Based on the vendor's public documentation, last reviewed .
Lakera
Lakera, part of Check Point since October 2025, screens prompts, model output and agent tool calls for prompt attacks, data leakage and risky actions. Applications send content to its Guard API and act on the result.
A SaaS API that processes requests in the EU, the US or Singapore, or self-hosted AI Guardrails on Kubernetes with a Helm chart or on Docker, including air-gapped installs. Self-hosting needs an Enterprise licence.
Vekri
Runtime authorization for AI agents. Vekri Gateway checks every tool call against a verified identity, a task scope and Cedar policy; privileged writes wait for a person's approval of the exact operation; a broker executes with credentials the agent never sees.
In Vekri's cloud, or self-hosted in your Kubernetes cluster with no connection to Vekri.
Side by side
| Capability | What it means | Vekri | Lakera |
|---|---|---|---|
| Decides each tool call inline | Every agent tool call is allowed or denied before it reaches the protected system. | Yes Vekri Gateway decides every MCP tool call sent through it before the broker executes it; a denied call never reaches the system. It is a boundary only when it is the only path to that system. | Partly The application sends each message or proposed tool call to the Guard API and acts on the flagged result; the API itself does not block. Among the platform connectors, only Microsoft Copilot Studio has inline runtime protection. [1] [3] |
| Verified workload identity | An agent running as a workload proves who it is with a credential the platform issued, not a shared key. | Yes Kubernetes projected service-account tokens, verified with TokenReview before Vekri issues a short-lived token for the run; every call is then authenticated. SPIFFE X.509-SVIDs from a configured trust bundle (no Workload API yet). | Not in public docs Requests authenticate with an API key. Verifying the identity of the workload behind a request is not described in the public documentation. [1] |
| Employee agents use company SSO | Agents that employees run sign in as that employee, through the company identity provider. | Yes Vekri is the MCP authorization server for Claude Code and federates to your OpenID Connect provider. SAML sign-in is not available yet. | Not in public docs The application can pass a user ID as request metadata for logs and analysis. Agents signing in as an employee through the company identity provider is not described. [4] |
| Task-scoped permissions from the server | What an agent may do is set per task by the server, not by the agent or its prompt. | Yes A run scope for workloads, or a protected access assignment for employees. The effective permission is always an intersection, never a widening. | Not in public docs Each project is assigned one policy, which applies to all of its requests. A scope issued by the server for each task is not described in the public documentation. [6] |
| Policy as code, deny by default | Decisions come from versioned policy, and anything not allowed is refused. | Yes Cedar over typed operations. Unknown operations and evaluation errors are denied, and a revision can be simulated before it is published. | Partly A policy selects the guardrails and a sensitivity level for each project, and a tool allow or deny list flags calls to tools outside it. A versioned policy language with default deny is not described. [6] [5] |
| Approval of the exact operation | A person approves one specific operation, and the approval cannot be reused for another. | Yes Privileged writes wait for another person to approve that operation, bound to its digest, short-lived and single use, with separation of duties. | Not in public docs Human approval of a single tool call is not described in the public documentation. A flagged call can return guidance that the application feeds back to the agent. [5] |
| Agent never holds backend credentials | Secrets for the protected system stay out of the agent's reach. | Yes A broker executes with narrowly scoped Kubernetes or GitHub App credentials. Incoming tokens are never forwarded. | Not in public docs The data-leakage guardrails detect sensitive data in content. Keeping the protected system's credentials out of the agent's reach is not described in the public documentation. [1] |
| Audit evidence of decision and outcome | Each call leaves a durable record of what was decided and what happened. | Yes Intent is recorded durably before execution and the outcome after; an uncertain outcome is never retried blindly. | Partly Screening results are logged, can be read through the Platform API and can be exported to a SIEM. Because the application carries out the action, recording its outcome is not described. [4] [5] |
| AI agents on workstations | Finds AI coding agents and other AI applications on employee devices, and watches or controls what they do there. | Partly Vekri Endpoint discovers coding agents, desktop and IDE AI apps, local models and their MCP servers on macOS and Linux, and its local MCP gateway decides each MCP tool call sent through it (Claude Code verified connecting; tool calls verified with an MCP client library). On macOS, a network gateway mediates MCP requests to approved remote servers, and agent control stops AI agents the organization has not allowed. Hooks observe built-in tools without blocking them; Windows is not supported. | Partly Workforce AI Security says its visibility extends to developer tools, coding agents and MCP tools, but it names no agent and does not describe what runs on the device. [7] |
| Prompt and output inspection | Detects prompt injection or sensitive data in what models read and write. | Partly Tool results on Vekri Gateway and the local MCP gateway are inspected for prompt injection before the agent reads them; sensitive-data categories are detected on the device, off by default. Browsers and file uploads are not inspected. | Documented The core of the product. Prompt-attack detection covers user input, retrieved documents and tool output, alongside PII, data-leakage, moderation and malicious-link guardrails. [1] [6] |
| Systems covered | Which systems, platforms or agents the product works with. | Kubernetes and GitHub, with GitLab, PostgreSQL, MySQL, MongoDB, Slack, Jira, Linear, Asana, ClickUp, monday.com, HubSpot, Pipedrive, Zendesk, Freshdesk and custom OpenAPI connectors in preview. | Any application or agent that can call the Guard API. Discovery connectors read configuration from Amazon Bedrock and AgentCore, Google Cloud, Salesforce Agentforce, n8n and Relevance AI; Microsoft Copilot Studio also has inline runtime protection. Red teaming covers GenAI applications. |
| Deployment | How the product is delivered. | In Vekri's cloud, or self-hosted in your Kubernetes cluster with no connection to Vekri. | A SaaS API that processes requests in the EU, the US or Singapore, or self-hosted AI Guardrails on Kubernetes with a Helm chart or on Docker, including air-gapped installs. Self-hosting needs an Enterprise licence. |
Every agent tool call is allowed or denied before it reaches the protected system.
An agent running as a workload proves who it is with a credential the platform issued, not a shared key.
Agents that employees run sign in as that employee, through the company identity provider.
What an agent may do is set per task by the server, not by the agent or its prompt.
Decisions come from versioned policy, and anything not allowed is refused.
A person approves one specific operation, and the approval cannot be reused for another.
Secrets for the protected system stay out of the agent's reach.
Each call leaves a durable record of what was decided and what happened.
Finds AI coding agents and other AI applications on employee devices, and watches or controls what they do there.
Detects prompt injection or sensitive data in what models read and write.
Which systems, platforms or agents the product works with.
How the product is delivered.
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