What each is for, where Lasso is strong, and how the two differ, capability by capability.
Lasso Security · Based on the vendor's public documentation, last reviewed .
Lasso
Lasso discovers AI agents and applications, red-teams them and enforces policy at runtime through a proxy, an AI or MCP gateway, or coding-agent hooks, with prompt-injection detection, data masking and checks on agent intent.
Runtime enforcement sits inline at a proxy, an API or an AI gateway. Coding-agent controls are deployed through the agents' enterprise managed settings, with no agent to install, and the Claude Enterprise integration reads Anthropic's Compliance API. The MCP Gateway is open source (MIT). Hosting options are not described on the public site.
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 | Lasso |
|---|---|---|---|
| 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. | Documented Policies are enforced inline at a proxy, an API or an AI gateway, and the coding-agent hooks inspect each tool call before it runs, flagging or blocking it. [1] [2] |
| 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 Verifying the identity of an agent running as a workload, for example with Kubernetes or SPIFFE credentials, 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 Administrators can control which users and teams connect to which MCP servers. Agents signing in as the employee through the company identity provider is not described. [3] |
| 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 Security teams define the tools permitted within a scope, and the intent engine compares actions with the developer's instruction. A scope issued by the server for each task is not described. [2] |
| 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 Security teams define the permitted tools, and calls outside them are flagged or blocked; access to MCP servers can be restricted by role. A published policy language or versioned policy as code is not described. [2] [3] |
| 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. Administrators can manually block an MCP server that scores as high risk. [3] |
| 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 Data-loss prevention detects API keys and credentials in tool calls and can mask them. Brokering credentials so that the agent never holds them is not described. [3] [5] |
| 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. | Documented MCP connections and tool calls are kept in an audit trail that can be exported to a SIEM, and usage control records inputs, policy decisions and enforcement actions. [3] [4] |
| 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. | Documented Lasso uses the lifecycle hooks of Claude Code, Cursor and Codex, deployed through managed settings, to inspect each tool call before it runs. Supported operating systems are not described. [2] |
| 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 Detects prompt injection in files, web responses and MCP output, and detects and masks PII, payment data and secrets before they reach models or external servers. [2] [3] [4] |
| 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. | Discovery of agents and applications, employee AI use through a browser extension or proxy integrations, MCP servers, Claude Enterprise, and coding assistants including Claude Code, Cursor, Codex and OpenCode. Red teaming draws on a library of more than 3,000 attacks. |
| Deployment | How the product is delivered. | In Vekri's cloud, or self-hosted in your Kubernetes cluster with no connection to Vekri. | Runtime enforcement sits inline at a proxy, an API or an AI gateway. Coding-agent controls are deployed through the agents' enterprise managed settings, with no agent to install, and the Claude Enterprise integration reads Anthropic's Compliance API. The MCP Gateway is open source (MIT). Hosting options are not described on the public site. |
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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