What each is for, where Pillar is strong, and how the two differ, capability by capability.
Pillar Security · Based on the vendor's public documentation, last reviewed .
Pillar
Pillar covers AI discovery, posture, red teaming, runtime guardrails and governance. Its Agentic Endpoint writes hooks into coding agents on developers' machines and can block an action before it runs.
Managed cloud, self-hosted on Kubernetes in your own cloud account or data centre, or hybrid; self-hosted installations can run with no data flowing back to Pillar. Guardrails connect through AI gateways or an API. Agentic Endpoint is installed through MDM as a per-user service on macOS, Linux and Windows.
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 | Pillar |
|---|---|---|---|
| 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 Agentic Endpoint runs in the pre-tool hook of Claude Code, Codex CLI and Cursor and blocks or asks before each action. Guardrails in AI gateways block prompt injection and policy violations. [2] [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 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 Linking an agent to the employee who runs it through the company identity provider is not described in the public documentation. [2] |
| 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 Controls tighten once untrusted web or MCP content enters a session. A scope issued by the server for each task is not described in the public documentation. [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 Each endpoint control is set to off, monitor or block, with per-server tool allow lists, and guardrail policies can mask, block or log. 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. | Partly A control can ask the developer to confirm an action before it runs, and the developer decides. Approval by another person is not described. [2] |
| 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 Agentic Endpoint blocks reads of keys and credential files from reaching external destinations and flags hard-coded MCP credentials. Keeping credentials out of the agent's reach through a broker is not described. [2] [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 Every block or ask decision appears in the activity view with the session timeline. Self-hosted installations keep the audit trail in your own environment. [2] [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 Installed through MDM on macOS, Linux and Windows, it scans six coding agents and blocks before execution in Claude Code, Codex CLI and Cursor. On macOS and Linux, the managed hooks for Codex and Cursor are protected from being turned off by developers. [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 Guardrails detect prompt injection and mask PII, health data and secrets in prompts and responses. Tool poisoning through MCP metadata is also detected. [3] [5] |
| 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. | AI applications behind gateways such as LiteLLM, Kong and TrueFoundry, or any gateway through the API. Agentic Endpoint scans Claude Code, Cursor, Codex CLI, Gemini CLI, OpenCode and Pi, and blocks before execution in Claude Code, Codex CLI and Cursor. |
| Deployment | How the product is delivered. | In Vekri's cloud, or self-hosted in your Kubernetes cluster with no connection to Vekri. | Managed cloud, self-hosted on Kubernetes in your own cloud account or data centre, or hybrid; self-hosted installations can run with no data flowing back to Pillar. Guardrails connect through AI gateways or an API. Agentic Endpoint is installed through MDM as a per-user service on macOS, Linux and Windows. |
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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