What each is for, where Prompt Security is strong, and how the two differ, capability by capability.
SentinelOne · Based on the vendor's public documentation, last reviewed .
Prompt Security
Prompt Security, part of SentinelOne, governs employee use of AI tools, protects homegrown AI applications with a runtime firewall, and discovers and risk-scores agents and MCP servers, with an MCP Gateway that can allow, block, filter or redact.
Part of the SentinelOne Singularity Platform and managed from its console. It reaches AI use through a lightweight agent, browser extensions, APIs and an MCP Gateway, and SentinelOne describes the architecture as fitting SaaS or self-hosted environments. Claude Enterprise is covered through the Compliance API without an endpoint agent.
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 | Prompt Security |
|---|---|---|---|
| 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 MCP Gateway sits between AI applications and MCP servers and applies allow, block, filter or redact actions, with rules by user, agent and action type. How each individual tool call is decided before it runs is not detailed. [3] [5] |
| 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 Issuing or verifying a platform credential for an agent workload is not described in the public documentation. |
| 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 Rules can apply by role, department and user. Integration with the company identity provider, and agents signing in as the employee, are not described in the public documentation. [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 SentinelOne describes keeping agents within a defined scope through allow and block rules by user, agent and action type. A permission scope issued by the server for each task is not described. [5] [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 Rules apply by user, role, department, agent and action type, with risk scores for agents and MCP servers. A policy language and deny-by-default behavior are not described. [4] [5] [6] |
| 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 A step in which a person approves one specific operation is not described in the public documentation. |
| 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 Secrets are redacted from prompts (see prompt and output inspection). Keeping backend credentials out of the agent's reach, for example through brokering, is not described in the public documentation. |
| 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 Prompt Security keeps a searchable, timestamped log of each agent session, action and tool, and logs prompts and responses for audit. [3] [6] |
| 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 Prompt Security inventories code assistants such as GitHub Copilot, Cursor and Claude Code, blocks unsanctioned tools and redacts sensitive data before prompts leave the device. Watching or blocking a coding agent's tool actions on the device is not described. [2] [4] |
| 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 A runtime firewall blocks prompt injection and jailbreaks and scrubs sensitive data from responses, and AI Usage Control redacts PII and secrets before a prompt leaves the device. [4] [7] |
| 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. | More than 15,000 AI tools and services by SentinelOne's count, code assistants such as GitHub Copilot, Cursor and Claude Code, homegrown AI applications, and agents and MCP servers. |
| Deployment | How the product is delivered. | In Vekri's cloud, or self-hosted in your Kubernetes cluster with no connection to Vekri. | Part of the SentinelOne Singularity Platform and managed from its console. It reaches AI use through a lightweight agent, browser extensions, APIs and an MCP Gateway, and SentinelOne describes the architecture as fitting SaaS or self-hosted environments. Claude Enterprise is covered through the Compliance API without an endpoint agent. |
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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