Claude
GPT
Gemini
Llama
Mistral
Grok
DeepSeek
+ any LLM
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Architecture, threat model, and cryptographic design in depth. Reviewed and sent personally by our team.
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One gateway between your systems and everyone who touches them
Authenticate
A human or AI agent authenticates through the gateway. Identity is verified before any request proceeds.
Evaluate policy
The policy engine checks the request against your rules: who is asking, what they want, when, and under which conditions.
Inject secret
On approval, the zero knowledge vault releases the credential directly into the session, scoped and time bound. Never exposed in the clear.
Log everything
Every request, approval, and secret use is written to a complete, audit ready trail you can export or connect directly to your eDiscovery or SIEM platform.
Connected to the tools your enterprise already runs on
Gatekeeper sits in front of the services your humans and agents use every day. Connect a native MCP server, or wrap any REST or gRPC API behind the same policy engine and audit trail.
Built to satisfy the controls your security team already requires
Sample attestation reports and full certification documents are available at trust.station70.com.
Visit trust centerA gateway routes requests. Gatekeeper seals the keys
MCP gateways came from IT governance. Gatekeeper comes from protecting assets where a single mistake is irreversible. The difference shows up in the architecture.
The credential never leaves the vault
Secrets live in a zero knowledge vault and are injected directly into the approved session, scoped and time bound. The agent, the LLM, and Gatekeeper itself never hold the key in the clear.
Hardware attested enforcement
Every policy decision executes on secure hardware and produces cryptographic attestation. Verdicts cannot be bypassed or tampered with by the agent or any intermediary.
Deterministic, deny by default
Rule based enforcement with no LLM discretion. Every request is verified against policy you control, and anything outside it is denied before it executes.
Built where failure is irreversible
Built by the team protecting billions in digital assets, with cryptography audited by Trail of Bits, SOC 2 Type 2 controls, and annual penetration testing.
Financial spend limits
Spend caps, velocity limits, and transaction size thresholds are enforced at the gateway, so the value at risk is bounded before any action executes.
Human approval step up
High risk actions can require quorum or four eyes approval mid request. A human signs off before the action executes, not after it appears in a log.
Twelve dimensions, combined however your risk model requires.
Policy, secrets, and agent access, under one roof
Policy engine
Define who and what can reach every system, with rules evaluated on every single request.
Zero knowledge secrets vault
API keys, tokens, and credentials encrypted at rest and released only under policy, never in the clear.
MCP gateway
A Model Context Protocol gateway that governs exactly which tools and data your AI agents can reach.
Agent identity
Give every AI agent a scoped, revocable identity, fully separate from human users.
Human approval workflows
Require a human in the loop for sensitive actions, with fast one click approvals.
Audit and observability
A complete, exportable record of every request, approval, and secret use across humans and agents.
First policy enforced in minutes, not weeks
Connect
Point your MCP clients at Gatekeeper, drop the SDK into your agent code, or register connectors from the admin console. Your team keeps the clients they already use.
Load the vault
Bring credentials in through automated import workflows or add them manually. Everything is sealed in the zero knowledge vault the moment it lands.
Enforce
Start from prebuilt policy templates, then refine them in the console or as code through the API. Deny by default from the first request.
Rolling out to a team? Employees request access to approved connectors, admins approve and set policy, and access is published with enforcement from the first call.
Gatekeeper is a policy engine, a zero knowledge secrets vault, and an MCP gateway in one product. It sits between your people and AI agents and the systems, credentials, and tools they need, and enforces policy you control on every request.
The MCP gateway governs how your AI agents use the Model Context Protocol. It controls exactly which tools, data sources, and actions each agent can reach, so an agent only ever operates inside the boundaries you define.
No. Gatekeeper is zero knowledge. Secrets are encrypted end to end and released directly into a session under policy. Gatekeeper never sees a credential in the clear and never stores one it could read.
Every request is evaluated against your rules in real time: the identity making the request, the resource it wants, and the conditions it must satisfy. Only requests that pass policy proceed, and the decision is recorded.
Yes. For sensitive actions you can require a human in the loop. Gatekeeper routes the request for a fast, one click approval before any secret is released or action is taken.
Everything. Every request, policy decision, approval, and secret use across both humans and agents is written to a complete trail you can export for security review and compliance.
Reference architectures
6 production deployment patterns across SaaS, on prem, and hybrid, from enterprise copilots to MPC wallet operations. Dataflow, components, and security considerations for each.
Bring policy, secrets, and agent access under one gateway
A technical briefing with our security and solutions team. Architecture walkthrough, policy design, and integration mapping for your systems and agents.