Control who can call what
Use authenticated access, approved registries and target-aware policy instead of relying on shared provider credentials.
AI security
Give AI access.
Keep data boundaries.
Put security policy at the boundary between your teams and AI services. Inspect routed model and tool traffic, control access and connect your existing DLP to a hosted, vendor-agnostic platform.
AI security workflowGoverned
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01
Identify callersUser and service-principal context
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02
Constrain accessRegistered models and MCP methods
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03
Inspect contentLocal scanning and optional ICAP DLP
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04
Review decisionsAllowed and blocked gateway requests
Identity · Policy · EvidenceWorkflow illustration
Identity, inspection & evidence
Control access.
Inspect what crosses.
Inspect the AI path
Apply configured scanning rules to requests and responses. Connect enterprise DLP where policy requires external inspection.
Investigate with context
Review caller, target, decision and available request details. Scope sensitive log access and retention to your deployment needs.
Fit your security architecture
Put existing controls
in the AI path.
Create an approved alternative
Give teams governed model access and useful agents so the official path supports their work.
Bring existing DLP into the flow
Inspect supported LLM and MCP traffic through ICAP, with fail-closed error handling for enabled checks.
Review the control boundary
AI Warden governs configured assets and routed traffic. Combine it with your broader endpoint, network and SaaS controls for coverage beyond the gateway.
Explore the platform
See the controls behind the outcome.
See AI Warden in action
Bring one workflow.
See the security boundary.
Walk through the identities, data, tools and inspection decisions in a real AI use case with our Swiss-based engineering team.