PII pseudonymization
Replace configured sensitive values with protected tokens before they reach an approved model, RAG pipeline or tool.
Data governance
Control sensitive data before AI uses it.
Protect configured prompt, RAG, agent and MCP routes with de-identification, policy checks and traceable evidence before an approved model or tool receives data.
Practical outcomeUseful AI context without sending unnecessary identity.
What we provide
What you can do with Securelay.
Start with one workflow, connect the systems in scope, and expand only after the control has been tested.
Purpose and consent checks
Use PBAC, consent state and data policy to decide whether a protected value may be disclosed.
Provider and region controls
Restrict configured AI connections to approved providers and deployment regions.
Lineage and evidence
Record the provider, purpose, transformations, decisions and value-free receipts for the protected path.
How it works
How we put it to work.
Protect
Detect configured sensitive data and replace it with a protected reference.
Decide
Evaluate role, purpose, consent, provider and policy requirements.
Use and prove
Send governed context, then retain evidence of the configured operation and any approved reveal.
Scope boundary
Securelay enforces these controls only on the application and AI routes you integrate. It does not control agent planning, guarantee complete detection, or certify an AI system as compliant.
Further reading
From the requirement to the data path.
Explore the primary sources and practical implementation decisions in our field notes.
data governanceBlocking a chatbot does not govern the AI data path.