AI Safety & Security Layer is designed to give engineering, security, and compliance teams one place to register AI use cases, score risk, map policies to controls, route model calls safely, and produce executive-ready evidence.
The product brings practical governance artifacts and a runtime control path into one workflow. Teams can start with one or two LLM use cases, then expand coverage as their AI footprint grows.
Register AI systems, owners, model providers, data types, lifecycle status, and risk level.
Score workflows across data sensitivity, autonomy, user exposure, prompt injection, output harm, and logging risk.
Convert policy requirements into technical controls and track whether each control is implemented, partial, or missing.
Route model calls through authenticated, rate-limited, observable, provider-neutral access.
Monitor usage, errors, latency, estimated cost, blocked requests, guardrail triggers, and open gaps.
Generate a concise leadership report with implemented controls, residual risk, and next actions.
The registry becomes the customer’s source of truth for AI usage. It shows what exists, who owns it, what data it touches, and whether the required controls are in place.
The risk workflow converts a short assessment into a practical control plan. High-risk workflows are not blocked forever; they are routed through the right safeguards before release.
Policies become actionable only when they map to specific controls. The product tracks what is required, what is implemented, and what evidence exists.
Customer PII must not be sent to unapproved model providers or stored in raw prompt logs.
Applications can route model calls through one approved path with authentication, consumer-level access, model routing, cost visibility, and security detection for exposed keys, sensitive data, dangerous tool calls, and policy violations.
The dashboard combines governance status with runtime telemetry. Teams can see AI systems, risk levels, blocked requests, implemented controls, and evidence for customer or management reviews.
A focused rollout starts with one real AI workflow, an initial AI registry, model access policy, and a controlled route for model traffic.
Share one LLM workflow you are considering or already running. We can walk through how AI inventory, risk review, model access controls, gateway routing, dashboards, and evidence reporting fit your environment.