Register and risk-tier use cases
Every AI use case has an accountable owner, business purpose, data classification, criticality, model approach, and lifecycle stage.
AI SDLC Governance
Move AI from experimentation to accountable production.
Role demonstratedAI governance operating-system architectReleasev0.1.0Data policy100% synthetic
AI adoption scales faster than governance evidence.
Leaders can have dozens of AI experiments moving through discovery, development, evaluation, and production without a shared view of ownership, risk tier, control evidence, exceptions, or readiness. The challenge is not adding another approval board. It is building governance into the delivery system so teams can move quickly while leaders retain accountable control.
Make lifecycle controls explicit
Required evidence is visible from intake through production across privacy, safety, security, SDLC controls, and operations.
Explain readiness and hard rules
Six weighted dimensions, status thresholds, and non-negotiable caps show exactly why a use case is ready, conditional, under review, or stopped.
End governance in decisions
Exceptions, blockers, top drivers, recommended actions, and a no-key executive brief turn governance evidence into accountable action.
Illustrative enterprise evidence model: Jira and Confluence · CI/CD pipelines · model evaluation and red-team evidence · security and privacy reviews · observability and incident controls · executive decision records. The public demo uses synthetic data, no API keys, and no employer connections.
This is AI governance designed as an operating system—not a policy document.
The application demonstrates how a senior or principal TPM can scale responsible AI adoption across product, engineering, risk, security, privacy, and operations: establish a common control model, preserve speed through explainability, surface exceptions early, and make production decisions defensible.