Docs · AI systems
AI-system governance workflows
Learn how SentinelAI governs runtime AI systems with linked models, use cases, datasets, readiness posture, and release references.
Overview
AI systems provide the runtime operating layer for governed AI. Instead of stopping at model inventory, SentinelAI lets teams maintain records for the deployed systems that combine approved models, use cases, datasets, and release context into one reviewable unit.
This page is part of the public SentinelAI documentation layer. It is meant to accelerate orientation and evaluation while staying aligned with the product’s governance-focused positioning and messaging guardrails.
Runtime system boundary
Each AI-system record is designed to describe the operational unit that actually goes live, including ownership, deployment target, lifecycle stage, and current release reference.
Dependency rollups
AI systems become more useful when they stay linked to the models, use cases, datasets, prompts, and retrieval sources that support them instead of leaving that dependency set implicit.
- Use AI systems to roll up governed dependencies into one runtime-facing record.
- Keep readiness state and release status close to the system that operational teams recognize.
- Anchor evaluation, telemetry, and case workflows to the AI system that changed.
Readiness and release posture
Runtime governance works best when readiness, release records, and follow-up workflows all refer back to the same AI-system object instead of rebuilding system context each time.