Docs · Platform overview
Platform overview
Understand how SentinelAI connects inventory, governance workflows, monitoring context, and reporting into one operating surface.
Overview
SentinelAI brings together the core records and workflows that usually sit across spreadsheets, inboxes, tickets, and point tools. The platform is intended to give governance teams a clearer lifecycle model from intake through runtime governance, monitoring, and reporting.
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.
Core product domains
The platform spans use-case intake, model registry, AI systems, prompt and RAG governance, dataset governance, vendor oversight, semantic administration, compliance workflows, telemetry connectors, governance cases, reporting, and administrative controls.
- Each domain can hold its own records while still linking into the broader governance lifecycle.
- The goal is to keep source evidence, runtime dependencies, semantic relationships, and decision history closer to the operating workflow.
Governance lifecycle
The platform supports an operating loop of intake, registration, runtime AI-system management, prompt and retrieval governance, review, monitoring, and reporting on AI systems and the controls around them.
- Capture use cases, systems, prompts, sources, and supporting data with owners and context.
- Standardize terms and relationships with taxonomy and ontology administration.
- Coordinate approvals, evaluations, releases, cases, obligations, and remediation work.
- Preserve evidence for internal review, executive updates, and buyer diligence.
Where to go next
Once the lifecycle is clear, the next best guide depends on whether your team starts with model inventory, dataset traceability, vendors, or compliance execution.