CCGE: Fail-Closed Governance Engine
Fail-closed governance engine for healthcare AI systems, ensuring deterministic boundaries around probabilistic models.
Private Repository
This system is listed as a B2B case note. The repository itself is not public; the page is here to show the architecture thesis and engagement relevance.
About This Work
CCGE is listed here as a private fail-closed governance system. Repository access is not public; the page exists to show the architecture thesis and engagement relevance.
The Problem
In regulated environments like healthcare, an AI model providing a highly probable but factually incorrect answer (a hallucination) is not just a bug—it is a critical liability fail-state. Standard LLM wrappers attempt to solve this via basic prompting or simple RAG, which still leaves the final output entirely probabilistic.
What I Built
CCGE (Clinical Context Governance Engine) is a fail-closed verification layer that sits between the reasoning model and the final output.
Instead of asking the model to "be careful," CCGE forces the model's output through a deterministic, rules-based static analysis engine.
Key Design Principles
- Default Deny: If the system cannot cryptographically trace a generated statement back to a verified medical ontology, the output is nullified.
- Audit Trails: Every token generated is logged alongside the specific reasoning chain and source document that justified it.
- Execution Sandboxing: The model cannot execute side-effects (API calls, database writes) without explicit human-in-the-loop authorization orchestrated by CCGE.
Why It Matters
Teams can finally deploy generative AI into clinical workflows knowing that the system is safe, even if the model occasionally drifts. It transitions the conversation from "How accurate is the model?" to "How secure is the pipeline?"
Announcements
synced Mar 13, 2026