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CIRCUIT Framework

Circuit-Informed Risk & Control — Understanding, Inventory & Transparency

An open standard for AI interpretability governance.

License: Apache 2.0 Version

Deploy AI you can explain. Defend AI you can inspect. Trust AI you can audit.


What CIRCUIT Is

Enterprise AI governance today is paperwork wrapped around a black box. CIRCUIT is three things and only three things:

A Score The Interpretability Maturity Score (IMS) — a 0–5 evidence ratchet. You don't declare a level. You produce the artifacts that prove it.
A Registry An eight-section YAML schema, one document per deployed AI system, machine-readable and diffable in Git.
A Control The Circuit Risk Score (CRS) and ten binding rules that turn the score into a deployment decision a pipeline can enforce.

Remove any one of the three and the other two stop working.


The Formula

CRS = Risk Tier × (6 − IMS) × Decision Consequence Weight
Band Range Meaning Approval
🟢 Green 1–12 Interpretability adequate for the deployment context Standard change-management approval
🟡 Amber 13–47 Watchlist; compensating controls recommended AI Governance Committee quarterly review
🔴 Red 48–96 Compensating controls mandatory; active remediation CISO + AIGC sign-off; ≤ 180 days to reach Amber
🟣 Purple 97–120 Not deployable in current configuration Blocked; reposition consequence or vendor

Variable domains:

  • Risk Tier: Low=1, Moderate=2, High=3, Critical=4
  • IMS: 0–5 (interpretability deficit = 6 − IMS)
  • DCW: Advisory=1, Recommended=2, Automated=3, Irreversible=4, Catastrophic=5
  • Category ceilings: A (open weights) → IMS 5 max · B (API) → IMS 3 max · C (embedded vendor) → IMS 2 max

Quick Start

1. Read the specificationwhitepaper.html — the full normative specification (55 pages)

2. Score your first three modelstemplates/registry-entry.yaml — blank template, fill in one per model

3. Send the vendor questionnairequestionnaire/vendor-questionnaire.md — 29 questions, send to every API and embedded-AI vendor

4. Compute the CRStooling/crs.py — reference calculator


Repository Contents

circuit-framework/
├── whitepaper.html              ← canonical specification (source of truth)
├── registry-schema.yaml         ← normative schema — Appendix B
│
├── data/
│   ├── framework.json           ← factor scales, band thresholds, KPI thresholds
│   └── questionnaire.json       ← 29 questions in machine-readable form
│
├── templates/
│   ├── registry-entry.yaml      ← blank template (fill in one per model)
│   ├── example-A-dlp-classifier.yaml   ← worked example: Category A, Critical, IMS 4
│   ├── example-B-soc-triage.yaml       ← worked example: Category B, High, IMS 2
│   └── example-C-m365-copilot.yaml     ← worked example: Category C, Critical, IMS 1
│
├── questionnaire/
│   └── vendor-questionnaire.md  ← "Show Me Your Circuits" — Appendix C
│
└── tooling/
    └── crs.py                   ← CRS calculator (reference implementation)

Adoption Stages

Adoption is a progression, not a project. Each stage compounds the last.

Stage Objective Exit criteria
1 — Foundation Complete picture of what you have; halt anything in Purple band Every deployed model has a registry entry
2 — Assess Replace opaque scores with real evidence; baseline KPIs All High-tier models have KPI baselines
3 — Operate Move from one-time assessment to continuous monitoring Production monitoring active; first governance review complete
4 — Mature Automate governance; contribute back to the standard At least one model under continuous automated monitoring

The Foundation stage requires no new tooling spend. The risk-scoring formula runs in a spreadsheet.


Community Standard

CIRCUIT is a vendor-neutral, community-owned standard. Jumpmind is its initial adopter, not its proprietor.

Change proposals, schema extensions, and tooling integrations are discussed in GitHub Discussions and reviewed by the steering committee before adoption.

→ See CONTRIBUTING.md for the full contribution process.


More Information


License

Apache License 2.0 — see LICENSE for full terms.

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Circuit-Informed Risk & Control — Understanding, Inventory & Transparency. An open standard for AI interpretability governance.

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