AI & Model Governance
AI is used for leverage. Governance remains the safety layer.
SupraOS uses AI for discovery, synthesis, classification, research, analysis, drafting, decision support, agent execution, and operating-review generation. Model output is not treated as truth by default. High-risk outcomes require grounding, policy checks, approvals, evidence, and Receipts.
Public commitments
- Customer data is not used to train shared models by default.
- Model output is not treated as truth by default.
- High-risk actions require policy checks and approvals.
- External intelligence is source-linked and confidence-scored.
- Customer-approved deployment and model choices may vary by engagement.
- Generated outputs should be tied to source evidence where possible.
Model output controls
- Source linking for factual claims where possible.
- Confidence scoring for hypotheses and external signals.
- Human approval for high-risk decisions.
- Policy enforcement before system actions.
- Evidence validation for Receipt completion.
- Separation of recommendations from execution authority.
Model use cases
| Use case | Example |
|---|---|
| Classification | Detect action types, risk level, evidence class, source category. |
| Summarization | Account briefs, incident summaries, operating reviews. |
| Analysis | Value-leak hypotheses, source coverage gaps, KPI variance. |
| Research | External market, competitor, regulatory, vendor context. |
| Drafting | Internal memos, approval requests, outreach drafts. |
| Agent execution | Running approved tasks inside Charter boundaries. |
AI governance alignment
SupraOS trust documentation should eventually map to recognized AI governance concepts such as risk management, transparency, human oversight, logging, and lifecycle controls. SupraOS does not claim compliance unless formal control mapping and legal review are complete.
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