AI Governance Framework
Confidence scoring, audit trails, guardrails, and continuous calibration.
The Four Pillars of AI Governance
Confidence Scoring
Every AI output includes a calibrated confidence score. Above 95%? Auto-execute with logging. Below 80%? Mandatory human review. Between? Present the recommendation with evidence and let the human decide.
Full Audit Trails
Every AI-assisted decision is logged: the input data, the model's recommendation, the confidence score, the human's decision, and the eventual outcome. Complete traceability for compliance and learning.
Guardrails by Design
AI workflows can never execute destructive actions, modify production infrastructure, or make commitments without human approval. Guardrails are built into the workflow definition, not bolted on as an afterthought.
Continuous Calibration
Weekly accuracy reviews. When the model is wrong, we understand why and retune against real outcomes. Better data leads to better models leads to better recommendations, a compounding improvement cycle.
What AI Can and Cannot Do
| AI Can (with logging) | AI Cannot (requires human) |
|---|---|
| Classify and route tickets | Make architecture decisions |
| Draft responses from knowledge base | Send customer communications |
| Triage alerts and package context | Execute destructive actions |
| Generate reports from data | Approve budget changes |
| Recommend service tiers | Commit to SLAs or pricing |
| Extract requirements from conversations | Override escalation decisions |
Compliance Readiness
The audit trail is designed to satisfy compliance requirements across frameworks:
- SOC 2: AI decision logging aligned with control monitoring expectations
- HIPAA: PHI is excluded from AI workflows by design; data boundaries enforced at the pipeline level
- ISO 27001: AI operations governed by controls aligned with information security management standards
- Internal audit: any decision can be reconstructed from logs: who asked, what the AI recommended, who approved, what happened