Validation and confidence

How preset AI evaluates quality and determines trust levels for automated guidance and enforcement.

Validation goals

Validation should answer:

• Is the output structurally and semantically correct?
• How severe are detected issues?
• What fixes are safe and actionable?

Confidence goals

Confidence should answer:

• How reliable is the system's interpretation or mapping?
• How much automation is safe at this stage?
• When should workflows require human review?

Practical usage

Validation and confidence work together:

• High validation confidence + low-risk scope → faster apply paths
• Low confidence or ambiguous findings → proposal + review path
• Repeated low-confidence zones → improve taxonomy, rules, and metadata

Operational guidance

Track and use:

• violation severity distribution
• false-positive and false-negative signals
• confidence trends by artifact type
• remediation latency

These should inform rule tuning and maturity-mode progression. See Rule model for the underlying rule contract and Active guardrails for how validation feeds the enforcement loop.