Evaluate AI Compliance Engine

Evaluate AI Compliance Engine — measure check accuracy, evidence quality, framework coverage, risk scoring calibration, false positive/negative rate. Use when: evaluate, audit compliance.

Evaluate AI Compliance Engine

When to Use

Validate compliance check accuracy against known test scenarios
Measure evidence collection completeness
Assess framework coverage (are all requirements checked?)
Calibrate risk scoring against expert assessments
Gate compliance engine deployment with quality thresholds

Compliance Engine Metrics & Targets

Metric	Target	How to Measure
Check accuracy	≥ 90%	LLM assessment matches expert review
Evidence completeness	≥ 95%	All required evidence sources connected
Framework coverage	100%	All regulation requirements have checks
Risk score calibration	Within ±1 of expert	Compare LLM risk vs human auditor risk
False positive rate	< 10%	Compliant items flagged as non-compliant
False negative rate	< 5%	Non-compliant items marked as pass
Report quality	≥ 4.0/5.0	Auditor rating of generated reports
Check execution time	< 30 min per framework	Full framework scan duration

Step 1: Prepare Compliance Test Scenarios

{"system": "test-rag-app", "framework": "GDPR", "check": "data-retention-policy",
 "known_status": "fail", "reason": "No retention policy documented"}
{"system": "test-rag-app", "framework": "GDPR", "check": "encryption-at-rest",
 "known_status": "pass", "evidence": "Azure Storage CMK enabled"}
{"system": "test-voice-app", "framework": "HIPAA", "check": "phi-access-control",
 "known_status": "partial", "reason": "RBAC configured but not audited"}

Minimum: 50 test scenarios with known pass/fail/partial status per framework.

Step 2: Validate Check Accuracy

python evaluation/eval.py --test-set evaluation/compliance-tests.jsonl --metrics check_accuracy

Compare LLM compliance assessment vs known ground truth
Per-framework accuracy (GDPR, HIPAA, EU AI Act, SOC 2)
Identify check types where LLM consistently wrong

Step 3: Validate Evidence Quality

For each check: is the evidence sufficient for an auditor?
Does evidence link to specific resources (not generic)?
Is evidence timestamped and verifiable?
Can an auditor reproduce the finding from the evidence?

Step 4: Calibrate Risk Scoring

Test Scenario	Expert Risk	LLM Risk	Delta
No encryption at rest	9 (Critical)	Measure	Target ±1
Missing audit logs	7 (High)	Measure	Target ±1
Documentation gap	3 (Low)	Measure	Target ±1

Step 5: Test False Negative Detection

Deploy test system with known violations
Run compliance engine → verify all violations detected
False negative (missed violation) = most dangerous outcome
Target: < 5% false negative rate

Step 6: Generate Report

python evaluation/eval.py --all --output evaluation/compliance-report.json --ci-gate

Quality Gate Decision

Result	Action
All PASS	Deploy compliance engine for audit use
Check accuracy < 85%	Improve evidence grounding in LLM prompts
False negative > 10%	BLOCKER — add missing checks before use
Risk miscalibration > ±2	Retrain risk scoring with expert labels
Evidence gaps	Connect missing data sources

Evaluation Cadence

Pre-deployment: Full test scenario suite per framework
Quarterly: Re-calibrate risk scoring with auditor review
On regulation update: Add new requirements, re-evaluate coverage
Annual: Full external audit comparison (LLM vs human auditor)

Common Failure Patterns

Symptom	Root Cause	Fix
All checks pass (suspicious)	Evidence source not connected	Verify data source connectivity
Inconsistent risk scores	No grounding in evidence	Add "base assessment on evidence only" to prompt
GDPR check misses consent	Consent management not exposed	Connect consent API as evidence source
Report rejected by auditor	Missing evidence references	Include resource IDs and timestamps in evidence
EU AI Act risk wrong	Wrong risk category assigned	Add risk classification decision tree to prompt
Check takes >1 hour	Full scan on every run	Enable incremental checks (delta since last run)

CI/CD Quality Gates

- name: Compliance Check Accuracy
  run: python evaluation/eval.py --metrics check_accuracy --ci-gate --threshold 0.90
- name: False Negative Gate
  run: python evaluation/eval.py --metrics false_negative --ci-gate --max-rate 0.05
- name: Framework Coverage
  run: python evaluation/eval.py --metrics coverage --ci-gate --threshold 1.0

Last updated: October 13, 2026

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