Deploy AI Compliance Engine

Deploy AI Compliance Engine — configure regulatory check framework (GDPR, HIPAA, EU AI Act, SOC 2), automated audit trail, risk scoring, evidence collection. Use when: deploy, configure compliance.

Deploy AI Compliance Engine

When to Use

Deploy automated compliance checking for AI systems
Configure regulatory framework checks (GDPR, HIPAA, EU AI Act)
Set up automated audit trail with evidence collection
Implement risk scoring for AI system assessments
Generate compliance reports for regulatory submissions

Prerequisites

Azure CLI authenticated: az account show
Azure OpenAI (gpt-4o for compliance analysis)
Cosmos DB (compliance evidence store)
Azure Storage (audit trail, report storage)
Regulatory framework definitions prepared

Step 1: Deploy Infrastructure

az bicep lint -f infra/main.bicep
az deployment group create -g $RG -f infra/main.bicep -p infra/parameters.json

Step 2: Configure Regulatory Frameworks

Framework	Key Requirements	Check Count
GDPR	Data subject rights, consent, DPO, breach notification	45 checks
HIPAA	PHI protection, access controls, audit logs, encryption	38 checks
EU AI Act	Risk classification, transparency, human oversight, testing	52 checks
SOC 2	Security, availability, processing integrity, confidentiality	64 checks
ISO 27001	ISMS, risk management, controls, continuous improvement	114 checks

Step 3: Configure Compliance Check Pipeline

# Check execution pipeline
async def run_compliance_check(system_id, framework):
    checks = load_framework_checks(framework)
    results = []
    for check in checks:
        evidence = await collect_evidence(system_id, check)
        assessment = await assess_compliance(check, evidence, model="gpt-4o")
        results.append({
            "check_id": check.id,
            "status": assessment.status,  # pass, fail, partial, na
            "evidence": evidence,
            "risk_score": assessment.risk_score,
            "remediation": assessment.remediation if assessment.status != "pass" else None
        })
    return generate_report(results)

Step 4: Configure Risk Scoring

Risk Level	Score	Criteria	Action
Critical	9-10	Regulatory violation likely	Immediate remediation
High	7-8	Major gap, significant exposure	Remediation within 30 days
Medium	4-6	Minor gap, moderate exposure	Remediation within 90 days
Low	1-3	Observation, best practice gap	Track, address in next cycle
None	0	Fully compliant	No action needed

Step 5: Configure Evidence Collection

Evidence Type	Source	Automation
Configuration audit	Azure Resource Graph	Fully automated
Access control review	Azure AD / Entra ID	Fully automated
Data encryption status	Key Vault + Storage	Fully automated
Logging completeness	Log Analytics	Fully automated
Policy documentation	SharePoint / Git	LLM-assisted review
Training records	HR system	Manual + LLM verification

Step 6: Configure Audit Trail

Immutable log: who ran what check, when, what result
Evidence snapshots: system state at time of check
Version tracking: which framework version was used
Retention: 7 years (regulatory requirement)
Tamper detection: hash chain for audit integrity

Step 7: Post-Deployment Verification

Framework checks loading correctly per regulation
Evidence collection from all configured sources
Risk scoring producing consistent assessments
Audit trail recording all compliance activities
Reports generating in required format
LLM compliance analysis providing actionable remediation
Dashboard showing compliance posture per framework

Troubleshooting

Issue	Cause	Fix
Check returns N/A incorrectly	Evidence source not connected	Verify data source connectivity
Risk score inconsistent	LLM hallucinating assessment	Add evidence-grounding in prompt
Audit trail incomplete	Missing event source	Add all check executions to log
Report format wrong	Template mismatch	Update report template per regulation
Stale evidence	Check schedule too infrequent	Increase frequency for critical checks
False compliance (pass when should fail)	Missing check for requirement	Audit framework coverage