Compliance & Governance
Navigate AI regulations and build compliant, trustworthy AI systems. Master regulatory frameworks like GDPR, CCPA, EU AI Act, and governance best practices for responsible AI deployment.
Major AI Regulations
GDPR
General Data Protection Regulation - European Union
Focus: Data Protection & Privacy
- • Right to explanation for automated decisions
- • Data minimization in AI training
- • Purpose limitation for AI models
- • Privacy by design requirements
EU AI Act
European Union AI Act - European Union
Focus: AI Systems Regulation
- • Risk-based AI classification
- • Prohibited AI practices
- • High-risk AI requirements
- • Conformity assessments
CCPA/CPRA
California Consumer Privacy Act - California, USA
Focus: Consumer Privacy Rights
- • Opt-out rights for AI profiling
- • Disclosure of AI logic
- • Non-discrimination for opt-out
- • Data deletion requirements
HIPAA
Health Insurance Portability and Accountability Act - United States
Focus: Healthcare Data Protection
- • PHI protection in AI models
- • Minimum necessary standard
- • De-identification requirements
- • Business associate agreements
GDPR Requirements for AI Systems
Article 22: Automated Decision Making
Individuals have the right not to be subject to decisions based solely on automated processing, including profiling.
# GDPR Article 22 Compliance Check
class GDPRComplianceChecker:
def __init__(self):
self.automated_decisions = []
def check_automated_decision(self, decision_process):
"""Check if automated decision requires human oversight"""
# Check for high-impact decisions
high_impact_categories = [
'credit_scoring', 'employment', 'healthcare',
'insurance', 'legal_proceedings'
]
if decision_process.category in high_impact_categories:
return {
'requires_human_review': True,
'explanation_required': True,
'opt_out_available': True,
'reason': 'High-impact automated decision'
}
# Check for profiling
if decision_process.uses_profiling:
return {
'requires_human_review': True,
'explanation_required': True,
'opt_out_available': True,
'reason': 'Decision involves profiling'
}
return {
'requires_human_review': False,
'explanation_required': False,
'opt_out_available': True
}Key Implementation Requirements:
- • Implement meaningful human oversight for high-impact decisions
- • Provide clear explanations of automated decision logic
- • Allow individuals to contest automated decisions
- • Maintain records of automated decision-making processes
AI Governance Framework
Policy Development
- • AI Ethics Policy
- • Risk Management Framework
- • Data Governance Policies
- • Incident Response Procedures
Risk Assessment
- • AI Impact Assessments
- • Bias Detection & Mitigation
- • Privacy Impact Analysis
- • Security Risk Evaluation
Audit & Monitoring
- • Continuous Monitoring
- • Performance Metrics
- • Compliance Reporting
- • External Audits
Implementation Roadmap
Assessment & Planning
Conduct comprehensive assessment of current AI systems and identify compliance gaps.
- • Inventory all AI systems and data flows
- • Map regulatory requirements to business processes
- • Identify high-risk AI applications
- • Create compliance project roadmap
Policy & Governance
Establish comprehensive AI governance framework and policies.
- • Develop AI ethics and governance policies
- • Create risk management procedures
- • Establish review and approval processes
- • Define roles and responsibilities
Technical Implementation
Implement technical controls and monitoring systems for compliance.
- • Deploy privacy-preserving technologies
- • Implement bias detection and mitigation
- • Create audit trails and logging
- • Build consent management systems
Training & Culture
Build organizational awareness and capability for responsible AI.
- • Train teams on AI ethics and compliance
- • Establish AI review boards
- • Create escalation procedures
- • Foster culture of responsible AI
Compliance Tools & Templates
Essential Documentation
Data Processing Impact Assessment (DPIA)
Required for high-risk AI processing under GDPR
AI Risk Assessment Framework
Systematic approach to evaluating AI system risks
AI Model Documentation
Comprehensive documentation of AI system capabilities and limitations
Incident Response Plan
Procedures for handling AI-related security or compliance incidents
Best Practices
Privacy by Design
Embed privacy considerations into AI system design from the ground up, not as an afterthought.
Transparency & Explainability
Provide clear explanations of AI decision-making processes, especially for high-impact applications.
Continuous Monitoring
Implement ongoing monitoring for bias, performance degradation, and compliance drift.
Documentation & Audit Trails
Maintain comprehensive documentation and audit trails for all AI systems and decisions.
Comprehensive Regulatory White Paper
Navigating the Global AI Regulatory Maze: A Strategic Playbook
Download our comprehensive white paper designed specifically for CISOs, AI developers, and technology leaders. This strategic playbook provides in-depth analysis of:
- EU AI Act comprehensive analysis
- NIST AI RMF implementation guide
- GDPR implications for AI systems
- Global regulatory comparison
- Compliance strategy frameworks
- Technical implementation guidance
- Risk management approaches
- Future-proofing strategies
This white paper provides strategic guidance for navigating the complex global AI regulatory landscape, including practical implementation strategies and compliance frameworks. Last updated: January 2025.