AI Compliance Checklist 2026: Complete Implementation Guide

This comprehensive checklist guides organizations through AI compliance requirements across Southeast Asia. Use this as your roadmap for implementing AI systems that meet regulatory expectations in Singapore, Malaysia, Indonesia, and Hong Kong.

How to Use This Checklist

Before You Begin:

1. Identify your AI system and its purpose
2. Determine which countries' regulations apply
3. Classify your AI system by risk level (high/medium/low)
4. Assign responsibility for each checklist item
5. Set target completion dates

Risk Classification:

• High-Risk: AI making significant decisions about individuals (credit, hiring, insurance, medical diagnosis)
• Medium-Risk: AI affecting individuals but with human oversight (customer service chatbots, fraud detection)
• Low-Risk: AI with minimal impact on individuals (process automation, non-personalized recommendations)

Phase 1: Planning and Assessment

AI System Documentation

☐ Define AI system purpose and intended use

• Clear description of what the AI does
• Intended users and use cases
• Expected outcomes and decisions
• Limitations and constraints

☐ Identify stakeholders

• Internal teams (legal, compliance, IT, business)
• External parties (vendors, service providers)
• Affected individuals (customers, employees)
• Regulatory authorities

☐ Determine applicable regulations

• ☐ Singapore: PDPA, Model AI Governance Framework, AI Verify
• ☐ Malaysia: PDPA 2010, Bank Negara guidance (if financial)
• ☐ Indonesia: UU PDP, sector-specific regulations
• ☐ Hong Kong: PDPO, AI Model Framework
• ☐ Sector-specific: Financial (MAS, BNM, OJK), Healthcare (HSA, MDA, MOH)

☐ Classify AI risk level

• High-risk indicators: automated decisions affecting rights, sensitive data processing, systematic monitoring
• Medium-risk indicators: personal data processing, moderate impact decisions
• Low-risk indicators: non-personal data, minimal individual impact
• Document risk classification rationale

Data Inventory and Mapping

☐ Identify all data sources

• Internal data (customer records, employee data, transaction logs)
• External data (third-party datasets, public data, scraped data)
• Real-time data (sensor feeds, API data, user interactions)
• Document data provenance and reliability

☐ Categorize data types

• Personal data vs. non-personal data
• Sensitive data (health, biometric, financial, children's data)
• Demographic data (age, gender, ethnicity, location)
• Behavioral data (purchases, browsing, interactions)

☐ Map data flows

• Collection points (web forms, APIs, sensors, scraping)
• Storage locations (cloud, on-premise, third-party)
• Processing activities (training, inference, analytics)
• Disclosure/sharing (vendors, partners, cross-border)
• Retention and deletion processes

☐ Identify cross-border data transfers

• Countries receiving personal data
• Purpose of each transfer
• Data protection standards in receiving countries
• Safeguards in place (contracts, consent, adequacy)

Phase 2: Legal Basis and Consent

Establish Legal Basis (All Countries)

☐ Determine legal basis for personal data processing

• ☐ Consent (most common for consumer AI)
• ☐ Contractual necessity (AI fulfilling service contract)
• ☐ Legal obligation (regulatory compliance AI)
• ☐ Legitimate interest (operational efficiency, fraud prevention)
• ☐ Vital interest (emergency/life-saving AI)
• Document legal basis assessment for each processing activity

Consent Implementation (If Required)

☐ Design consent mechanisms

• ☐ Specific: Clearly identify AI application and purpose
• ☐ Informed: Explain AI processing in plain language
• ☐ Separate: Unbundle from other consents
• ☐ Freely given: Ensure genuine choice without detriment
• ☐ Documented: Maintain consent records with timestamps
• ☐ Withdrawable: Easy mechanism to withdraw consent

☐ Create consent notices

• What personal data is collected
• How AI will process the data
• What decisions or outcomes AI will produce
• How long data will be retained
• How to withdraw consent
• Contact for questions/complaints

☐ Implement consent tracking

• Database recording: who, what, when, how
• Consent version control
• Withdrawal tracking and processing
• Audit trail for compliance demonstration

Purpose Limitation

☐ Define specific AI purposes

• Precise description of AI use cases
• Avoid generic purposes ("AI development," "analytics")
• Document intended vs. prohibited uses

☐ Assess purpose compatibility

• For existing data: Is AI use compatible with original purpose?
• Document compatibility assessment
• Obtain fresh consent if incompatible

Phase 3: Data Protection Impact Assessment

DPIA Requirement (High-Risk AI)

☐ Determine if DPIA required

• Singapore: Best practice for high-risk AI
• Malaysia: Recommended for high-risk
• Indonesia: Mandatory for high-risk (Article 35)
• Hong Kong: Recommended for high-risk

DPIA Components

☐ Description of AI processing

• Systematic description of processing operations
• AI system architecture and techniques
• Data types, sources, and flows
• Purposes and intended outcomes
• Third parties involved
• Retention periods

☐ Necessity and proportionality assessment

• Why AI is necessary for stated purpose
• Less intrusive alternatives considered
• Data minimization applied
• Benefits proportional to privacy intrusion

☐ Risk identification

• ☐ Discrimination: AI perpetuating biases
• ☐ Privacy intrusion: Inappropriate inferences
• ☐ Autonomy: Over-reliance on AI decisions
• ☐ Security: Data breaches, unauthorized access
• ☐ Function creep: Purpose expansion
• ☐ Transparency: Lack of explainability
• ☐ Accuracy: Errors harming individuals

☐ Risk mitigation measures

• Technical: Bias testing, encryption, access controls, differential privacy
• Organizational: Human oversight, policies, training, audits
• Transparency: Notices, explanations, appeal mechanisms
• Governance: Ethics committee, accountability structures

☐ Stakeholder consultation

• Data Protection Officer review
• Affected individuals or representative groups
• Internal stakeholders (legal, compliance, business)
• Document consultation outcomes

☐ DPIA approval and review

• Obtain DPO or senior management approval
• Schedule periodic reviews (annually or when AI changes)
• Update DPIA when risks or mitigation change

Phase 4: Data Quality and Accuracy

Training Data Quality

☐ Pre-training validation

• Data quality audits identifying errors, outliers, anomalies
• Verification of data source reliability and provenance
• Removal or correction of obviously inaccurate data
• Handling of missing or incomplete data
• Documentation of known data quality limitations

☐ Bias identification

• Audit training data for historical biases
• Assess representation across demographic groups
• Identify potential sources of discriminatory patterns
• Document bias analysis findings

☐ Bias mitigation

• Diverse, representative training datasets
• Rebalancing or reweighting techniques
• Fairness-aware algorithms
• Regular fairness testing
• Validation across subgroups

☐ Accuracy maintenance

• Regular data refreshes to avoid stale data
• Monitoring for data drift over time
• Processes for individuals to correct their data
• Model retraining when underlying data changes significantly

Phase 5: Security and Confidentiality

Data Security

☐ Encryption

• Personal data encrypted at rest (AES-256 or equivalent)
• Data encrypted in transit (TLS 1.3+)
• Encryption key management (secure storage, rotation)

☐ Access controls

• Role-based access control (RBAC)
• Principle of least privilege
• Multi-factor authentication for AI systems
• Logging and monitoring of data access
• Regular access reviews and revocations

☐ Network security

• Firewalls protecting AI infrastructure
• Network segmentation (isolate AI systems)
• Intrusion detection/prevention systems
• Regular security testing and vulnerability scans

☐ AI-specific threat protection

• ☐ Model inversion: Differential privacy, query limiting, output perturbation
• ☐ Adversarial attacks: Input validation, adversarial training, confidence thresholds
• ☐ Data poisoning: Input validation, anomaly detection, secure data sourcing
• ☐ Model theft: API authentication, rate limiting, watermarking

Third-Party Security

☐ AI vendor due diligence

• Security certifications (ISO 27001, SOC 2)
• Security policies and practices assessment
• Incident response capabilities
• Data protection compliance

☐ Data processing agreements

• Processing only on your instructions
• Confidentiality obligations
• Security requirements (encryption, access controls)
• Subprocessor restrictions
• Breach notification obligations
• Audit rights
• Data return/deletion upon termination

☐ Regular vendor monitoring

• Periodic security assessments
• Compliance audits
• Performance reviews
• Contract compliance verification

Incident Response

☐ AI security incident response plan

• Incident detection and classification
• Containment and remediation procedures
• Breach notification processes (regulatory, individuals)
• Post-incident review and improvement
• Regular incident response testing

Phase 6: Retention and Deletion

Data Retention

☐ Define purpose-specific retention periods

• Align retention with specific AI purposes
• Document retention rationale
• Consider regulatory requirements (employment law, financial records)

Example Retention Periods:

• Recommendation AI training data: 12-24 months
• Chatbot conversation logs: 6-12 months
• Hiring AI applicant data: 6-12 months post-decision
• Medical AI patient data: Per health record regulations
• Video surveillance: 30-90 days (unless incident)

☐ Implement automated deletion

• Technical processes deleting data when retention expires
• Removal from training datasets and backups
• Assessment of model retraining needs post-deletion
• Deletion logging and documentation

☐ Anonymization for long-term use

• Robust anonymization techniques (irreversible de-identification)
• Regular audits confirming re-identification impossible
• Documentation of anonymization methodology
• Note: Truly anonymous data outside data protection scope

Phase 7: Transparency and Explainability

Privacy Policies and Notices

☐ Update privacy policies

• ☐ AI applications processing personal data
• ☐ Types of personal data used by AI
• ☐ How AI processes data (training, inference)
• ☐ What decisions or outcomes AI produces
• ☐ Automated decision-making details
• ☐ Individual rights (access, correction, objection)
• ☐ Data retention periods for AI
• ☐ Third-party AI service providers
• ☐ Cross-border data transfers
• ☐ How to exercise rights and contact information

☐ Collection notices

• Clear notice at point of data collection
• Specific information about AI use
• Plain language, understandable to average person

Automated Decision-Making Transparency

☐ Inform individuals of AI decisions (Required: Indonesia Article 40; Best practice: others)

• That automated decision-making is used
• What data feeds the AI decision
• The logic or criteria used (general explanation)
• Significance and consequences of decision
• How to challenge or request human review

☐ Implement explainability mechanisms

• High-level explanations for all affected individuals
• Technical explanations available upon request
• Explainable AI tools (SHAP, LIME) for complex models
• Documentation of decision factors and logic

Example Decision Explanation:

Your loan application was assessed by our AI credit model.

Key factors:
- Annual income: $60,000 (meets minimum)
- Debt-to-income ratio: 48% (above preferred 40%)
- Credit history: 18 months (prefer 24+ months)
- Recent inquiries: 3 (indicates credit seeking)

Result: Elevated credit risk detected.

You may request human review by calling [number] or provide
additional information supporting your application.

Phase 8: Individual Rights

Access Rights Implementation

☐ Build access request handling capability

• Web forms or email for access requests
• Identity verification procedures
• Data retrieval from AI systems and training data
• Plain-language processing descriptions
• Respond within regulatory timeframes:
  • Singapore PDPA: 30 days
  • Malaysia PDPA: 21 days
  • Indonesia UU PDP: As specified by regulation
  • Hong Kong PDPO: 40 days

☐ Define what to disclose

• Personal data in training datasets
• Personal data processed for AI inference
• AI systems that processed their data
• Purposes of processing
• Third parties receiving data
• Predictions/decisions made by AI

☐ What NOT to disclose

• Proprietary AI algorithms or model architecture
• Trade secrets
• Other individuals' personal data

Correction Rights

☐ Correction request process

• Receive and verify correction requests
• Investigate data accuracy
• Correct inaccurate data in source systems and training datasets
• Assess whether AI models need retraining
• Notify individual of actions taken
• Inform third parties who received incorrect data (if required)

Deletion/Erasure Rights

☐ Deletion request process

• Verify deletion right applies (consent withdrawn, purpose fulfilled, unlawful processing)
• Identify all data locations (databases, training data, backups, third parties)
• Delete from active systems
• Remove from training datasets
• Assess model retraining necessity
• Instruct third parties to delete
• Confirm to individual within timeframes
• Document deletion for audit trail

Objection Rights

☐ Objection handling (Indonesia Article 40, others best practice)

• For automated decisions: Human review process
• For legitimate interest processing: Cease unless compelling grounds
• For marketing: Immediate cessation
• Document objections and responses

Phase 9: Human Oversight and Governance

Human-in-the-Loop

☐ Define human oversight level

• Human-in-the-loop: Human makes final decision based on AI recommendation (high-risk AI)
• Human-on-the-loop: Human monitors AI and intervenes when necessary (medium-risk)
• Human-in-command: Human sets parameters and oversees AI operations (low-risk)

☐ Implement oversight mechanisms

• Qualified personnel reviewing AI decisions
• Authority to override AI recommendations
• Escalation procedures for edge cases
• Documentation of human review and decisions

AI Governance Structure

☐ Designate accountability

• Executive sponsor for AI governance
• AI ethics committee or governance board
• Data Protection Officer (if required/appointed)
• Compliance, legal, technical representatives

☐ Develop AI governance policy

• AI development and deployment standards
• Risk assessment requirements
• Documentation expectations
• Approval processes for new AI systems
• Ongoing monitoring and auditing
• Incident response procedures

☐ AI ethics principles

• Fairness and non-discrimination
• Transparency and explainability
• Privacy and data protection
• Human agency and oversight
• Accountability and responsibility
• Safety and security

Phase 10: Cross-Border Transfers

Transfer Safeguards

☐ Identify all cross-border transfers

• Countries receiving personal data
• Purposes of each transfer
• Data categories transferred
• Third parties receiving data

☐ Implement transfer mechanisms

• ☐ Adequacy: Transfer to countries with adequacy decision (currently limited in SEA)
• ☐ Standard Contractual Clauses (SCCs): Use approved clauses with overseas recipients
• ☐ Binding Corporate Rules (BCRs): For multinational groups (if approved)
• ☐ Consent: Obtain explicit consent for cross-border transfer
• ☐ Derogations: Necessary for contract performance, legal claims, etc.

☐ Document transfers

• Transfer inventory (what, where, why, safeguards)
• Copies of SCCs or other safeguards
• Consent records (if applicable)
• Transfer impact assessments

☐ Consider data localization

• For sensitive or high-risk AI
• When transfer safeguards difficult
• Use local cloud regions or on-premise deployment

Phase 11: Testing and Validation

Pre-Deployment Testing

☐ Functional testing

• AI achieves intended purpose
• Accuracy meets performance benchmarks
• Edge cases handled appropriately

☐ Fairness and bias testing

• Performance across demographic subgroups
• Disparate impact analysis
• Bias metrics appropriate to context
• Comparison to fairness benchmarks

☐ Security testing

• Penetration testing
• Vulnerability scanning
• AI-specific threat testing (model inversion, adversarial, poisoning)

☐ User acceptance testing

• Stakeholder feedback
• Usability and explainability assessment
• Real-world scenario testing

Ongoing Monitoring

☐ Performance monitoring

• Accuracy tracking over time
• Model drift detection
• Performance degradation alerts

☐ Fairness monitoring

• Continuous bias testing
• Disparate impact tracking
• Fairness metric dashboards

☐ Security monitoring

• Anomalous access patterns
• Unusual query behavior
• Security incident detection

☐ Compliance monitoring

• Data protection metrics (consent rates, rights requests, breaches)
• Policy adherence
• Training completion
• Audit findings tracking

Phase 12: Documentation and Record-Keeping

Required Documentation

☐ AI system documentation

• System design and architecture
• Intended use and limitations
• Training data sources and characteristics
• Model development methodology
• Validation and testing results
• Known risks and mitigation measures

☐ Data protection records

• Legal basis assessments
• Consent records
• DPIAs for high-risk AI
• Data processing agreements with vendors
• Cross-border transfer documentation
• Individual rights requests and responses
• Breach logs and responses

☐ Governance records

• AI governance policies
• Risk assessments
• Approval records for AI deployments
• Audit reports
• Training completion records
• Incident reports and lessons learned

☐ Change management logs

• AI model updates and versions
• Data changes (sources, quality improvements)
• Policy and procedure changes
• Regulatory change assessments

Phase 13: Training and Awareness

Staff Training

☐ AI developers and data scientists

• Data protection principles (PDPA, UU PDP, PDPO)
• Privacy-by-design in AI development
• Bias detection and mitigation
• Security best practices for AI
• Documentation requirements

☐ Legal and compliance teams

• AI technologies and applications
• AI-specific regulatory requirements
• Risk assessment methodologies
• Emerging AI regulations and guidance

☐ Business users and stakeholders

• Appropriate AI use and limitations
• Data protection obligations
• Individual rights and how to respond
• Escalation procedures for issues

☐ Leadership and executives

• AI governance and accountability
• Strategic compliance considerations
• Regulatory trends and developments
• Risk oversight responsibilities

Awareness Programs

☐ Regular communications

• AI compliance updates and reminders
• Regulatory change notifications
• Best practice sharing
• Success stories and lessons learned

☐ Compliance culture

• Recognize and reward compliance excellence
• Open channels for reporting concerns
• No retaliation for good-faith compliance questions
• Leadership role modeling

Phase 14: Continuous Improvement

Regular Reviews

☐ Quarterly reviews

• AI system performance and compliance metrics
• Incidents, issues, and resolutions
• Regulatory developments affecting AI
• Stakeholder feedback

☐ Annual audits

• Comprehensive AI compliance audit
• DPIA updates for high-risk systems
• Policy and procedure effectiveness
• Training effectiveness assessment

☐ Post-incident reviews

• Root cause analysis
• Lessons learned documentation
• Control improvements
• Communication to stakeholders

Regulatory Engagement

☐ Monitor regulatory developments

• Singapore: PDPC guidance, AI Verify updates
• Malaysia: PDPC Commissioner guidance, MDEC frameworks
• Indonesia: Data Protection Authority regulations
• Hong Kong: PCPD guidance, legislative amendments
• Industry-specific regulators (MAS, BNM, OJK, HSA, MDA)

☐ Participate in consultations

• Respond to regulatory consultations
• Engage with industry associations
• Contribute to best practice development
• Build regulator relationships

Country-Specific Compliance Checklist

Singapore Additional Requirements

☐ AI Verify testing (voluntary but recommended)

• Test AI systems using AI Verify toolkit
• Generate objective performance metrics
• Document AI Verify results
• Address issues identified

☐ Model AI Governance Framework alignment

• Internal governance structures
• Operations management for AI risks
• Human oversight mechanisms
• Continuous improvement processes

☐ Sector-specific (if applicable)

• ☐ Financial: MAS FEAT Principles compliance
• ☐ Healthcare: HSA medical device registration

Malaysia Additional Requirements

☐ Sector-specific (if applicable)

• ☐ Financial: BNM RMiT policy compliance (AI governance, model risk management)
• ☐ Healthcare: MDA medical device registration

Indonesia Additional Requirements

☐ Mandatory DPIA for high-risk AI (Article 35)

• Complete before deployment
• Update when AI changes significantly
• Document consultation and approval

☐ Article 40 automated decision-making rights

• Inform individuals of automated processing
• Implement human intervention mechanisms
• Enable individuals to express views
• Provide explanations of decisions

☐ Sector-specific (if applicable)

• ☐ Financial: OJK, BI requirements
• ☐ E-commerce: PSE registration with Kominfo

Hong Kong Additional Requirements

☐ AI Model Framework adoption (recommended)

• AI strategy and governance
• Risk assessment and human oversight
• Model customization and implementation
• Stakeholder communication

☐ Prepare for 2026 amendments

• Breach notification capability (PCPD and individuals)
• Data processor contract updates
• Enhanced compliance monitoring

Implementation Timeline Recommendation

Months 1-2: Assessment

• Complete Phases 1 (Planning) and initial Phase 2 (Legal Basis)

Months 3-4: Core Compliance

• Complete Phases 2-4 (Consent, DPIA, Data Quality)

Months 5-6: Security and Retention

• Complete Phases 5-6 (Security, Retention)

Months 7-8: Transparency and Rights

• Complete Phases 7-8 (Transparency, Individual Rights)

Months 9-10: Governance and Transfers

• Complete Phases 9-10 (Governance, Cross-Border)

Months 11-12: Testing and Documentation

• Complete Phases 11-12 (Testing, Documentation)

Ongoing: Training and Improvement

• Phases 13-14 (Training, Continuous Improvement)

Conclusion

AI compliance is not a one-time project but an ongoing commitment. Use this checklist to:

1. Assess your current state
2. Prioritize gaps based on risk
3. Implement controls systematically
4. Monitor compliance continuously
5. Improve based on lessons learned

Remember:

• Start with high-risk AI systems
• Document everything
• Involve cross-functional teams
• Engage with regulators proactively
• Build compliance into AI development lifecycle

By following this checklist, organizations can deploy AI systems that meet regulatory requirements, respect individual rights, and build stakeholder trust across Southeast Asia.