PCI DSS Compliance for AI/ML Companies | Complete Guide
Establish Your Audit Baseline
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Key Compliance Highlights
Cardholder data environment (CDE) scope reduction
Network segmentation and access control implementation
Encryption and key management best practices
Vulnerability management and penetration testing
Self-Assessment Questionnaire (SAQ) selection guidance
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Establish Your Audit Baseline
Get your readiness score, identify critical gaps, and unblock enterprise deal velocity in under 2 minutes.
Frequently Asked Questions
Does AI processing of payment data require special controls?
AI/ML on cardholder data must occur within the Cardholder Data Environment (CDE). Apply access controls, encryption, and audit logging. Consider whether tokenized data meets your analytical needs.
Can we use card data for fraud detection models?
Yes, fraud detection is a valid use case. Ensure models are trained and run within PCI scope. Document data flows and implement appropriate controls for model training environments.
What about payment pattern analysis?
Analysis using cardholder data must be in scope. Consider aggregated/anonymized data for pattern analysis to reduce scope while maintaining analytical value.
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Disclaimer: Compliance costs and timelines are estimates based on market benchmarks (AICPA fee surveys, vendor pricing indices 2025). Actual auditor fees and internal effort will vary based on your specific control environment, system complexity, and auditor selection. Consult with a qualified CPA for a formal statement of work.