Data Anonymization & Synthetic Data for AI Model Training
Executive Summary
Organizations need high‑quality data to train AI/ML models—without exposing regulated information. Business Compass LLC provides end‑to‑end services to de‑identify, tokenize, and synthesize data so teams can innovate responsibly while aligning with HIPAA, PCI DSS, GDPR/CCPA, GLBA, and SOC 2 expectations. As an AWS Advanced Consulting Partner, we design secure pipelines, implement policy‑as‑code, and measure residual re‑identification risk so your models learn from safe, useful data.
Sector
Who This Is For
- Healthcare (providers, payers, life sciences) handling PHI/PIHI
- Financial services & fintech handling PAN/PCI, PII, and transaction data
- Public sector & education (FERPA considerations)
- Retail & e‑commerce with loyalty, clickstream, and profile data
- ISVs & data providers that need privacy‑preserving data sharing
Cover
Compliance Alignment (What We Cover)
1
HIPAA
Safe Harbor & Expert Determination pathways; PHI redaction; BAA support; minimum necessary; audit trails
2
PCI DSS
PAN tokenization, truncation, and format‑preserving controls; key management; scope reduction & segmentation
3
GDPR/CCPA/CPRA
Pseudonymization, data minimization, purpose limitation, DSAR enablement, and privacy impact assessments
4
GLBA/SOC 2
Data classification, confidentiality controls, and evidence collection
We build controls to support attestations and audits; final compliance responsibility remains with the customer.
Deliver
What We Deliver
1
Privacy Risk Assessment & Data Mapping
- System‑of‑record inventory, lineage, and risk scoring
- PII/PHI/PCI detection coverage analysis; sampling plan
2
De‑Identification Playbooks
- HIPAA Safe Harbor: definitive identifier removal
- Expert Determination: k‑anonymity, l‑diversity, t‑closeness, differential privacy—all tuned to your re‑id risk thresholds
3
PCI Scope Reduction
- Tokenization (vaulted or vaultless), encryption, and PAN masking
- Network & data flow segmentation to reduce PCI footprint
4
Synthetic Data Generation for AI/ML
- Model‑driven tabular, time‑series, and event‑stream synthesis
- Utility benchmarking vs. source data (predictive fidelity, correlation, and drift checks)
5
PII/PHI Redaction Pipelines
- NLP‑based entity detection for names, addresses, IDs, medical terms
- Configurable redaction, generalization, and perturbation strategies
6
Data Utility Preservation
- Statistical utility tests; downstream task performance checks
- Smart binning, micro‑aggregation, and noise calibration
7
Privacy Risk Testing
- Re‑identification simulations & membership inference checks
- Privacy budget management (ε, δ) for DP workflows
8
Security & Governance
- Encryption in transit/at rest; KMS/HSM key rotation
- Lake governance, fine‑grained access, and immutable audit logging
9
MLOps Integration
- Privacy‑aware feature stores, CI/CD for data pipelines, and policy‑as‑code guardrails
