Standard frameworks — OWASP Top 10, NIST CSF — were designed for conventional software. They don't address what happens when you build ML systems: training data poisoning, model inversion, membership inference, embedding extraction, feature store backdoors. Generic AI can't produce this framework either — it requires simultaneously understanding data engineering architecture, ML-specific security controls, and regulatory compliance for AI workloads.

GDPR, SOC2, and the EU AI Act are actively catching up. EU AI Act enforcement for high-risk AI systems begins August 2026 — if your pipeline feeds credit scoring, employment decisions, or clinical support, that deadline is close.

17-page framework document (PDF + DOCX)

Section 01: 4-tier ML data classification taxonomy with model inheritance rules

Section 02: 6-stage pipeline security architecture with per-stage threat model and controls

Section 03: 6 ML-specific attack surfaces — MITRE ATLAS mapped, detection signals, mitigations

Section 04: RBAC/ABAC access control matrix at pipeline stage level + CI/CD secrets standards

Section 05: Encryption standards per tier + differential privacy guidance and epsilon budgets

Section 06: Feature store security — 6 risks with controls and verification methods

Section 07: Audit logging schema (12 fields) + retention + tamper-evidence requirements

Section 08: Compliance mapping — GDPR · EU AI Act · SOC2 · NIST AI RMF · CCPA · HIPAA-adjacent

Section 09: 30/60/90-day implementation roadmap — baseline to audit readiness

Annual update included for registered customers