Implement AI Governance to Meet EU AI Act High-Risk Rules
EU AI Act classifies AI as high-risk for hiring, credit, personalization—requiring risk assessments, logging, human oversight by Aug 2026 or face €35M/7% revenue fines. Build accountability, transparency, data controls now.
Distinguish Governance from Ethics and MLOps for Operational Compliance
AI governance provides the policies, processes, controls, and accountability linking AI ethics (aspirational values like fairness) to MLOps (technical model lifecycle) and regulatory demands. It assigns RACI ownership for failures, sets risk thresholds, and generates audit-ready evidence. Progress through stages: ad-hoc (siloed projects) to scaled (automated guardrails, real-time monitoring). Core pillars include accountability (board oversight; only 15% boards track AI metrics), transparency (internal technical docs on architecture/data/testing per EU AI Act Article 11; external explainability for users per GDPR Article 22), risk management (continuous assessments for drift/bias/IP risks), data governance (bias detection, provenance, completeness to ensure representative datasets), and human oversight (design for intervention/override per Article 14). 99% of organizations report $4.4M average AI risk losses, mainly non-compliance (57%) and bias (53%).
Navigate Risk-Tiered Regulations with Unified Assessments
EU AI Act (effective Aug 2024) tiers AI: unacceptable risk banned Feb 2025 (e.g., social scoring, emotion recognition in workplaces); high-risk from Aug 2026 (Annex III: employment screening, credit, biometrics; Annex I: medical/vehicles)—extraterritorial like GDPR. High-risk demands risk management systems (iterative, post-market surveillance), data quality (bias stats across demographics, provenance docs), technical documentation (logic, testing metrics, oversight mechanisms), tamper-proof logging (events for malfunctions), and incident reporting (2-15 days for serious harm). Combine with GDPR Article 22 (human intervention for automated decisions; explain logic) and Article 35 DPIAs via unified FRIA/DPIA addressing rights and data risks. US uses NIST AI RMF (Govern/Map/Measure/Manage) and FTC enforcement on deception/bias. Global: OECD Principles, G7 Code converge on shared standards.
Secure High-Risk and Generative AI with Specific Controls
High-risk providers/deployers implement continuous risk mitigation for misuse/drift, bias checks (proxy discrimination), and legal basis for sensitive data to fix biases despite GDPR minimization. GPAI/LLMs (Aug 2025): publish training summaries (web scrapes/code repos), copyright policies; systemic risk models (>10^25 FLOPs) add red-teaming, 72-hour incident reports, cyber protections—use GPAI Code of Practice as safe harbor. Embed in lifecycle: design (risk classification), development (controls), deployment (monitoring), decommissioning. Organizational model: cross-functional committees; roles—legal (regs), privacy (DPIAs), IT (logging), business (value alignment), board (AI posture: Pioneer/Transformer/Pragmatic). Integrate into GRC via AI stacks linking monitoring to dashboards.