Leveraging Privacy Frameworks for AI Compliance

Navigating AI compliance doesn’t have to mean starting from scratch. By adapting privacy practices - such as data mapping, risk classification, and data governance - organizations can tackle the EU AI Act’s complex requirements using resources and processes that are already in place. Utilizing these proven privacy risk management frameworks, as well as internal and external privacy expertise, enables the rapid and cost-effective development and implementation of compliant and responsible AI governance programs.

Global Collaborators Draft First Guidelines for EU AI Act

In November 2024, the first draft of the General-Purpose AI Code of Practice established comprehensive guidelines specifically for GPAI providers in preparation for their August 2025 compliance deadline under the EU AI Act. Created through collaboration with over 400 global organizations, the Code focuses on critical areas including transparency, copyright rules, risk identification, and risk mitigation frameworks that will govern how general-purpose AI models are developed and deployed in Europe. The Code's 22 detailed measures address crucial concerns from technical documentation to systemic risk assessment.

What You Need to Know About the EU AI Act

The EU AI Act introduces a groundbreaking regulatory framework for AI systems, classifying them into risk categories and imposing compliance requirements accordingly. High-risk systems, such as those used in healthcare and law enforcement, must undergo stringent risk management, bias reduction, and cybersecurity measures, with significant penalties for non-compliance. Transparency obligations and robust data governance are central to the Act, ensuring that AI systems are deployed ethically, responsibly, and in alignment with EU standards.

Navigating the Rapidly Evolving U.S. AI Laws

Recent U.S. state regulations demonstrate a rapid evolution in AI-specific legislation, with notable examples including California's Artificial Intelligence Transparency Act (SB942), Colorado's algorithmic discrimination law (SB24-205), and New York City's Local Law 144 requiring bias audits for AI employment tools. While some AI protections are embedded within broader privacy laws, these dedicated AI regulations, along with Illinois' AI Video Interview Act, create a complex compliance landscape focusing on transparency, algorithmic bias prevention, and informed consent for businesses deploying AI technologies across state lines.

The Struggle to Regulate AI in U.S. State Legislatures

The landscape of U.S. AI regulation in 2024 was marked by several notable failed legislative attempts, including California's Safe and Secure Innovation for Frontier Artificial Intelligence Systems Act (SB 1047), Colorado's AI real estate regulation bill (HB 24-1057), and Massachusetts' generative AI transparency bill (SB 31). These unsuccessful bills, which aimed to address issues ranging from high-risk AI model controls to algorithmic bias and AI-generated content disclosure, highlight the ongoing challenges legislators face in balancing innovation with regulation, defining enforcement mechanisms, and managing compliance costs for AI governance.

Adapting AI Act Compliance Frameworks Across Global Regions

The EU AI Act provides a comprehensive foundation for AI governance that companies can leverage when expanding into other markets, though significant regional adaptations are required. While the EU emphasizes a structured risk-based approach with strict documentation requirements, other regions present varying priorities: China focuses on content control and state alignment, Japan promotes voluntary ethical guidelines, and the U.S. emphasizes innovation with lighter regulatory touch. Companies that establish strong EU compliance frameworks will find themselves well-positioned to adapt to emerging regulations in other regions.