AI Governance

Blog

Everything You Need to Know About the New California Consumer Privacy Act’s Automated Decision-Making Regulations

August 13, 2025

On July 24, 2025, the California Privacy Protection Agency (CPPA) voted unanimously to finalize rules under the California Consumer Privacy Act (CCPA), as amended by the California Privacy Rights Act. These new rules introduce significant obligations for businesses subject to the CCPA. They impose requirements on the use of automated decision-making technologies (ADMT) and mandate cybersecurity audits and risk assessments. The California Office of Administrative Law must review and approve these rules by around September 5, 2025. Once approved, they will become effective on a phased basis between 2027 and 2030.

Blog

3 Types of Risk & Impact Assessments, And When to Use Them

August 8, 2025

A common requirement in many AI standards, regulations, and best practices is to conduct risk and impact assessments to understand the potential ways AI could malfunction or be misused. By understanding the risks, organizations can prioritize and implement appropriate technical, organizational, and legal mitigation measures. While there are standards for these assessments, such as the […]

Blog, Insights

What the Trump Administration’s AI Action Plan Means for Enterprises

August 5, 2025

The Trump Administration released “Winning the AI Race: America’s AI Action Plan” (AI Action Plan) on July 23, 2025. The AI Action plan was published in accordance with the January 2025 Removing Barriers to American Leadership in AI Executive Order. The AI Action Plan proposes approximately 90 policy recommendations within three thematic pillars: Pillar I addresses […]

Blog, Insights, Research

FAccT Finding: AI Takeaways from ACM FAccT 2025

July 15, 2025

Anastassia Kornilova is the Director of Machine Learning at Trustible. Anastassia translates research into actionable insights and uses AI to accelerate compliance with regulations. Her notable projects have involved creating the Trustible Model Ratings and AI Policy Analyzer. Previously, she has worked at Snorkel AI developing large-scale machine learning systems, and at FiscalNote developing NLP […]

Blog

Trustible Becomes Official Implementation Partner for the Databricks AI Governance Framework (DAGF)

July 8, 2025

Despite the explosive growth of AI, most enterprises remain unprepared to manage the very real risks that come with its adoption. While the opportunities are vast—from smarter products to more efficient operations—the path to realizing AI’s full potential is fraught with challenges around performance, cybersecurity, privacy, ethics, and legal compliance. Without a strong AI governance […]

Corporate News

Trustible Releases Updates and Improvements to Model Transparency Ratings

April 8, 2025

Updates include new models from OpenAI, Meta, Google, Anthropic, DeepSeek, and more; additional data evaluations; new regulatory and policy analysis; and more.  Generative AI models are transforming industries, driving innovation, and reshaping how organizations operate. Despite their wide-scale adoption, these models introduce risks to organizations given the limited transparency into their data, training, and operations. […]

Insights, Whitepaper

AI Governance Triggers: When to Act and Why It Matters

March 25, 2025

The rapid evolution of artificial intelligence—with continuous advancements in models, policies, and regulations—presents a growing challenge for AI governance teams. Organizations often struggle to determine when governance intervention is necessary in order to balance risk oversight without imposing excessive compliance burdens. This eBook introduces the concept of “AI Governance Triggers” to provide clarity on the specific AI model events that should prompt governance activities.

Blog, Insights

Understanding the Data in AI

February 11, 2025

Data governance is a key component of responsible AI governance, and it features prominently in every emerging AI regulations and standards. However, “data” is not a monolithic concept within AI systems. From the massive datasets collected for training large language models (LLMs), to user feedback loops that refine and improve outputs, multiple “data streams” flow through any modern AI application.