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. […]
As organizations increase their adoption of AI, governance leaders are looking to put in place policies that ensure their AI deployment aligns with their organization’s principles, complies with regulatory standards, and mitigates potential risks. But where to start in developing your policies can oftentimes be overwhelming.
While AI has been used in enterprise and consumer products for decades, only large tech organizations with sufficient resources were able to implement it at scale. In the past few years, advances in the quality and accessibility of ML systems have led to a rapid proliferation of AI tools in everyday life. The accessibility of these tools means there is a massive need for good AI Governance both by AI providers (e.g. OpenAI), as well as the organizations implementing and deploying AI systems into their own products.
