Foundation Models

Foundation models are large AI systems trained on broad datasets so they can be adapted to many different tasks, such as writing, coding, image generation, or data analysis. Examples include the general-purpose models offered by OpenAI, Anthropic, Google, and Meta. For governance teams, they matter because a single model's flaws, biases, or licensing terms can flow into many downstream products, which makes vendor due diligence, usage policies, and documentation of where these models sit in company systems essential.

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EnforcementUK2026-11-10

ICO Finds Training Data Transparency Gaps at Ten Foundation Model Developers

The UK Information Commissioner's Office completed a two-year supervision programme covering ten major foundation model developers. It found that all ten made or committed to make data protection improvements, with training data transparency and personal information handling identified as the most persistent concerns. The findings signal that regulators now treat the full AI supply chain as within data protection scope.

ICO foundation models GDPR training data data protection UK vendor due diligence

Insight2026-07-15

Thinking Machines Lab Releases Inkling, a 975B Open-Weight Model With Self-Fine-Tuning Capability, Creating New Intake and Agentic Governance Obligations

Thinking Machines Lab has released Inkling, a 975B-parameter open-weight Mixture-of-Experts model pretrained on 45 trillion tokens of multimodal data. Full weights available for public download and fine-tuning. The model supports a 1M-token context window and is paired with a hosted fine-tuning platform called Tinker. A demonstrated self-fine-tuning capability, in which the model wrote and executed its own fine-tuning job autonomously. Introduces governance complexity around agentic autonomy and model change provenance.

Inkling Thinking Machines Lab open-weight models agentic AI self-hosted AI model fine-tuning foundation models EU AI Act