Data Governance Frameworks

A data governance framework is the set of policies, roles, and processes an organisation uses to manage its data assets. For AI, this means defining who can access training data, what quality standards it must meet, how long it's retained, and what approvals are needed before it's used in a model. Frameworks range from lightweight internal guidelines to formal structures aligned with standards like ISO 27001 or the DAMA-DMBOK body of knowledge. The right level of formality depends on your organisation's size, industry, and risk tolerance. What matters most is consistency and enforceability - a beautifully written policy that nobody follows is worse than no policy at all, because it creates a false sense of security. Practical governance frameworks assign clear ownership: someone is responsible for each dataset, someone approves its use, and someone monitors compliance. They also build in review cycles, because the rules that made sense when you started may not hold as your AI use cases evolve. If you're evaluating an AI vendor, ask about their governance framework. If the answer is vague or defensive, that tells you something important about how seriously they take the data behind their product.