Data & AI Governance

A data retention policy sets out how long information should be kept, why it is needed, and what happens when that need ends.

Without clear rules, teams retain too much, delete too soon, or handle exceptions inconsistently, creating avoidable privacy, security, and compliance risk.

An AI governance framework is useful only when it defines who can approve, monitor and stop an AI system. Otherwise, principles remain separate from daily decisions.

Build it around risk, ownership, lifecycle controls and evidence so responsible AI can scale with business needs.

Data stewardship is the human layer of governance: the network of accountable individuals embedded in the business who translate governance policy into observable, measurable practice.

Deploying AI at scale is not simply a technical challenge. It is a governance challenge.

AI governance best practices are not abstract principles to be endorsed in a policy document. They are the organizational mechanisms that determine whether AI delivers sustained value or accumulates risk silently until a failure makes it impossible to ignore.