Many organisations have operated for years without formal data classification and handling rules. The exposure was manageable when data movement was limited to known internal systems. AI deployment changes this equation fundamentally. Every AI tool is a potential data destination. Every workflow is a data handling decision. The question of what data can enter which tool must now be answered before each workflow goes live. Without documented rules, the answers default to individual judgment. Individual judgment is inconsistent across functions, inconsistent over time and impossible to audit.
Three data governance decisions must exist before any AI workflow is deployed. A classification scheme that defines what types of data exist in the organisation and their sensitivity levels. A set of handling rules that specify which data types can be processed by which categories of AI tool: for example, which tools may process customer personal data and which may not. And a retention and deletion policy that addresses what happens to data once it enters an AI system: how long it is retained, who can access it and how deletion is triggered. These decisions do not require a data engineering team. They require a named owner, a structured design session and documentation that is referenced in every workflow approval process.
Before the next AI workflow goes live, answer three questions: what data will this workflow process, and what is its classification? Which handling rules apply to this data type in an AI context? What happens to this data after the AI processes it? If these questions cannot be answered from a documented policy, the policy needs to be written first. Every week of deployment without it is a week of decisions made without accountability for what enters the system. A week in which the gap between the organisation's data handling practice and its regulatory obligations widens.