Document the privacy risks in your AI project with a lawyer-drafted assessment

A privacy policy explains your data handling externally. A short advice note may flag issues at a high level. A privacy impact assessment goes further by recording the processing in a structured way, identifying where privacy risks arise and setting out the factors that need attention before the project moves ahead. For AI systems, that may include training data, user prompts, generated outputs, monitoring, human review, profiling concerns and third-party model providers. It is often the more useful document where the business wants a written internal record rather than just public-facing wording.

It will usually examine what personal data the system uses, where that data comes from, the purpose of the processing, who can access inputs and outputs, what third parties are involved, how long information is kept, and where the main privacy risk points sit. Depending on the project, it may also address automated decision-making concerns, special category data, testing datasets, feedback loops, model improvement practices and governance controls. Your data collection points, internal use and third-party sharing arrangements all affect the way this should be drafted, so the assessment needs to reflect the real workflow rather than a generic AI description.

We usually need a practical explanation of the AI tool or feature, including what it does, who uses it, what data goes in, what comes out, whether the outputs affect individuals, and which providers or internal teams are involved. It also helps to know whether the system is customer-facing, internal-only, or embedded in a wider product. Existing privacy documents, supplier terms and technical summaries can be useful. The contract should reflect the practical arrangement, not just a generic precedent or one-sided checklist, not just the label attached to the tool.

Templates can be useful for structure, but AI projects often raise fact-specific issues that a generic form will not capture well. A chatbot using customer support history, an internal productivity assistant, and a recommendation engine may all involve very different privacy questions even if each is described as AI. If the assessment is too generic, it may miss the real points of concern or record the processing inaccurately. A tailored document is usually more helpful where you need a credible written analysis of the actual system you are using or planning to deploy.

No single document can do that by itself. This gives you a stronger basis for decision-making, but it should be applied alongside the way your business actually operates. Whether the project is compliant will depend on the wider factual setup, including contracts, governance, security measures, internal practices and how the system is used over time. The assessment is one important part of that picture because it records the processing and the issues identified. If you later need policy updates, contract changes or broader implementation support, that would be separate work.

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