Preparing Government Data for AI

AI begins with trusted data

Generative AI and machine learning can accelerate mission work, but their value depends on the quality, accessibility, security, and context of the underlying data. Agencies should resist starting with a model and instead begin with a well-defined mission problem and trusted information sources.

Create the data foundation

Priority work includes data profiling, quality rules, metadata, access controls, retention policies, and authoritative-source identification. Structured and unstructured data should be prepared in ways that preserve lineage and support repeatable retrieval.

Use focused architectures

Retrieval-augmented generation can connect large language models to approved agency content without requiring the model to memorize sensitive information. Vector search, document chunking, embeddings, prompt controls, and response citations can make outputs more relevant and easier to validate.

Build security and governance in

Identity, least-privilege access, encryption, logging, human review, model evaluation, and acceptable-use controls should be established before broad deployment. Agencies also need clear processes for handling sensitive data, bias, hallucinations, and records-management obligations.

Start small and measure

The strongest early use cases are bounded, repeatable, and measurable. such as knowledge search, document summarization, correspondence assistance, or analyst support. Pilots should evaluate accuracy, time saved, user adoption, and risk before scaling.

Key considerations

• Start with a bounded mission problem rather than a model or tool.
• Identify authoritative data sources and address quality, access, lineage, and retention.
• Design security, human review, evaluation, and records obligations before scaling.

Practical recommendations

• Prioritize measurable pilots such as knowledge search, summarization, or analyst support.
• Ground responses in approved content and require citations or other traceability.
• Scale only after usefulness, adoption, accuracy, and risk are understood.