Responsible AI in Government: Set the Guardrails Before You Deploy
AI tools are arriving in government offices faster than policies to govern them. Here's how to capture the benefits while protecting sensitive data and public trust.
Artificial intelligence has moved quickly from pilot projects into everyday tools. Staff are drafting reports with AI assistants, vendors are adding AI features to records and case management systems, and leaders are being asked what their organization’s AI strategy is.
The opportunities are real: less time on paperwork, faster searches through large volumes of records, and better use of the data agencies already collect. But government organizations carry obligations private companies often don’t, including protecting sensitive data, meeting public records and retention rules, and maintaining the public’s trust. The time to set guardrails is before AI is embedded in critical work, not after.
Start with a clear policy
Every organization using AI, formally or informally, needs a written policy that answers a few basic questions:
- Which tools are approved, and for what kinds of work?
- What data can never be entered into AI tools, especially public consumer tools? Criminal justice information, health data and personal information belong on that list.
- Who reviews AI output before it’s used, and who is accountable for the result?
- How is AI use disclosed, to the public and in records?
Many staff are already experimenting with free AI tools. A policy that offers approved, secure options is more effective than a blanket ban people quietly ignore.
Protect sensitive data
For agencies handling criminal justice information, CJIS Security Policy requirements apply to AI systems just as they do to any other system that stores, processes or transmits CJI. Before adopting an AI-enabled product, ask the vendor where data is processed and stored, whether your data is used to train their models, how access is controlled and logged, and how the product meets your compliance obligations.
Keep humans in the loop
AI can produce confident answers that are wrong. For decisions that affect people’s rights, benefits or safety, AI should support human judgment, not replace it. Build in review steps, train staff on AI’s limitations, and periodically check outputs for accuracy and bias.
Use an established framework
You don’t need to invent an approach from scratch. The NIST AI Risk Management Framework offers a practical structure for identifying and managing AI risks across governance, mapping, measuring and managing. It’s voluntary, widely referenced and adaptable to organizations of any size.
Build AI into procurement
As AI features appear in more products, your RFPs and contracts should address them directly: data use and ownership, model transparency, security, accuracy expectations, and your right to turn AI features off.
Start small and prove the value
The most successful AI efforts start with a well-defined problem, such as summarizing lengthy documents, routing requests or finding information in large records collections. They prove value with a pilot before scaling. That’s exactly the approach of our Technology Incubator: build a working concept, measure the results, and decide with real evidence.
How GTEL can help
We help organizations write practical AI policies, evaluate AI-enabled products, add AI requirements to procurements, and build and test AI concepts safely. Contact us to start the conversation.
Resources: NIST AI Risk Management Framework
