AI policy is not about restriction. It is about effectiveness.
Tell your team not to use AI, and they will use it anyway. The tools are readily available, and the pressure to move faster is real; so they use it in the shadows, where you have no visibility and no control.
This is not a productivity problem. It is a policy problem.
If you haven’t defined what acceptable AI use is, every individual makes their own decisions, and you can’t manage risk, ensure quality, or operate efficiently. A good AI policy defines data security and usage expectations. Your policy must specify which tool(s) your team is allowed to use, when and how, by role within the opportunity lifecycle. It will enhance your team’s efficiency; it will not restrict their productivity.
Policy Rules: Leadership determines the limits
Not all AI platforms are the same. Some are built to meet federal security requirements (e.g., FedRAMP); many are not. Leadership, along with IT Security, evaluates tools for security, compliance, and appropriateness, then communicates what is approved and, more importantly, why. The AI tool must fit within your operational environment as well as your security protocols.
Rule 1. Do not use tools that will train on your company’s proprietary data.
Free browser versions and AI tools built into search engines are not secure and will train on your interactions. Many subscription-based AI tools have settings to disable model training, which secures your data for internal use only. Check the data handling terms to ensure the tool complies with your company’s data security protocols.
Rule 2. Vet the architecture, not the brochure.
Make the vendor prove it understands FAR compliance, evaluation mechanics, source selection nuance, and federal data security requirements, not just how to write clean sentences. Review the terms of service, then test the tool beyond the demo to ensure it applies these concepts appropriately.
Rule 3. Reassess your policy when the tool changes.
A new platform, or a significant update to an approved one, can change how your content needs to be structured, tagged, or stored. This needs to be assessed, documented, and distributed back to the team. Build a milestone-based process for evaluating whether your knowledge base and the policy still fit what the platform requires.
