Policy should make safe behavior easier
Fire departments are already dealing with AI questions whether a formal policy exists or not. Staff may be experimenting with public tools, vendors may be demonstrating new capabilities, and leaders may be asked to approve a purchase before they have agreed on the problem. A practical policy gives the department a common language for those conversations.
The goal is not to ban every tool or to create a document so restrictive that nobody can follow it. The goal is to establish boundaries that protect the mission while giving leaders room to test low-risk, reviewable uses.
Seven parts of a practical guardrail framework
1. Purpose and scope
State why the department is addressing AI and who the policy covers: employees, volunteers, contractors, interns, vendors, and anyone using department information. Include both department-owned tools and public tools used for department work.
2. Approved uses
List examples of lower-risk work that may be allowed with review, such as meeting summaries, training outlines, draft public-education material, document organization, and non-sensitive writing assistance. Make clear that examples are subject to the department’s data and review rules.
3. Prohibited or restricted information
Identify information that must not be entered into an unapproved AI system. Depending on the department and jurisdiction, that may include PHI, personally identifiable information, personnel matters, active investigations, juvenile information, protected addresses, security-sensitive details, credentials, and confidential legal or procurement material. The policy should direct staff to ask when classification is unclear.
4. Human review and accountability
Say plainly that AI output is a draft or aid, not an authority. A named person remains responsible for checking facts, local policy, calculations, tone, citations, and operational implications. The policy should identify when a second review or supervisor approval is required.
5. Approved tools and vendor review
Do not treat a polished demonstration as approval. Before adoption, ask where data is processed, how it is retained, who can access it, whether it is used for model training, how deletion works, what logs exist, and what support is available when the tool is wrong. Procurement, information security, legal counsel, and records staff may all have a role.
6. Records and retention
AI-related drafts and prompts may become part of a department record depending on the task and jurisdiction. Coordinate the policy with public-records obligations, existing retention schedules, discovery requirements, and applicable labor or privacy rules. Avoid making a legal conclusion in the AI policy; point staff to the authority responsible for that determination.
7. Training, reporting, and review
Explain how people will learn the policy, how they report a problem or suspected disclosure, and who reviews the policy as tools change. A short annual review is useful, but a significant vendor, workflow, or legal change may justify an earlier review.
What the policy should not promise
Do not promise that an AI tool is accurate, private, compliant, or safe simply because a vendor says it is. Do not promise that a policy eliminates risk. Do not promise that AI can replace professional judgment or emergency decision-making. A credible policy names the controls the department can actually enforce.
Use a policy draft as a conversation starter
A first draft is valuable when it exposes unanswered questions: Which tools are approved? Who can authorize a pilot? What information is sensitive? What review is required for public communications? Who receives an incident report? Which records rules apply? Those questions are not a failure of the draft. They are the work leadership needs to complete before adoption.
Put the guardrails where work happens
A policy that sits in a folder and never reaches the people doing the work will not change behavior. Pair the written policy with a short approved-use checklist, examples of information that must stay out of public tools, and a clear person or office to contact when a situation is uncertain. Supervisors should be able to explain the policy in the same plain language used during training.
Review the policy after the first real question or near miss. Those moments show where the language is unclear, where a workflow needs a better control, or where the department has made an assumption that needs authority. A living policy is not a sign that the first draft failed. It is a sign that leadership is paying attention as tools and use cases change.
Related resources: Readiness Call · AI Readiness Checklist · REACT Guide · Consulting · Training.