Summary:
Growing companies should put AI rules in writing before informal tool use spreads across departments. A workable policy identifies approved platforms, barred inputs, review steps, and team-specific examples for sales, support, HR, and engineering. Keep it short, assign one owner, train staff, and review it on a schedule so the policy keeps pace with daily operations.
For growing companies, AI use is now an internal controls issue. Teams are already using public and embedded tools across go-to-market, people ops, customer support, and product development. That creates immediate questions around confidential inputs, vendor access, output review, record retention, and accountability across functions. A written internal policy puts those rules in one place before informal practice turns into company-wide exposure.
AI Use Is Already a Workplace Practice
That kind of adoption arrives through convenience. Teams reach for tools that save time, clean up language, summarize calls, draft code, and sort information. Without written rules, employees tend to fill in the blanks on their own. One person might enter customer details into a public tool, another might rely on AI data analysis without checking it.
An internal policy gives the company a shared set of rules before habits form. It tells employees which tools are approved, what data stays out of public systems, when human review is required, and who can answer questions when a use case falls into a gray area. This keeps speed in place while cutting avoidable risk.
What a Usable Policy Should Cover
A workable policy should name approved tools, barred inputs, review steps, retention rules, and team-specific examples. For example, sales may use AI for first-draft outreach and call summaries, while customer lists, deal terms, and nonpublic revenue figures stay out. Support may draft responses, but a person should review messages tied to refunds, account access, or safety issues before they go out.
HR and engineering should have their own lanes as well. HR can use AI for things like public-facing job copy and interview guides, while candidate files, compensation data, and employee records stay protected. Engineering teams can use coding assistants inside defined workflows with code review, testing, and license checks built in. Clear examples usually do more for internal adoption than abstract rules.
Put One Owner on the Calendar
Policies fail when they live in a folder without any review. Consider assigning one owner, bringing in legal, HR, security, and technical leads for input, and training teams on the rules they will use in real work. The policy should be reviewed on a set schedule, especially when the company adds new vendors, new data flows, or new hiring plans. AI tools change fast, so internal rules should keep pace.
Write the Rules Before Cleanup Starts
If your company is growing and AI is already in daily use, this is the point to put guardrails in writing. Fridman Law Firm helps founders and leadership teams build practical internal policies that fit hiring, product development, customer communications, and fundraising plans, so the business can keep moving with fewer surprises later.
FAQ: Protecting Your Company with AI Policies
- Do we need an AI policy if only a few employees use ChatGPT?
Yes. A policy is most useful early, when habits are still forming. Even a short document can set rules for approved tools, blocked data, and review steps before unofficial practices spread.
- Who should own the policy?
Best practice is often to assign one accountable owner, then gather input from legal, HR, IT or security, and department leads. One owner keeps revisions moving and gives employees a clear place to bring questions.
- What should employees keep out of public AI tools?
Keep out customer personal data, candidate materials, employee records, nonpublic financial information, confidential contract terms, and source code or internal documents that the company would not post publicly.