AI & Startup Law

How do I write an internal AI-use policy and stop 'shadow AI'?

A corporate AI-use policy protects trade secrets and client data from 'shadow AI' — employees using unapproved public tools that may train on their inputs. An effective policy sets acceptable-use perimeters, whitelists closed-environment enterprise tools, prohibits entering confidential information into training-enabled platforms, and assigns accountability for AI-generated output. It works best when grounded in how the specific tools actually behave inside the organization. Lysinski & Associates P.C. builds policies that are realistic to enforce, not just to publish.

Why does my company need an AI-use policy?

To protect trade secrets and client data from 'shadow AI' — employees using unapproved public tools that may train on their inputs.

The data-leak risk is the core driver, and it grows as more staff adopt AI on their own.

How do employees actually leak data through AI?

Through APIs and chat interfaces that retain or train on inputs — which is why access controls, sandboxing, and endpoint measures matter, not just a policy PDF.

Understanding the leak path technically is what turns a policy into real protection.

What legal exposure does shadow AI create?

Trade-secret loss, plus notice duties under laws like Illinois HB 3773 where AI affects employment decisions, BIPA exposure for biometric tooling, and Business Associate Agreement gaps for regulated data.

Verify which of these attach to your organization and tools.

How do I make the policy enforceable?

Pair it with real technical boundaries — whitelisting, access controls, and monitoring — so it is realistic to enforce, not just to publish.

Enforcement comes from pairing the written rules with real technical boundaries, not from the document alone.

Talk to an attorney who builds AI

Your employees already use generative AI — the only question is whether they are leaking proprietary data doing it. Get an enforceable policy with real technical boundaries. (773) 777-9888.

For the firm’s related legal service, see AI governance frameworks.

(773) 777-9888 · info@lysinski.com ·

Frequently asked questions

How do I stop employees putting confidential data into ChatGPT?

Combine a clear policy with technical controls: whitelist closed-environment enterprise tools, prohibit confidential inputs to training-enabled public tools, and back it with access controls and monitoring. A policy alone, without technical boundaries, tends not to hold.

What goes in a corporate AI-use policy?

Acceptable-use perimeters, a list of approved (and prohibited) tools, a ban on entering confidential or client data into training-enabled platforms, and assigned accountability for AI-generated output — all grounded in how your specific tools actually handle data.

What is 'shadow AI' and why is it risky?

Shadow AI is employees using unapproved AI tools outside company oversight. The risk is that those tools may retain or train on the inputs, exposing trade secrets and client data, with no contract or controls governing where that data goes.

Does an AI policy need technical enforcement?

Yes. Because leaks happen through APIs and chat interfaces, real protection needs access controls, sandboxing, and endpoint measures alongside the written rules. A PDF policy without technical boundaries is hard to enforce.

What laws touch internal AI use?

Trade-secret law; notice duties under laws like Illinois HB 3773 where AI affects employment decisions; BIPA for biometric tools; and Business Associate Agreement requirements for regulated data. Which apply depends on your tools and data — verify before relying on a specific rule.

Who should own the AI-use policy?

Typically the General Counsel, HR, and IT jointly — legal for the obligations, HR for the employment dimension, and IT for the technical controls. The policy should assign clear accountability rather than leave ownership ambiguous.

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