Why Your Firm's AI Policy Probably Isn't Enough

The Policy Everyone Wrote

Sometime in the last two years, your firm probably wrote an AI policy. It likely says something along the lines of: "Employees shall not input client data, confidential information, or proprietary materials into external AI tools including but not limited to ChatGPT, Google Bard, and similar platforms."

That policy was a good first step. It demonstrated awareness. It showed clients that the firm takes data security seriously. And in many cases, it was enough to satisfy the immediate compliance concern.

But here is the problem: that policy is not working.

The Enforcement Gap

Talk to any managing partner privately, and they will tell you the same thing. Associates are still using ChatGPT. Analysts are still pasting financial models into Claude. Paralegals are still uploading contracts to AI summarization tools. They are doing it because the productivity gains are simply too significant to ignore.

A junior associate who can draft a first-pass motion in 20 minutes instead of three hours is not going to stop because of a policy memo they read six months ago. An analyst who can summarize a 200-page offering memorandum in seconds is not going back to manual review. The incentives are too strong.

Your policy created a rule. It did not create an alternative. And when the only options are "use the tool that makes me faster but violates policy" or "spend three extra hours on this task," people will quietly choose the first option every time.

What Actually Happens When Someone Violates the Policy

When an associate pastes a client brief into ChatGPT, several things happen that most firms do not fully appreciate.

First, that data is transmitted to OpenAI's servers. Depending on the account type and the terms in effect at the time, that data may be used to train future models. Even if OpenAI's current terms say otherwise for certain account tiers, those terms can change, and the data has already left your control.

Second, there is no audit trail within the firm. Nobody knows what was uploaded, when, or by whom. If a client asks whether their data was exposed to third-party AI systems, the honest answer is: "We don't know."

Third, depending on your industry, this could constitute a regulatory violation. For law firms, ABA Model Rule 1.6 requires that attorneys make reasonable efforts to prevent unauthorized disclosure of client information. Pasting that information into a consumer AI tool is a hard argument to defend as "reasonable." For financial advisory firms, SEC and FINRA expectations around data handling create similar exposure.

Policy Without Infrastructure Is Just Hope

The fundamental problem with a policy-only approach is that it relies entirely on individual compliance. Every person, every time, has to make the right decision. No tool, no system, no safeguard catches them if they don't.

Compare this to how firms handle other security concerns. You don't rely on a policy memo to prevent unauthorized access to your network. You deploy firewalls, access controls, encryption, and monitoring. You build the infrastructure to enforce the standard.

AI should be no different. If your firm has decided that client data should not be processed by third-party AI providers, the right answer is not to write a policy and hope for the best. The right answer is to deploy infrastructure that makes the secure choice the easy choice.

The Infrastructure Approach

What does this look like in practice? Instead of telling your team they cannot use AI, you give them a private AI assistant that runs on infrastructure the firm controls. The interface is just as intuitive as ChatGPT. The capabilities are comparable. But every query, every uploaded document, every generated response stays within your firm's secure environment.

Your team gets the productivity gains they are already chasing. The firm gets the data security and compliance posture it needs. And you replace an unenforceable policy with a system that enforces itself.

What Firms Should Do Now

If your firm currently relies on a written AI policy as its primary control, consider three steps.

Audit the gap. Have an honest internal conversation about whether people are actually following the policy. Anonymous surveys can help. The answer will almost certainly be that adoption of consumer AI tools is higher than leadership realizes.

Evaluate the risk. For each practice area or department, assess what data might be entering third-party AI systems. Client names, financial figures, health records, legal strategies — the exposure may be broader than expected.

Deploy the alternative. Give your team a private AI environment that delivers the same productivity benefits without the data exposure. When the secure option is just as easy to use as the insecure one, the policy becomes a backstop rather than the primary defense.

The firms that will navigate this transition successfully are the ones that recognize a simple truth: you cannot policy your way out of an infrastructure problem.