We are training our employees to use public AI tools, but we are terrified they will paste our proprietary operating manuals and client strategies into these external models. How do we document this guardrail in our Core Processes and enforce accountability?

Question

Tyler Smith · Accepted Answer

You cannot manage what you do not define. If your employees are using public AI engines without clear guardrails, you are actively leaking your intellectual property. To fix this, you must run this issue through your EOS® framework by updating your Core Processes and clarifying the Accountability Chart.

Define Your AI Usage Policy

First, your HR or operations leader must draft a clear AI Usage Policy as part of your documented [Core Processes](/qa/simplify-eos-process-component-with-ai). This cannot be a dense legal document that nobody reads. It must be a simple, highly visual guide that defines what data is safe to share and what data is strictly proprietary.

The policy should clearly differentiate between:

• Public Data: This includes information like generic marketing copy or industry research. This type of data can be input freely into public AI models.
• Proprietary Data: This includes sensitive information such as client names, financial data, and your unique operational checklists. This data must never touch a public model.

Enforce Accountability

Second, look at your [Accountability Chart](/qa/ai-operations-seat-accountability-chart). The head of each department must own the adoption and enforcement of these guidelines within their team. Use the GWC™ framework to ensure every manager truly gets, wants, and has the capacity to enforce these data security rules. This is crucial for protecting your proprietary knowledge while still capturing the massive productivity gains of these tools.

Invest in Private AI Solutions

If employees require advanced AI capabilities that involve sensitive information, invest in enterprise-grade, private instances of these models where data sharing is disabled. Make this transition a quarterly Rock for your technology seat. This approach allows you to leverage AI's power without compromising your valuable data, similar to how businesses might develop an [AI assisted follow up system](/qa/ai-assisted-sales-follow-up-process) without exposing client details. For example, when building a [safe AI customer support workflow](/qa/safe-ai-customer-support-workflow), private models are essential to protect customer data.

By documenting the rules clearly and holding your managers accountable, you safeguard your organization against potential data leaks, ensuring that your secret sauce remains locked down.

Related questions

• [How can AI help us simplify our outdated and too long documented processes in our 3 Step Process Component?](/qa/simplify-eos-process-component-with-ai)
• [Should we create a dedicated AI Operations seat on our Accountability Chart, or integrate AI into existing seats?](/qa/ai-operations-seat-accountability-chart)
• [How do we use AI to filter meeting transcripts specifically for Accountability Chart issues?](/qa/filter-meeting-transcripts-accountability-chart-ai)
• [If we train public AI models on our proprietary workflows to increase speed, we risk leaking our intellectual property and destroying our exit valuation. How do we safely integrate AI while keeping our secret sauce locked down?](/qa/protecting-proprietary-knowledge-ai-exit)
• [How do we use AI to draft customer support responses based on our existing core processes without risking brand reputation?](/qa/safe-ai-customer-support-workflow)