AI has crossed an important threshold. It is no longer something companies are “exploring” or “keeping an eye on.” For many businesses, AI is being embedded in daily workflows: drafting content, summarizing data, answering customer questions, and accelerating decision-making. With that adoption has come a question that surfaces in nearly every serious conversation about AI, what happens to our data?

That question is important. Unfortunately, the answer to that question is, “it depends”.  

Thankfully there is a systematic approach to understanding how AI handles your data, and it begins by understanding your data, where it is stored, who should have access to the data, and the privacy tradeoffs of exposing that data to AI.  

Public content, anonymized usage patterns, and generalized feedback are very different from customer records, financial information, or proprietary business logic. Treating all data the same is a fatal flaw to using AI. If you understand your data, it becomes much easier to understand what AI solutions to choose.

Choosing an AI solution does not need to be, and often is not, a one size fits all.  An analogy I often use is thinking of AI as a tool belt.  AI tool usage depends on your intended outcome and the type of data you are dealing with. 

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Here is the Key Take Away… For SMBs evaluating AI tools, the path forward does not require deep technical expertise, but it does require intentionality. 

• Start by understanding your own data. Identify what is public, what is internal, and what is sensitive or regulated. 
• Then look closely at how an AI vendor handles that data. Marketing language is easy; clarity is harder. Ask whether data is used for model training, whether that training is optional, how long data is retained, and how it is protected. Vague answers are often more telling than explicit ones.
• Align your privacy posture with the use case. Using AI to rewrite marketing copy or summarize public documents does not carry the same risk profile as analyzing contracts or customer records. Applying a single, rigid standard across all AI usage is usually unnecessary and often counterproductive. Flexibility, not absolutism, is what allows organizations to benefit from AI while staying aligned with their risk tolerance.
• Select AI tools based on outcomes and data sets. This may result in multiple tools if a single tool does not have the flexibility to accommodate all use cases and varied privacy needs. For example, you may select a frontier Cloud-based tool for your business research and select a local tool for inquiries about customer contracts.

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The most overlooked aspect of AI privacy is that it is not a one-time decision. Models evolve. Regulations change. Business priorities shift. The privacy threshold that makes sense today may not be the right one a year from now. Organizations that revisit these decisions periodically will be far better positioned than those that lock themselves into static policies.

At its core, AI is powerful precisely because it learns from data. That reality should not be ignored or demonized. At the same time, it is neither realistic nor responsible to assume that all data should always be available for training. Different businesses, industries, and individuals will draw that line in different places.

The goal, then, is not to eliminate risk entirely. That is impossible. The goal is to make informed choices and to work with tools that respect those choices. Responsible AI adoption does not mean avoiding innovation, and it does not mean surrendering control. It means understanding the tradeoffs, setting boundaries deliberately, and choosing a tool that aligns with your values and your business realities.