Protecting Sensitive Data During Interactions with Generative AI Services: Best Practices and Emerging Technology

As generative AI tools like Microsoft Copilot, ChatGPT, and Google Gemini become increasingly integrated into our daily workflows, they’re transforming how employees write, code, research, and collaborate. Their efficiency and accessibility make them indispensable in many industries, but their rapid adoption also brings a new layer of risk. Every time a user interacts with one of these platforms by asking a question, pasting in a document, or sharing snippets of proprietary code, there’s potential for sensitive data to slip into environments outside the organization’s control.

Data leaks, unintentional exposure of intellectual property, and violations of privacy regulations are all real risks of generative AI use. For SMBs who are in the process of introducing generative AI services into the workplace, there is now the challenge of learning how to use it responsibly. Protecting sensitive information requires balancing innovation with security, establishing guardrails for employees, and leveraging new technologies designed to make AI adoption safer.

The Risk Behind Using AI Chatbots

Generative AI platforms are designed to absorb input, analyze it, and provide human-like responses. But in doing so, they can inadvertently capture, process, and retain information that was never meant to leave a secure corporate environment. For example, when employees paste proprietary source code, financial reports, or customer records into a chatbot to “speed up” analysis, they may be unknowingly handing over highly confidential data. Depending on the platform’s data retention policies, that information might be stored, logged, or even used to further train the model. This raises red flags for both cybersecurity and compliance officers.

The risks aren’t hypothetical. In 2023, Samsung made headlines when engineers reportedly uploaded sensitive source code to ChatGPT, which later leaked beyond intended channels. As a result, the company instituted a sweeping ban on employee use of the tool. Their story underscores a broader point: without proper oversight, even a single misstep can have outsized consequences.

Still, the solution isn’t to abandon AI altogether. Just as email, cloud storage, and mobile devices were once viewed as security threats before organizations developed governance policies around them, generative AI can be used safely with the right 

precautions. By combining employee awareness, clear policies, and emerging protective technologies, businesses can unlock the benefits of AI while minimizing the risks.

Best Practices for Protecting Sensitive Data

1. Establish Clear Usage Policies: Organizations need to set clear rules about when and how employees may interact with AI chatbots. Without guidance, workers may unintentionally upload sensitive information simply to save time or improve efficiency. Policies should explicitly prohibit sharing confidential data—like customer PII, financial details, or source code—into public AI platforms. For example, in early 2023, JPMorgan Chase restricted employee use of ChatGPT and issued company-wide guidelines to prevent accidental disclosure of financial data. Rather than banning AI outright, they encouraged employees to use internal, sandboxed AI tools that met security standards.

2. Choose Business-Friendly AI Platforms with Strong Privacy Controls: For small and mid-sized businesses, cost and accessibility matter. Fortunately, many AI vendors now offer business-friendly plans that provide stronger privacy guarantees than free, consumer-facing versions. These options, such as Microsoft 365 Copilot, often include settings that prevent your data from being stored or used to train models, along with clearer terms of service around data protection. When evaluating a tool, SMBs should prioritize platforms that integrate with their existing workflows and allow them to disable data retention by default.

3. Implement Data Anonymization and Minimization: Before sending data into an AI tool, organizations should strip out identifying details. Techniques like pseudonymization, masking, or tokenization can make inputs safer without undermining their usefulness for analysis. The principle of data minimization, or sharing only what’s absolutely necessary, further reduces the risk of leakage.
A promising new development in this space is the use of data twin technology, which allows organizations to create a safe “mirror” of sensitive information. Employees can interact with the twin, rather than the raw data, ensuring privacy is protected while still enabling meaningful insights. We’ll explore this technology in more detail later in the article, but it’s worth noting here as an emerging best practice.

4. Train Employees on Safe AI Practices: Technology alone won’t solve the problem, and employee awareness is critical. Training should cover what kinds of information are considered sensitive, how AI systems process data, and the risks of accidental disclosure. Regular reminders, scenario-based exercises, and easy-to-follow checklists can reinforce good habits.
For SMBs, this doesn’t have to mean costly, enterprise-scale training programs. Instead, short workshops, quick-reference guides, or even monthly “lunch and learn” sessions can be enough to build awareness. Leadership should also make AI safety part of the culture, reminding employees that security isn’t just an IT responsibility but a company-wide effort.

5. Deploy Monitoring and Protective Technologies: Finally, organizations should integrate monitoring tools that detect and block sensitive data before it leaves the corporate environment. Data Loss Prevention (DLP) systems, combined with AI-specific safeguards, can flag when employees attempt to share restricted content with chatbots. These technologies act as a last line of defense when policies and training fall short.

Data Twins in Action

Suppose a company wants to generate a quarterly customer analysis report using last year’s sales data and extensive customer records. Normally, this would require uploading sensitive financial and personal information into an AI model, posing a clear security and compliance risk. With TouchBrick’s solution and its data twin technology, every element of that sensitive dataset is replaced with a synthetic equivalent before entering the AI system. The AI can then process the data twin as though it were real, producing accurate insights and reports, while the true data never leaves the company’s secure environment.

For small and mid-sized organizations, this is especially valuable. SMBs often lack the enterprise-grade security infrastructure of larger corporations, making them more vulnerable to accidental data leaks. Data twins provide a cost-effective way to mitigate these risks. Whether it’s employee salary records, patient health information, or confidential internal notes, synthetic data ensures sensitive details stay protected while still allowing teams to harness the productivity benefits of AI.

The concept of data twins represents a shift in how organizations think about privacy. Instead of locking down data so tightly that it becomes difficult to use, this approach creates a safe middle ground: enabling insight, automation, and innovation while ensuring security and compliance. For SMBs that want to embrace AI but can’t afford the fallout of a data breach, technologies like data twins could prove to be a game-changer.

Moving Forward

Generative AI is quickly becoming a cornerstone of modern work. For SMBs, the challenge isn’t whether to adopt AI, but how to do so safely. The risks of data leakage, compliance violations, and accidental exposure are real, but they’re also manageable. 

By combining clear usage policies, business-friendly platforms, anonymization techniques, employee training, and protective monitoring technologies, organizations can create a safer environment for AI adoption. 

If you’re ready to explore how to make AI both powerful and safe for your business, PulseOne can help. Take our free online assessment to gauge if your business is ready for AI and reach out to our team to learn how to adopt generative AI responsibly.

For more information, be sure to check out the complete PulseOne Tech Talk with Jason Melo of TouchBrick.