Protecting Your Business Data While Leveraging AI Innovation

Companies now use generative AI tools like ChatGPT, Bard, and Bing to revolutionize customer interactions and content generation. These tools transform how businesses operate. However, they also create significant data security and protection concerns.

This article explores the reality of generative AI and data security. We’ll provide tips for safe and responsible usage.

Generative AI and Data Security Risks

Third-party generative AI tools pose primary risks through potential data breaches and leaks. These tools automatically generate content. They collect and store vast amounts of data during this process.

Major financial institutions recognize these risks. JP Morgan Chase and Deutsche Bank have restricted or banned ChatGPT in their workplaces.

Many users don’t understand the risks of third-party generative AI. ChatGPT uses an iterative tool that relies on machine learning. The system uses every interaction as training data for future responses. The AI engine saves all information shared in chats on its servers. This data may include sensitive, personally identifiable information (PII).

This data lives outside your infrastructure. You’ll find it hard to retrieve and delete when data leaks occur.

People embrace generative AI’s impressive power. They integrate it into daily tasks like drafting emails and generating code. Samsung banned generative AI tools after this integration caused concerns. Engineers uploaded source code into ChatGPT. This action potentially shared sensitive intellectual property with a third party.

How to Use Generative AI Responsibly

The risks of generative AI largely depend on how people use it. These tools increase efficiency. This can tempt users to apply them without discrimination.

Understanding how the technology works creates genuine data protection concerns. Here are steps to avoid data leaks when using generative AI tools:

• Limit shared data: Share only necessary data for tool function. Anonymize sensitive data, make it obscure (use percentages or factors of 10), or don’t share it.
• Train employees: Ensure all tool users receive training on data protection and privacy best practices. Help them understand risks to avoid sharing sensitive IP or proprietary information.
• Keep data in-house: Use AI solutions with local large language models (LLMs) instead of sending data to third parties. This requires developing a company-specific LLM through an AI solutions provider.

Benefits of Local Large Language Models

Bloomberg took the third security option. The financial services company developed its own language model called BloombergGPT. This model specifically serves the financial industry’s needs.

Bloomberg built the 50-billion parameter LLM using its financial data and domain knowledge. The AI team leveraged rich data the company collected over 40 years. They translated this into training data for their generative AI solution.

BloombergGPT demonstrates how domain-specific LLMs outperform general-purpose LLMs in narrow fields. In this case, it excels in financial markets. Bloomberg may not have developed its GPT for security reasons alone. Having it ensures employees won’t accidentally share sensitive data outside the company.

BloombergGPT helps company employees create accurate reports. It processes vast amounts of data from the Bloomberg Terminal.

Conclusion: Balance Innovation with Security

Generative AI tools offer tremendous benefits to companies. They automate tasks and generate high-quality content quickly and efficiently. However, these tools create risks around data security and protection.

Having your own company-specific or domain-specific LLM helps mitigate data leaks. It provides employees with generative AI benefits while maintaining security.

Want to see how a business-specific language model can benefit your company? Our specialists can help you explore secure AI solutions tailored to your needs.