Privacy and Security when using LLMs

Security and Privacy in the Use of LLMs

With the rise of Large Language Models (LLMs) such as ChatGPT, the need for clear policies around security and privacy is growing. These powerful tools can offer enormous benefits, but they also present risks that must not be ignored. In this article, I discuss key considerations and concrete measures to remain secure and compliant.

Privacy: How to Stay Compliant?

Privacy is a fundamental right, and legislation like the GDPR (General Data Protection Regulation) imposes strict requirements on the processing and storage of personal data. According to the GDPR, personal data of Europeans may only be processed outside the EU if appropriate protection mechanisms are in place. This means organizations must clearly know where their AI provider’s servers are located.

Practical Example:

Suppose an employee summarizes meeting minutes using an LLM like ChatGPT. If these minutes include names and contact information, they cannot simply be shared with a standard AI tool hosted on servers outside Europe. The solution? Anonymize the data or use a service hosted in Europe, such as Azure OpenAI or a Google Gemini server.

Measures to Stay Compliant:

• Use AI tools with servers within the EU.

• Verify that providers comply with GDPR guidelines.

• Share no personal data unless strictly necessary.

By following these steps, you remain within legal boundaries and protect sensitive data.

Security: Protect Your Data

In addition to privacy, security plays an equally important role in using LLMs. Unintentional data leaks or improper usage can have serious consequences.

Preventing Data from Being Used for Training

Many AI providers use input data for model training. Organizations must ensure their data is not unintentionally stored or reused.

• Disable options that allow data use for training.

• Carefully review the AI provider’s terms and conditions.

Use of Open-Source Models

For maximum control, you can opt for open-source LLMs hosted locally on your servers. This ensures that data never leaves your organization. Models like LLaMA 3 or other open-source alternatives can be installed locally, allowing confidential documents to be processed even if the computer is offline.

Data Anonymization and Synthetic Data

You can also prevent sending “real” data to an LLM by using anonymous or synthetic data:

• Anonymization: Remove all personal identifiers before sharing data with an LLM.

• Synthetic Data: Use fictitious data that has the same structure as real data.

Example:

A data analyst wants to generate a chart for an internal presentation using an LLM. Instead of real client data, the analyst creates a synthetic dataset with the same structure. The LLM then generates the code for the chart, and the analyst replaces the synthetic data with the real data. This eliminates the risk of exposing sensitive information.

Additional Measures:

• Ensure encryption during data transfer.

• Limit access to LLM tools to authorized users.

• Conduct regular audits to assess compliance and security.

Transparency from AI Providers: Building Trust Through Clarity

Trust in AI starts with transparency. A reliable provider openly communicates about:

• Data Processing: How and why data is used.

• Storage Locations: Where your data is physically stored.

• Security: Measures taken, such as encryption or access control.

Practical Tips for Choosing a Provider:

1. Select AI providers that comply with local laws and regulations, such as GDPR.

2. Verify that the provider performs independent security audits and shares results transparently.

3. Explicitly ask whether your data is used for model training and how to disable this.

User Education: Awareness of Data Sharing

No security policy is effective without well-informed users. User education plays a crucial role in preventing unintended risks.

What Users Need to Know:

1. What to Share and Not Share: Users must understand that sensitive data, such as names or financial information, should never be shared with an LLM without precautions.

2. Using Synthetic Data: Train employees to use synthetic data for testing or tasks involving LLMs.

3. Recognizing Risks: Teach users to identify unsafe situations, such as tools requesting more data than necessary.

Example of User Awareness:

An employee summarizing client contracts with an LLM must first anonymize the documents and ensure no confidential information is shared.

Conclusion

LLMs offer significant opportunities to increase productivity and efficiency, but they require careful handling. By implementing privacy and security measures, collaborating with transparent providers, and investing in user education, organizations can fully benefit from LLMs without compromising security.

Focusing on the right models, data anonymization, synthetic data, and aware users ensures that LLMs can be used securely.