Building a GDPR-Compliant AI System

Building a GDPR-compliant AI system requires careful navigation of both regulatory and technical complexities. While compliance is often 80% paperwork and 20% engineering, working with large language models (LLMs) introduces unique risks that demand specialized safeguards.

At Bilanc, we’ve built our AI-powered engineering management platform with privacy and security at the core, ensuring compliance without sacrificing functionality.

Key Risk Areas in AI-Powered Analytics

When leveraging LLMs for AI-driven engineering insights, the primary risks include:

• Handling Personally Identifiable Information (PII) – Preventing the processing of names and other personal details in ways that violate privacy regulations.

• Third-Party LLM Dependencies – Establishing clear agreements on data retention and usage policies when working with external providers.

• Security and Data Retention – Ensuring that all data is processed ephemerally, minimizing the risk of leaks or unauthorized access.

Architectural Patterns for GDPR Compliance

1. PII Removal and Secure Data Processing

Instead of merely masking or substituting names, we take a zero-PII approach, removing personal identifiers before any request is sent to an LLM. Every developer in the system is referred to generically as "The Engineer." This ensures that:

✅ No personally identifiable data is exposed to the model.
✅ Biases related to names, demographics, or personal details are eliminated.
✅ The integrity of the contextual data remains intact for AI performance.

Additionally, unstructured data, such as chat logs, poses a higher risk of inadvertently storing PII. To mitigate this:

• We do not store unstructured logs containing sensitive information.

• Code analysis is performed without identifiable user data.

• All processing occurs in a secure, transient manner with no long-term storage.

2. Zero Data Retention and Strong Provider Agreements

We work with OpenAI under a strict zero data retention agreement, ensuring:

• Data is only stored ephemerally for processing and not retained post-request.

• OpenAI does not use our data for training or improving its models.

• No historical logging of user queries occurs on the provider's end.

As AI providers face growing scrutiny over data usage, it’s critical to establish well-defined agreements that address:

• Data Ownership – Ensuring our prompts and inputs never contribute to external model training.

• Security & Privacy Commitments – Requiring contractual guarantees around data handling, storage, and retention.

• Compliance Certifications – Verifying providers meet GDPR, SOC 2, and other regulatory standards.

3. Bring Your Own LLM API Key (BYOK)

For companies with stricter compliance requirements, BYOK provides an additional layer of security and control. Instead of using a shared API key for all users, customers can bring their own API credentials, ensuring:

🔹 Full Control Over Data Flow – Customers directly manage their OpenAI, Azure, or other LLM provider settings.
🔹 Independent Compliance Management – Organizations can enforce their own retention policies and security controls.
🔹 Separation of Customer Data – Eliminating the risk of cross-tenant data exposure.

By supporting BYOK, Bilanc enables enterprise clients to integrate AI securely within their own compliance framework, ensuring regulatory alignment without limiting access to powerful LLM-driven insights.

Conclusion

At Bilanc, we take data privacy and compliance seriously. By eliminating PII from requests, enforcing strict data retention policies, and offering BYOK for greater control, we ensure our AI-powered engineering management platform remains GDPR-compliant while delivering valuable insights.

If you’re interested in seeing how Bilanc’s privacy-first AI approach works, book a demo today!