Building Multi-Tenant AI SaaS Without the Data Privacy Nightmares
This article discusses the challenges of adding data privacy and protection to AI systems, and presents a solution using an LLM-based detection and masking approach.
Why it matters
This solution addresses a critical challenge for AI teams, enabling them to add production-grade data privacy to their systems quickly and cost-effectively.
Key Points
- 1Traditional data masking tools are not designed for the pace and complexity of modern AI systems
- 2LLM-based PII detection can identify sensitive information more accurately than regex-based approaches
- 3Context-aware masking preserves the semantic meaning of data while protecting sensitive details
- 4The solution provides compliance features like audit logging, policy management, and multi-tenancy
Details
The article outlines the specific challenges of adding data privacy to AI systems, which involve multiple layers of data processing (input, logging, vector databases, fine-tuning, evaluation) and unstructured data formats. Traditional masking tools struggle with these requirements, leading teams to either ship unprotected data or build custom solutions. The article presents a solution built by Protecto that uses LLM-based PII detection to identify sensitive information with high accuracy, and context-aware masking to preserve data utility. The solution also includes compliance features like audit logging, policy management, and multi-tenancy. Real-world results show the system processing over 50 million API calls per month with 99%+ accuracy and low latency.
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