AI Data Loss Prevention via Word Embeddings
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Solution Overview
Problem
Traditional data loss prevention solutions are inadequate for addressing the risks associated with the use of artificial intelligence tools, particularly generative large language models, as they struggle to identify and prevent the leakage of sensitive information in evolving and complex communication threads, and fail to track the output of these tools effectively.
Innovation Solution
The implementation of a data loss prevention policy that utilizes word embeddings and classifications to identify and track sensitive information, intercepting communications between client devices and AI tools, creating identifiers for sensitive content, and storing them in a tracking database to enforce compliance with data handling policies, thereby preventing unauthorized transmission or usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data loss prevention solutions are used, then existing data security needs are met, but they fail to detect and prevent sensitive information leakage in AI tool communications
Solution Approach 1:
The patent transforms sensitive data patterns into word embeddings (vector representations) and compares them using similarity metrics. This parameter transformation from raw text to embedding space enables detection of sensitive information in AI communications while maintaining detection accuracy and adapting to new AI tool formats.
Solution Approach 2:
The patent introduces an intermediary data loss prevention service that sits between client devices and AI tool interfaces. This intermediary intercepts communications, applies embedding-based detection, and prevents sensitive data transmission without requiring changes to the AI tools themselves, thus improving adaptability.
2Reliability
If comprehensive monitoring of AI tool communications is implemented, then sensitive information leakage is detected, but system complexity increases
Solution Approach 1:
The system uses self-service by leveraging pre-trained word embedding models that automatically capture semantic meaning of sensitive data patterns. The embedding comparison mechanism autonomously identifies sensitive information without requiring manual rule updates or complex configuration, reducing system complexity while maintaining prevention effectiveness.
3Measurement precision
If real-time analysis of communication threads is performed, then sensitive data is identified promptly, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing embedding comparison only on relevant portions of AI communications that may contain sensitive data. Rather than analyzing entire communication threads uniformly, the system selectively applies detection to suspicious segments, reducing processing time while maintaining detection precision through targeted embedding similarity checks.
Data Source
AI summary
The disclosed technology addresses the need in the art for a data loss prevention policy that is adapted to new and evolving uses of artificial intelligence tools, such as generative large language models. The present technology can use techniques such as word embeddings, or classifications using artificial intelligence tools to identify leakage of sensitive information in the context of generative large language models. The present technology can also identify and track the use of content created by artificial intelligence tools for uses within an organization.