Document Semantic Representation for Data Security Risk Analysis

The patent replaces traditional mechanical NLP approaches (word-counts, topic models) with a deep learning-based semantic embedding system. The system uses pre-trained language models (such as BERT, RoBERTa, or other transformer-based models) to generate semantic representations of documents, enabling accurate identification of sensitive content even when traditional keyword-based methods fail due to synonymy, polysemy, or contextual variations.

The patent introduces semantic embeddings as an intermediary layer between document content and security policies. Instead of directly analyzing raw text or relying solely on metadata, the system transforms documents into semantic representations that capture meaning, enabling intelligent access control decisions that consider both content semantics and user context, thereby improving reliability while maintaining operational ease through automated processing.

The patent employs pre-trained language models that have been previously trained on large corpora of text data. These pre-trained models capture linguistic patterns, synonyms, and contextual relationships before being applied to security analysis tasks. By leveraging this preliminary training, the system achieves high accuracy in semantic content analysis without requiring computationally intensive training processes during actual security assessments, thus reducing operational computational requirements.

The patent segments text processing into multiple levels: tokenization, sentence segmentation, and document-level semantic representation. Instead of treating words as atomic units, the system breaks down text into sentences and then generates semantic embeddings that capture the meaning of entire sentences and documents. This segmentation approach maintains processing manageability while significantly improving the ability to capture contextual meaning through hierarchical analysis.