Real-Time Semantic Analyzer for Compliance Enforcement
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Solution Overview
Problem
Existing compliance mechanisms in companies are ineffective in preventing non-compliant messages from being sent, as they rely on employee self-policing and typically analyze communications after the fact, failing to prevent potential policy, compliance, or legal violations.
Innovation Solution
A system and method that utilizes a semantic analyzer to parse documents in real-time, assess sentiment against a policy model, and disable save functionality if a violation threshold is exceeded, while also allowing for policy model training using feedback from reviewers to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time semantic analysis is implemented to prevent non-compliant messages, then compliance effectiveness is improved, but system complexity increases
Solution Approach 1:
The system performs semantic analysis of messages before they are transmitted or stored, preventing non-compliant content from being recorded. The save functionality is disabled in real-time when policy violations are detected, rather than analyzing communications after they have been sent.
Solution Approach 2:
A semantic analyzer acts as an intermediary component between the messaging system and storage functionality. This analyzer evaluates message content against policy models and controls whether the save function is enabled or disabled, serving as a mediator that resolves the conflict between communication freedom and compliance requirements.
2Measurement precision
If continuous monitoring and real-time analysis are performed on all communications, then policy violation detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system establishes policy models and violation thresholds in advance through training phases. During runtime, the semantic analyzer applies these pre-established models to messages, enabling rapid classification without performing complex analysis on every single message from scratch.
Solution Approach 2:
The system uses configurable violation thresholds and adjustable scoring parameters that can be modified based on organizational needs. The semantic analyzer assigns scores to different message attributes and compares them against configurable thresholds, allowing flexibility in balancing detection accuracy with processing efficiency.
3Object-affected harmful factors
If the save function is disabled to prevent non-compliant message storage, then compliance risk is reduced, but user productivity decreases
Solution Approach 1:
The save functionality is dynamically enabled or disabled based on real-time analysis of message content. When messages comply with policies, the save function remains enabled allowing normal operations. When policy violations are detected, the save function is temporarily disabled only for problematic content, rather than imposing a blanket restriction on all user activities.
Solution Approach 2:
The system provides automated policy enforcement without requiring manual intervention from compliance officers for each message. The semantic analyzer independently evaluates content and controls save functionality, reducing the need for human review of compliant messages while maintaining oversight of potentially problematic content.
Data Source
AI summary
This disclosure describes systems, methods, and apparatus that monitor any manifestation of an idea, such as typed, written, or verbal message or document creation (e.g., while a user types an email or instant message, or makes a phone call) and analyze the manifestation in real-time to extract a sentiment and based on this sentiment, determine if the idea(s) manifested in the message, document, or other medium poses a risk of violating compliance, policy, or law.