Email Topic Shift Detection for Stealthy Threat Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional cybersecurity defenses are inadequate against sophisticated cyber threats that evade signature-based detection by embedding malicious payloads within benign communications, leading to analyst fatigue and increased risk of undetected attacks.
Innovation Solution
An AI-based cyber security system utilizing a topic shift analysis module to calculate a topic shift score by comparing a communication's lexical profile to a historical profile, with an assessment module determining anomalies and an autonomous response module taking mitigation actions, including data parsing and a security mailbox assistant for user-submitted analyses, within a high-availability DLP architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signature-based detection methods are used, then known threats can be identified and blocked, but sophisticated cyber threats that embed malicious payloads within benign communications cannot be detected
Solution Approach 1:
The system changes the detection parameters from static signatures and keywords to dynamic lexical profiles that capture the statistical distribution of words. By comparing the lexical profile of communications against a user's historical pattern, the system can detect subtle deviations indicating malicious content, even when no known signatures exist. This parameter transformation enables detection of novel attacks while maintaining reliability through statistical significance thresholds.
Solution Approach 2:
The patent replaces traditional mechanical signature-matching systems with an AI-based lexical analysis system. Instead of mechanically searching for known threat patterns, the system uses machine learning models to analyze the semantic and statistical characteristics of communications, substituting the mechanical detection process with intelligent pattern recognition that adapts to evolving threats.
2Measurement precision
If manual investigation of every potential threat is performed, then thorough analysis can be conducted, but analyst fatigue increases and sophisticated attacks may go unnoticed
Solution Approach 1:
The system performs self-service by automatically generating lexical profiles for users based on their historical communications and continuously monitoring new communications against these profiles. The AI system autonomously identifies anomalies and generates alerts, eliminating the need for manual analysis of every communication while maintaining thorough detection capability through automated statistical comparison.
Solution Approach 2:
The system performs preliminary action by pre-establishing lexical profiles for each user based on their historical communication patterns before any potential attack occurs. These baseline profiles are continuously updated and used for real-time anomaly detection, allowing the system to prepare detection mechanisms in advance and respond automatically to deviations without requiring manual investigation of each communication.
3Difficulty of detecting and measuring
If high-volume alerts are generated for security analysis, then potential threats can be identified, but false positives increase and security teams become overwhelmed
Solution Approach 1:
The system incorporates feedback mechanisms where the lexical profiles are continuously updated based on actual user behavior patterns. When anomalies are detected, the system can adjust thresholds and refine profiles, creating a feedback loop that improves detection accuracy over time. This adaptive feedback reduces false positives by learning legitimate user variations while maintaining sensitivity to actual threats.
Solution Approach 2:
The patent replaces mechanical alert-generation systems with AI-based statistical analysis that inherently filters false positives. By using machine learning to understand normal user behavior patterns and calculating statistical significance of deviations, the system automatically reduces noise and generates only truly anomalous alerts, substituting brute-force monitoring with intelligent filtering.
Data Source
AI summary
Systems and methods for protecting electronic communications are described. A cyber security appliance may be configured to calculate a topic shift score for a communication by comparing a first lexical profile derived from the communication to a historical lexical profile established for an associated user. This analysis may be performed without using a large language model. The system may also parse communications to extract sensitive data and content from attachments, performing behavioral modeling on the extracted data. Based on the analysis, an autonomous response module may take a variety of mitigation actions. Furthermore, a security mailbox assistant module may perform a secondary, in-depth analysis on user-submitted communications and generate a deterministic report. For outbound communications, a data loss prevention architecture may divert messages for in-line analysis and may include a fail-safe timeout mechanism to ensure service continuity.