Credential Sharing Detection via Non-Public Data Access

The patent replaces manual security verification processes with an automated machine learning-based threat level assessment system. The system automatically evaluates access requests by analyzing user behavior patterns, device characteristics, and access context through algorithms, eliminating the need for manual security checks while maintaining high security standards. This substitution of mechanical/manual verification with automated computational analysis resolves the contradiction by providing both high security and ease of operation.

The patent dynamically adjusts security parameters based on the assessed threat level of each access request. Instead of applying uniform high-security protocols to all access attempts, the system modifies security measures according to the specific risk assessment results. For low-threat requests from trusted third parties, security barriers are reduced or removed, while high-threat requests trigger enhanced security protocols. This parameter-based adaptation resolves the contradiction by making security proportional to actual risk rather than universally restrictive.

The patent segments the threat detection system into distinct functional modules: a machine learning model for threat level assessment, an access analysis component for evaluating access requests, and a decision-making component for determining access outcomes. Each module performs a specific function and can be independently developed, tested, and maintained. This segmentation improves detection accuracy through specialized algorithms while managing system complexity by organizing functions into manageable, modular components with clear interfaces.

The patent introduces an intermediary machine learning model that acts as a mediator between raw access request data and security decisions. The ML model processes and interprets complex access patterns, device information, and user behavior data, transforming them into a simplified threat level assessment. This intermediary layer handles the complexity of analysis while presenting simplified results to the access control system, thereby improving detection accuracy without proportionally increasing overall system complexity.