Adaptive Trust Profile Generation for Security Analytics
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Solution Overview
Problem
Existing security systems lack effective methods to adaptively mitigate risks associated with entity behavior changes and malicious intent, particularly in cases where users or entities may be compromised or radicalized due to internal or external factors.
Innovation Solution
A method and system for generating a prepopulated adaptive trust profile by accessing and identifying relevant adaptive trust profile data, using entity characteristics to create a profile that can adapt to changes in user or entity behavior, incorporating user and non-user entity profiles, and employing a security analytics system to assess and respond to risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static security profiles are used, then system simplicity is maintained, but the ability to adapt to changing user behavior and detect malicious intent deteriorates
Solution Approach 1:
The patent implements dynamic trust profiles that automatically adjust based on observed user behavior patterns. The system continuously monitors user actions and modifies trust scores in real-time, transforming static security profiles into adaptive systems that respond to behavioral changes without requiring manual intervention.
Solution Approach 2:
The system performs self-learning by automatically analyzing user behavior patterns and generating trust profiles without external input. The machine learning algorithms autonomously identify malicious intent and adjust security parameters, enabling the system to service itself through continuous behavioral analysis.
2Measurement precision
If comprehensive behavioral analysis is performed, then detection precision of malicious intent is improved, but processing time and computational resources increase
Solution Approach 1:
The system pre-computes baseline behavior patterns and trust scores during periods of normal operation. By establishing expected behavior profiles in advance, the system can quickly compare actual user actions against these pre-analyzed patterns during security events, significantly reducing real-time processing requirements while maintaining high detection precision.
Solution Approach 2:
The patent replaces traditional rule-based mechanical security checks with machine learning algorithms that automatically analyze behavioral patterns. This substitution enables the system to process complex behavioral data more efficiently, achieving high detection precision without linear increases in processing time.
3Reliability
If real-time adaptive profiling is implemented, then security response effectiveness is improved, but system resource consumption increases
Solution Approach 1:
The system implements periodic behavior analysis at strategically determined intervals rather than continuous monitoring. Trust profiles are updated at optimal moments based on behavioral significance thresholds, reducing computational resource consumption while maintaining security reliability through timely detection of malicious intent.
Solution Approach 2:
The patent applies different levels of analysis intensity to different user contexts and risk levels. High-value targets or suspicious behaviors receive more intensive real-time analysis, while normal low-risk activities use lighter monitoring, optimizing resource allocation across the system while maintaining overall security reliability.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for generating a prepopulated adaptive trust profile via an adaptive trust profile operation. In various embodiments the adaptive trust profile operation includes: receiving a request to generate a prepopulated adaptive trust profile for a target entity; accessing adaptive trust profile data, the adaptive trust profile data comprising a plurality of adaptive trust profiles; identifying an adaptive trust profile relevant to the entity from the plurality of adaptive trust profiles, the adaptive trust profile relevant to the entity comprising at least one substantively similar entity characteristic to an entity characteristic of the target entity; and, generating an adaptive trust profile for the target entity using the adaptive trust profile relevant to the target entity.