Adaptive Data Security via Machine Learning
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Solution Overview
Problem
Current security measures are inadequate in detecting and preventing insider threats to data security, as they often rely on one-size-fits-all approaches that fail to account for the nuanced and unintentional risks posed by insiders, leading to difficulties in prevention and detection.
Innovation Solution
A system and method utilizing machine-readable instructions and a machine learning controller to process data payloads with originating node attributes, infosec data attributes, and biometric enterprise attributes, creating an infosec control attribute to dynamically adjust network security controls in real-time, incorporating deep learning techniques and neural networks to identify and respond to potential security incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional one-size-fits-all security measures are used, then implementation is simple and cost-effective, but detection precision and adaptability to insider threats deteriorate
Solution Approach 1:
The patent implements dynamic security controls that automatically adjust based on real-time behavioral analysis and risk assessment. The system transitions from static, one-size-fits-all security measures to dynamic, adaptive controls that modify security parameters according to detected anomalies and user behavior patterns, thereby improving detection precision without requiring complete system redesign
Solution Approach 2:
The system changes security parameters dynamically based on analyzed data. By monitoring behavioral attributes and computing risk scores, the system adjusts security control parameters in real-time, transforming fixed security configurations into adaptable ones that respond to changing threat conditions, thus enhancing detection capability while managing complexity through automated parameter adjustment
2Adaptability or versatility
If traditional security measures are used, then system complexity is low, but adaptability to nuanced insider threats deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing behavioral data, computing risk scores, and adjusting security controls without requiring manual intervention for each security decision. The automated machine learning models continuously learn from data and adapt security parameters independently, enabling the system to handle nuanced insider threats autonomously while keeping operational complexity manageable
Solution Approach 2:
The patent implements continuous feedback loops where security outcomes are monitored, analyzed, and used to refine future security decisions. The system collects data on security events and outcomes, feeds this information back to the machine learning models, and uses the learned patterns to improve adaptability over time, creating a self-improving security system that handles complexity through iterative learning
3Reliability
If real-time behavioral analysis is implemented, then detection capability improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing behavioral data in the background, building profiles and establishing baselines before security events occur. This advance preparation allows the system to quickly evaluate security incidents in real-time without performing heavy analysis from scratch, thereby maintaining high detection capability while reducing actual processing time during security events
Solution Approach 2:
The patent applies partial analysis by focusing computational resources on the most relevant and suspicious attributes rather than analyzing all data equally. The system identifies and prioritizes key behavioral indicators that are most predictive of insider threats, applying intensive analysis only where needed while using simpler rules for routine monitoring, thus balancing detection capability with processing efficiency
Data Source
AI summary
Methods, systems, and computing platforms for data communication are disclosed. Exemplary implementations may: electronically process with a machine learning controller; electronically process the data payloads in the network with deep machine learning; and real-time adjusting of a plurality of network infosec controls associated with the originating node attribute based on the infosec control attribute.