Dynamic Policy Injection for Threat Detection
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Solution Overview
Problem
Current network security systems face challenges in effectively managing and visualizing vast amounts of security data to detect evolving threats in real-time, as static security rules fail to keep pace with dynamic user and application activities, leading to overwhelming alarms and resource constraints.
Innovation Solution
A threat intelligence platform that employs dynamic policies for real-time threat detection and analytics, providing a consolidated view of active threats and user activity, and enables adaptive authorization, content inspection, and enforcement through a policy bus for dynamic policy injection and communication across multiple enforcement entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static security rules are used, then device complexity is reduced, but adaptability to evolving threats deteriorates
Solution Approach 1:
The patent implements dynamic security policies that automatically adapt to evolving threats based on real-time user behavior patterns and contextual information. The system transitions from static rules to dynamic decision-making where policies are continuously updated based on monitored user activities, device characteristics, and threat intelligence, enabling the security system to respond to new threats without manual rule updates.
Solution Approach 2:
The system performs self-learning and self-adjustment by automatically analyzing user behavior patterns, device fingerprints, and security events to generate and refine security policies. The machine learning components enable the system to autonomously identify anomalies and update access control decisions without requiring continuous manual intervention, reducing operational complexity while improving adaptability.
2Reliability
If real-time threat detection is implemented, then security effectiveness is improved, but data processing volume increases
Solution Approach 1:
The patent extracts and focuses analysis on critical security-relevant data elements such as user behavior patterns, device fingerprints, and contextual metadata, rather than processing all raw data uniformly. By selectively extracting only the necessary features for threat detection, the system maintains high security effectiveness while reducing the overall data processing burden through intelligent data filtering and feature selection.
Solution Approach 2:
The system performs preliminary data processing and feature extraction at data collection points, pre-computing security-relevant attributes and filtering out irrelevant data before it enters the main analysis pipeline. This preliminary action reduces the volume of data requiring real-time processing while preserving the essential information needed for effective threat detection.
3Difficulty of detecting and measuring
If comprehensive security monitoring is deployed, then threat detection capability is improved, but operational burden on security personnel increases
Solution Approach 1:
The system implements continuous feedback loops where security events, user behaviors, and threat intelligence automatically trigger policy updates and alert security personnel only when anomalies are detected. The machine learning models continuously learn from security outcomes, automatically adjusting detection thresholds and priorities, which reduces the operational burden by eliminating routine manual analysis while maintaining high threat detection capability.
Solution Approach 2:
The patent introduces machine learning models and automated policy engines as intermediaries between raw security data and human operators. These intermediaries perform complex analysis, pattern recognition, and initial response actions, transforming the operational burden from manual data analysis to supervised review of automated decisions, thereby reducing the direct operational burden on security personnel.
4Adaptability or versatility
If dynamic policies are implemented, then adaptability to user activity is improved, but system complexity increases
Solution Approach 1:
The patent segments the dynamic policy system into modular components including user behavior analysis modules, device fingerprinting modules, contextual analysis modules, and policy enforcement modules. Each module handles specific aspects of adaptability independently, making the overall complex system more manageable and easier to deploy. This segmentation allows each component to be optimized and maintained separately while contributing to the overall adaptability.
Data Source
AI summary
The present disclosure relates generally to threat detection, and more particularly, to techniques for analyzing security events using dynamic policies and displaying a consolidated view of active threats and user activity including the dynamic policies being triggered by the active threats and user activity. Some aspects are directed to the concept of a policy bus for injecting and communicating the dynamic policies to multiple enforcement entities and the ability of the entities to respond to the policies dynamically. Other aspects are directed providing a consolidated view of active threat categories, a count of policies being triggered for each threat category, and associated trends. Yet other aspects are directed to providing a consolidated view of users, applications being accessed by users, and the access policies, if any, implicated by the such accesses.