Dynamic Threat Protection via Real-Time Contextual Analysis
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Solution Overview
Problem
Static threat protection policies in modern systems become ineffective due to dynamic changes in service providing systems, such as user authentication, reputation changes, and IP address fluctuations, leading to resource-intensive and marginally effective guesswork by threat protection systems in selecting the correct policy.
Innovation Solution
A dynamic threat protection system that receives real-time contextual data from various sources, analyzes it to determine a security threat score, and automatically applies a security policy based on this score, using an analyzing module and a mitigation device to provide adaptive protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static policies are used to protect nodes in a service providing system, then the policy definition is simple and manual application is straightforward, but the policy becomes ineffective over time due to dynamic changes in the system state
Solution Approach 1:
The patent implements dynamic threat protection policies that automatically adapt to changing system states. Instead of static policies, the system continuously monitors contextual data (user authentication status, device reputation, location, network conditions) and dynamically adjusts security policies in real-time. This resolves the contradiction by making the policy system responsive to dynamic changes while maintaining automated operation.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring contextual data from multiple sources and using this information to adjust security policies. The threat protection system receives feedback about system state changes (authentication events, reputation updates, roaming status) and automatically modifies policies based on this feedback, ensuring ongoing effectiveness without manual intervention.
2Reliability
If threat protection systems attempt to automatically ascertain the correct policy through active challenges and passive observation, then the system can adapt to dynamic changes, but the process becomes resource-intensive and only marginally effective
Solution Approach 1:
The system performs preliminary actions by pre-establishing security policies for different contextual scenarios and threat levels. Instead of reacting to each change through resource-intensive analysis, the system has pre-defined policies ready for immediate application when specific contextual conditions are detected, reducing real-time computational burden while maintaining effectiveness.
Solution Approach 2:
The system changes parameters by adjusting security policy parameters based on contextual threat scores rather than performing complete policy re-evaluations. The threat protection system modifies specific policy parameters (access permissions, monitoring intensity, challenge requirements) based on calculated threat levels, reducing computational resources needed compared to comprehensive policy analysis.
3Ease of operation
If static policies are manually applied, then the implementation is straightforward, but the policies cannot keep up with constant flux in services, clients, and servers
Solution Approach 1:
The threat protection system performs self-service by automatically monitoring contextual data, evaluating threat levels, and applying appropriate security policies without manual intervention. The system serves itself by autonomously adapting to system changes, eliminating the need for manual policy updates while maintaining ease of operation through automated decision-making processes.
Solution Approach 2:
The system implements dynamic policy application that automatically responds to changing system conditions. Security policies are not statically applied but dynamically adjusted based on real-time contextual data about user authentication, device reputation, network location, and other system state factors, ensuring continuous adaptability while maintaining automated operation.
Data Source
AI summary
Methods and systems for dynamic threat protection are disclosed. An example method for dynamic threat protection may commence with receiving real-time contextual data from at least one data source associated with a client. The method may further include analyzing the real-time contextual data to determine a security threat score associated with the client. The method may continue with assigning, based on the analysis, the security threat score to the client. The method may further include automatically applying a security policy to the client. The security policy may be applied based on the security threat score assigned to the client.