Threat Mitigation Platform with AI/ML Detection Module Rollouts
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Solution Overview
Problem
The increasing complexity of computer attacks necessitates advanced threat mitigation systems that leverage Artificial Intelligence (AI) and Machine Learning (ML) to effectively detect and respond to security events in computing platforms.
Innovation Solution
A threat mitigation platform incorporating AI/ML-based probabilistic processes to analyze unstructured data, detect security events, and provide a rollout schedule for threat detection capability modules, enabling graphical or text-based updates and user interface views to clients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security systems are used, then device complexity is low, but the ability to detect and respond to complex attacks is insufficient
Solution Approach 1:
The threat mitigation system is divided into multiple independent threat detection capability modules, each specializing in detecting specific types of threats. These modular components can be selectively deployed and managed independently, allowing the system to scale complexity only where needed while maintaining overall system manageability.
Solution Approach 2:
The system performs preliminary analysis and classification of threats using AI/ML models before full detection and response actions are initiated. Probabilistic processes pre-assess incoming data to determine threat likelihood, enabling the system to prepare appropriate response measures in advance rather than reacting to every event in real-time.
2Measurement precision
If AI/ML-based threat detection is implemented, then threat detection precision improves, but processing time and computational resources increase
Solution Approach 1:
The system applies AI/ML-based probabilistic detection only to suspicious or anomalous events rather than processing all incoming data with full computational intensity. Normal traffic flows through lightweight filtering rules, while only potentially threatening events trigger the more computationally demanding AI/ML analysis, reducing overall processing time while maintaining high detection precision for critical threats.
3Adaptability or versatility
If multiple threat detection capability modules are deployed, then threat detection versatility improves, but system complexity and deployment difficulty increase
Solution Approach 1:
The system provides dynamic control over threat detection module deployment through rollout schedules that can be adjusted based on organizational needs, threat landscapes, and resource availability. Modules can be incrementally activated or deactivated without requiring complete system reconfiguration, allowing versatile threat detection capabilities to be implemented in a controlled, manageable manner.
Data Source
AI summary
A computer-implemented method, computer program product and computing system for: defining a threat mitigation platform for a client, wherein the threat mitigation platform includes a plurality of threat detection capability modules; defining a rollout schedule for at least a portion of the plurality of threat detection capability modules; and presenting the rollout schedule to the client.