AI Threat Mitigation Platform for Security Coverage Gaps
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Solution Overview
Problem
The complexity of computer attacks is increasing, making it difficult for good actors to effectively prevent and mitigate threats using existing technologies, and there is a need for advanced methods to analyze and address these threats efficiently.
Innovation Solution
A threat mitigation system utilizing Artificial Intelligence (AI) and Machine Learning (ML) to process large quantities of unstructured data, identify inefficiencies and coverage gaps, and provide recommendations for mitigating these issues by deploying undeployed rules and optimizing platform utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing technologies are used to prevent and mitigate threats, then the system can operate with current tools and methods, but the system cannot effectively address the increasing complexity of computer attacks
Solution Approach 1:
The patent replaces traditional mechanical security analysis methods with AI and Machine Learning systems that can automatically process and analyze unstructured data, security logs, and threat patterns. This substitution enables the system to handle increasing attack complexity by using intelligent algorithms rather than conventional rule-based approaches.
Solution Approach 2:
The system changes the parameters of threat analysis by incorporating unstructured data processing capabilities and using ML models that can adapt to new attack patterns. This allows the system to maintain reliability against increasingly complex attacks by dynamically adjusting analysis parameters and learning from new threat vectors.
2Loss of information
If large quantities of unstructured data are processed to identify threats, then the system can extract non-obvious information, but the processing complexity and computational resources increase
Solution Approach 1:
The patent employs AI and Machine Learning systems to replace manual or rule-based processing of unstructured data. These intelligent systems can automatically extract meaningful information from complex data sources such as security logs, network traffic, and threat intelligence feeds, reducing the need for manual analysis while improving information extraction capability.
Solution Approach 2:
The ML-based system performs self-learning and automatic pattern recognition without requiring extensive manual configuration. The system autonomously processes unstructured data, identifies threat patterns, and improves its analysis capabilities over time, reducing the operational complexity despite handling large data volumes.
3Reliability
If AI and Machine Learning are used to process security data, then the system can gain the upper hand in threat detection, but the implementation complexity and resource requirements increase
Solution Approach 1:
The patent implements a unified AI/ML platform that performs multiple security functions including threat detection, pattern recognition, anomaly detection, and predictive analysis. This multi-functional system consolidates various security operations into a single intelligent platform, improving threat detection capability while managing implementation complexity through integration.
Solution Approach 2:
The system introduces AI/ML models as intermediary layers between raw security data and decision-making processes. These intelligent intermediaries automatically process and interpret complex data patterns, providing actionable insights without requiring direct human analysis of every data point, thus improving detection reliability while managing system complexity.
4Reliability
If coverage gaps and inefficiencies are identified in the computing platform, then security vulnerabilities can be addressed, but the system requires additional resources to deploy undeployed rules
Solution Approach 1:
The patent uses ML models to perform preliminary analysis and identification of coverage gaps and inefficiencies before deploying additional security rules. By proactively detecting vulnerabilities and prioritizing them based on risk assessment, the system can strategically deploy resources to address the most critical gaps first, improving security coverage while optimizing resource utilization.
Solution Approach 2:
The system implements a prioritized approach to deploying security rules, focusing on addressing the most critical coverage gaps rather than attempting to deploy all possible rules simultaneously. This partial action strategy allows the system to improve security coverage effectively while managing computational resources by concentrating efforts on high-impact areas.
Data Source
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AI summary
A computer-implemented method, computer program product and computing system for: obtaining consolidated platform information to identify current security-relevant capabilities for a computing platform; identifying coverage gaps in the current security-relevant capabilities; and providing one or more recommendations concerning how to mitigate the coverage gaps.