Autonomous AI Agent Analysis for Validated Malicious Entity Detection
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Solution Overview
Problem
Conventional cybersecurity techniques for detecting malicious activities often result in a high number of false positives, diverting resources away from critical security matters.
Innovation Solution
Utilizing autonomous AI agents that selectively employ multiple AI tools to analyze relevant data, including random forest learning, isolation forest anomaly detection, and SVM classification, to accurately determine malicious behavior, with additional validation by multiple agents to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques are used for detecting malicious activities, then detection coverage is maintained, but false positives increase substantially
Solution Approach 1:
The patent divides the malicious activity detection process into multiple specialized autonomous AI agents, each responsible for specific detection tasks. This segmentation allows each agent to focus on particular patterns or types of malicious behavior, improving overall detection accuracy while reducing false positives through specialized analysis rather than generic detection.
Solution Approach 2:
The patent introduces autonomous AI agents as intermediaries between raw data and final detection conclusions. These agents process and interpret data through multiple layers of analysis, including validation agents that verify findings before final classification. This intermediary layer reduces false positives by adding verification steps and multiple perspectives to the detection process.
2Measurement precision
If multiple AI tools are used by autonomous agents, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent creates autonomous AI agents that can perform multiple functions - detection, analysis, validation, and classification - within a single unified framework. Each agent is designed to handle various aspects of malicious activity detection using multiple AI tools, reducing the need for separate specialized systems while maintaining high detection accuracy through multi-functional capabilities.
Solution Approach 2:
The patent combines multiple AI tools and detection methods into integrated autonomous agents that work together as unified entities. Rather than operating as separate complex systems, the AI tools are merged within agents that coordinate their functions, simplifying the overall system architecture while preserving the benefits of using multiple detection approaches.
3Reliability
If autonomous AI agents validate findings through multiple agents, then false positives are reduced, but processing time increases
Solution Approach 1:
The patent implements preliminary filtering and pre-validation mechanisms where agents perform initial assessments and preliminary validation of findings before full multi-agent verification is required. This preliminary action reduces the burden on subsequent validation agents by pre-processing data and identifying obvious cases, thereby reducing overall validation time while maintaining reliability.
Solution Approach 2:
The patent applies partial validation approaches where not all findings require full multi-agent verification. Low-risk or clearly identifiable malicious activities may undergo simplified validation processes, while only uncertain or high-stakes determinations receive exhaustive multi-agent validation. This selective approach reduces average processing time while maintaining high reliability for critical detections.
Data Source
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AI summary
Techniques are described herein that are capable of performing entity maliciousness analysis using autonomous AI agents. A first autonomous AI agent selects relevant data from a corpus of data using a first selected AI tool in an AI model as a result of the relevant data being associated with an entity. A second autonomous AI agent generates a maliciousness determination, which indicates whether the entity exhibits malicious behavior, by analyzing the relevant data using a second selected AI tool in the AI model. A third autonomous AI agent generates a validity determination, which indicates whether the maliciousness determination is valid, by analyzing the maliciousness determination using a third selected AI tool in the AI model. As a result of an analysis that takes into consideration the validity determination, execution of an instruction that causes a security action to be performed with regard to the entity is triggered.