AI Security Breach Detection in Cloud Environments
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Solution Overview
Problem
Cloud-based environments face challenges in detecting and mitigating security breaches due to their dynamic and shared nature, which increases the complexity of monitoring and analyzing vast amounts of data.
Innovation Solution
A method utilizing a trained artificial intelligence (AI) model to analyze event data from client devices in a cloud-based environment, identifying activities indicative of security breaches and recommending appropriate security actions with confidence levels, enabling automated mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and analysis of vast amounts of data is performed, then detection accuracy may be maintained, but the complexity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual monitoring and analysis (mechanical human operation) with an automated AI-based system that uses machine learning models to analyze event data, network traffic, and logs. This substitution maintains high detection accuracy while significantly reducing operational complexity and time consumption.
Solution Approach 2:
The system implements self-service through automated threat detection and response capabilities. The AI model autonomously analyzes security events, identifies potential breaches, and triggers mitigation actions without requiring constant human intervention, thereby reducing monitoring complexity while maintaining detection precision.
2Reliability
If comprehensive monitoring of all client devices is implemented, then security coverage is improved, but the time and resources required for analysis increase
Solution Approach 1:
The system performs preliminary action by continuously collecting and pre-processing event data from all client devices, maintaining ready-to-analyze datasets of network traffic, logs, and security events. This preparation enables rapid response to security threats without requiring time-consuming analysis when breaches occur, thus improving security coverage while minimizing analysis time.
Solution Approach 2:
Automated AI algorithms replace manual analysis of comprehensive monitoring data, enabling the system to process vast amounts of security information from all client devices simultaneously. This substitution maintains complete security coverage while dramatically reducing the time required to analyze and respond to threats.
3Speed
If automated security response actions are executed, then response speed is improved, but the risk of false positive actions increases
Solution Approach 1:
The system implements feedback mechanisms where the AI model continuously learns from the outcomes of security actions taken. By analyzing whether automated responses successfully mitigated threats or caused false positives, the system refines its decision-making algorithms, maintaining rapid response speeds while improving action accuracy over time through iterative learning.
Solution Approach 2:
The automated response system dynamically adjusts its behavior based on confidence levels and threat assessments. The AI model can escalate uncertain cases for human review while automatically handling high-confidence threats, creating a dynamic response mechanism that maintains speed for clear cases while ensuring accuracy for ambiguous situations.
Data Source
AI summary
Methods and systems for security breach detection and mitigation in a cloud-based environment are provided herein. Event data associated with client devices of a cloud-based environment are provided as input to a trained artificial intelligence (AI) model. The event data indicates activities performed with respect to the client devices. One or more outputs of the AI model are obtained, the one or more outputs indicating activities, of the event data, that is indicative of a security breach, one or more security actions to be taken at the cloud-based environment in response to the activities, and for each of the one or more security actions, a level of confidence that a respective security action will mitigate the security breach. A security action having a level of confidence that satisfies a confidence criterion is determined. A set of operations to initiate the determined security action at the cloud-based environment is performed.