Cloud ML Breach Prediction via Deception Tech
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Solution Overview
Problem
The increasing complexity of cyber threats and the expansion of attack surfaces due to remote work and reliance on public cloud services have made it challenging for organizations to effectively predict and prevent breaches, especially with traditional security measures being inadequate in detecting sophisticated threats.
Innovation Solution
A cloud-based system utilizing machine learning to predict breaches by leveraging vast data from cloud security services, integrated with deception technology that includes dynamic risk scoring and targeted threat detection using breadcrumbs/honeypots, allowing for proactive identification and mitigation of potential threats without relying on on-premises appliances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are used, then device complexity is reduced, but breach detection capability deteriorates against sophisticated threats
Solution Approach 1:
The patent replaces traditional mechanical/security-based detection systems with machine learning models that process vast amounts of cloud data to predict breaches. The ML model analyzes patterns from cloud security services, endpoint security, and threat intelligence to identify sophisticated threats that traditional rules-based systems miss, thereby improving detection capability while managing complexity through automation.
Solution Approach 2:
The patent introduces an intermediary layer - a cloud-based ML prediction system - between traditional security measures and actual breach detection. This intermediary processes data from multiple cloud security services and provides enriched threat predictions, enabling traditional systems to detect sophisticated threats without requiring them to directly handle the complexity of modern attack vectors.
2Reliability
If more cloud services are integrated for comprehensive security, then threat detection capability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal cloud-based security platform that integrates multiple security functions including endpoint security, web gateway, cloud access security broker, and threat intelligence into a single unified system. The ML model serves as a common processor that analyzes data from all these services, providing comprehensive threat detection while managing integration complexity through a centralized architecture.
Solution Approach 2:
The patent merges multiple cloud security services and data sources into a unified ML prediction system. By combining endpoint security data, web gateway logs, cloud access security broker information, and threat intelligence feeds into a single analytical platform, the system achieves comprehensive threat detection without requiring separate complex integration layers for each service.
3Loss of time
If proactive breach prediction is implemented, then breach response time is improved, but data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by continuously training the ML model on historical breach data and cloud security service data before actual breaches occur. The model predicts potential breaches in advance based on patterns learned from past incidents, enabling proactive response. This preliminary training and prediction process allows the system to detect and respond to threats before they materialize, reducing breach response time.
Solution Approach 2:
The patent implements feedback mechanisms where the ML model continuously learns from actual breach outcomes and adjusts its predictions. By analyzing the effectiveness of detected threats and comparing predicted breaches with actual incident data, the system refines its algorithms to improve accuracy over time. This feedback loop enables more efficient data processing by focusing computational resources on high-probability threats while reducing false positives.
Data Source
AI summary
Breach prediction via machine learning includes, responsive to (1) training one or more machine learning models in a breach prediction engine, (2) monitoring one or more users associated with an enterprise, and (3) detecting an incident that is one or more of a threat and a policy violation for a first user of the one or more users, analyzing details related to the incident with the breach prediction engine; displaying a breach prediction likelihood score for the enterprise based on the analyzing; and providing one or more recommendations for the enterprise based on the incident and the analyzing.