Absolute Risk Scoring for Cloud Attack Path Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack an efficient and comprehensive method for monitoring and managing compute assets in cloud environments to detect anomalies, ensure data security, and maintain compliance, particularly in complex network environments involving multiple entities and regulatory requirements.
Innovation Solution
A data platform that integrates data ingestion, processing, and user interface resources to monitor and manage compute assets, utilizing agents to collect data, create polygraphs of behavioral graphs, and perform real-time anomaly detection and compliance monitoring, with optional data aggregation through a proxy model to minimize network exposure and optimize data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive monitoring and detection of compute assets is implemented, then security and compliance are improved, but system complexity increases
Solution Approach 1:
A data platform is introduced as an intermediary system that sits between compute assets and monitoring systems. The platform collects data from agents deployed on compute assets, processes this data through centralized services, and delivers security and compliance monitoring without requiring complex direct monitoring of each asset individually. This mediator architecture simplifies the overall system while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The data platform is designed to perform multiple functions: data collection from diverse compute assets, data processing and normalization, anomaly detection, compliance monitoring, and risk scoring. By consolidating these functions into a single universal platform, the system avoids the complexity of separate specialized monitoring systems for each function.
2Reliability
If real-time anomaly detection is performed, then security response is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction at the data collection stage, preparing data in advance for anomaly detection. By pre-processing and structuring data before it enters the analysis pipeline, the system reduces the computational burden during real-time anomaly detection while maintaining timely responses.
Solution Approach 2:
The system extracts only the critical features and indicators from the vast amount of data collected from compute assets. By identifying and isolating the most relevant data elements for anomaly detection, the system processes a smaller subset of information in real-time, reducing processing requirements while maintaining detection effectiveness.
3Measurement precision
If comprehensive data collection from multiple entities is implemented, then monitoring accuracy is improved, but network exposure increases
Solution Approach 1:
The data platform serves as a secure intermediary that collects data from agents on compute assets through controlled data transmission channels. The platform processes and analyzes data in an isolated environment, minimizing the need for direct network exposure between monitoring components and compute assets. This mediator approach enables comprehensive data collection while maintaining network security.
4Reliability
If compute asset management across multiple entities is implemented, then compliance monitoring is improved, but operational complexity increases
Solution Approach 1:
The data platform provides universal compliance monitoring capabilities that work across multiple entities and regulatory frameworks. The same platform handles compliance monitoring for different entities by applying appropriate rules and policies, eliminating the need for separate operational processes for each entity while maintaining comprehensive compliance oversight.
Data Source
AI summary
An illustrative method includes identifying, based on a scan of a compute environment associated with an entity, a plurality of attack paths from one or more networks to one or more datasets associated with the entity, determining individual risk scores for each attack path indicating a level that each attack path could be exploited to access the one or more datasets, and determining, based on the individual risk scores, an absolute risk score for the entity indicating a status of a security posture for the entity. A risk mitigation operation associated with the entity is performed based on the absolute risk score.