Cloud Attack-Path Reconciliation for Near-Real-Time Detection
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Solution Overview
Problem
Existing cloud management systems face challenges in presenting attack paths in real-time or near-real-time due to computational and financial expenses, leading to inconsistent security information and potential threats from critical vulnerabilities.
Innovation Solution
A cloud management system that integrates a cloud-native application protection platform (CNAPP) to pre-calculate attack paths by scanning enterprise cloud resources and combining risks from microservices, using a machine-learning model to identify vulnerabilities and construct asset dependency graphs, enabling real-time or near-real-time detection and presentation of attack paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If attack paths are calculated continuously to detect vulnerabilities in real-time, then detection speed is improved, but computational cost and financial expense increase significantly
Solution Approach 1:
The system pre-calculates and stores attack paths when changes are detected in the cloud environment, rather than calculating them continuously. This preliminary action allows the system to have attack path information ready when needed, improving detection speed without requiring constant expensive computations.
Solution Approach 2:
The system performs attack path calculations periodically or event-driven based on changes in the cloud environment, rather than continuously. This periodic action reduces computational cost by only performing expensive calculations when necessary, while still maintaining timely detection capabilities.
2Reliability
If attack path calculations are performed frequently to maintain real-time security views, then reliability of security information is improved, but system complexity increases
Solution Approach 1:
The system pre-computes and caches attack paths when environmental changes are detected, ensuring reliable security information is available when needed. This preliminary computation approach maintains information reliability without requiring complex continuous calculation systems.
Solution Approach 2:
The system introduces an intermediary layer that manages the complexity of attack path calculations by caching and retrieving pre-computed paths. This intermediary mechanism simplifies the overall system architecture while maintaining reliable security information through strategic pre-computation.
3Measurement precision
If comprehensive attack path analysis is conducted across all cloud assets, then measurement precision of vulnerabilities is improved, but loss of time increases due to computational overhead
Solution Approach 1:
The system performs comprehensive attack path analysis in advance when changes are detected, storing the results for quick retrieval. This preliminary comprehensive analysis ensures high measurement precision of vulnerabilities while avoiding time loss during actual detection by using pre-computed information.
Solution Approach 2:
The system conducts comprehensive attack path analysis periodically or event-driven, rather than continuously. This periodic comprehensive analysis maintains high measurement precision by regularly updating security views while reducing time loss by avoiding redundant continuous computations.
Data Source
AI summary
In one embodiment, a method includes receiving a plurality of records associated with a plurality of assets in a cloud system and one or more risks associated with one or more of the plurality of assets from one or more microservices, identifying one or more inconsistencies within the plurality of records, initiating a reconcile process to resolve the one or more inconsistencies, determining that a condition for an attack-path analysis is satisfied, and triggering the attack-path analysis to identify one or more attack paths.