Cloud-Native Attack Path Prioritization Using Runtime Telemetry
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Solution Overview
Problem
Existing automatic attack path analysis (AAPA) techniques for cloud native applications suffer from high false positives and false negatives, leading to alert fatigue and reduced confidence in security analysis results due to the combinatorial explosion of interactions and incomplete knowledge about software component actions and vulnerability consequences.
Innovation Solution
Combining static AAPA with dynamic cloud native detection and response (CNDR) techniques to utilize real-time monitoring data and telemetry information to prioritize attack paths, using machine learning models to update and reprioritize attack paths based on observed network activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static AAPA is used to identify all possible attack paths, then comprehensive security coverage is achieved, but false positives increase significantly
Solution Approach 1:
The patent applies dynamics by transitioning from static attack path analysis to a dynamic prioritization system that continuously updates attack path rankings based on real-time metrics. The system monitors data flows, application flows, access patterns, and telemetry information to dynamically adjust the priority of identified attack paths, ensuring that the most relevant threats are surfaced while reducing false positives from static analysis.
Solution Approach 2:
The patent implements feedback mechanisms by using detected metrics from runtime monitoring to continuously refine and reprioritize attack paths. The system collects telemetry data, analyzes access patterns and data flows, and feeds this information back into the attack path prioritization model, creating a closed-loop system that improves accuracy over time and reduces false positives through iterative refinement.
2Loss of information
If detailed matching criteria are used to identify exploitable vulnerabilities, then false positives are reduced, but feasible attack paths may be missed
Solution Approach 1:
The patent applies partial action by implementing a two-stage approach: first identifying all potential attack paths with broad criteria, then applying detailed matching criteria selectively to prioritize the most likely exploitable paths. This allows the system to maintain comprehensive coverage while using detailed analysis only where necessary, avoiding the loss of feasible paths that might be missed by overly restrictive filtering.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the stringency of matching criteria based on contextual information from runtime monitoring. The system modifies priority scores and detection thresholds based on observed data flows, access patterns, and telemetry metrics, allowing flexible adaptation that maintains detection completeness while reducing false positives through context-aware parameter adjustment.
3Loss of information
If runtime monitoring and dynamic prioritization are implemented, then false positives are reduced, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex dynamic prioritization system into distinct modular components: a static attack path analysis module, a runtime monitoring module that collects metrics, a metric analysis module that processes telemetry data, and a prioritization module that integrates both static and dynamic information. This modular architecture manages system complexity by allowing each component to be developed, maintained, and optimized independently.
4Reliability
If comprehensive static analysis is performed on all components, then all vulnerabilities are identified, but analysis time increases significantly
Solution Approach 1:
The patent applies preliminary action by performing static attack path analysis upfront to identify all potential vulnerabilities and attack paths before runtime monitoring begins. This initial comprehensive analysis establishes a baseline set of attack paths that are then prioritized and refined using runtime metrics, allowing the system to maintain complete vulnerability detection while reducing ongoing analysis time through dynamic prioritization.
Data Source
AI summary
Techniques for using real-time metrics and telemetry information to dynamically prioritize attack paths identified during a static analysis of a cloud native application, and using top priority attack paths identified during the static analysis to steer the dynamic analysis. The techniques may include identifying components of the cloud native application and connections between the components. The components and connections are analyzed to identify a set of attack paths. Network communications are monitored between the connections and metrics representing signals in the communications collected. A first subset of the attack paths based on a first portion of the metric indicating a real-time security vulnerability are identified. Finally, the first subset of the attack paths is prioritized over a second subset of the attack paths based at least in part on the first subset having the first portion of the metrics indicating real-time security vulnerabilities.