Natural Language Query Generation for Cloud Anomaly Investigation
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Solution Overview
Problem
Existing systems lack an efficient and scalable method to monitor and analyze network activities within cloud environments for detecting anomalies, compliance, and security threats, particularly in complex datacenter setups where entities and behaviors are dynamic and ephemeral.
Innovation Solution
A data platform with agents installed on compute assets that collect and report data to a data aggregator, which then processes and models the data to create polygraphs, allowing for the detection of anomalies and threats by establishing behavioral baselines and deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive monitoring of network activities is implemented, then detection capability for anomalies and threats is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system divides the monitoring function into separate components: agents deployed on compute assets for local data collection, a data aggregator for centralized processing, and polygraph models for anomaly detection. This segmentation allows each component to be optimized independently and reduces overall system complexity while maintaining comprehensive monitoring capability.
Solution Approach 2:
The polygraph model acts as an intermediary between raw network activity data and anomaly detection decisions. It processes and models network behaviors to identify deviations from normal patterns, enabling reliable detection without requiring complex real-time analysis of all network traffic.
2Speed
If real-time monitoring of dynamic datacenter activities is implemented, then threat identification speed is improved, but data processing requirements and computational resources increase
Solution Approach 1:
The system performs preliminary actions by collecting and aggregating network activity data in advance, building polygraph models of normal behavior patterns before threats occur. This allows rapid anomaly detection when events happen, as the models are pre-trained to recognize deviations from normal patterns.
Solution Approach 2:
The system changes parameters by transforming raw network activity data into polygraph representations that capture essential behavioral patterns. This transformation reduces the dimensionality and complexity of data while preserving the information needed for anomaly detection, thereby reducing computational resource requirements.
3Reliability
If detailed monitoring of insider behaviors and network communications is implemented, then security coverage is improved, but false alarm rate increases
Solution Approach 1:
The system applies local quality by customizing monitoring and analysis parameters for different types of network activities and entities. Different polygraph models are trained for different behaviors (e.g., data transfers, authentication, file access), allowing the system to distinguish between legitimate variations and actual anomalies specific to each context, thereby reducing false alarms.
Solution Approach 2:
The system incorporates feedback mechanisms where detected anomalies and false alarms are used to refine and update polygraph models. This continuous learning process improves the accuracy of anomaly detection over time, reducing false alarm rates while maintaining comprehensive security coverage.
Data Source
AI summary
Using natural language queries to conduct an investigation of a monitored system, the method comprising: receiving a natural language input; generating, by a large language model and based on the natural language input, a first query directed to one or more tables of a plurality of tables; generating, based on the first query, one or more second queries each directed to a different table of the one or more tables; and presenting, via a user interface, a response to each of the one or more second queries.