Abstract

The present disclosure provides systems and methods for contextual anomaly detection. The system includes a data ingestion module configured to receive data streams from a plurality of data sources within an enterprise environment. The system includes a feature extraction module configured to process the received data streams and determine anomaly indicators based on statistical analysis of the received data streams. The system includes a contextual reasoning module configured to determine contextual factors associated with the anomaly indicators and process the anomaly indicators and the contextual factors using a Large Language Model to classify the anomaly indicators as genuine anomalous events or contextually explainable occurrences. The system includes an insight generation module configured to generate human-readable insights based on the classified anomaly indicators. The system includes a feedback module configured to receive user validation of the generated human-readable insights and refine subsequent anomaly classifications based on the user validation. By integrating heterogeneous real-time data sources and leveraging advanced contextual reasoning, the system overcomes limitations of traditional rule-based or statistical anomaly detection systems.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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