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This paper investigates the effectiveness and limitations of Provenance-Based Endpoint Detection and Response (P-EDR) systems from an industrial perspective, highlighting the industry\'s concerns around operating costs and identifying significant gaps between academia and industry expectations.
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Provenance-based endpoint detection refers to a method of tracking and analyzing the origins and history of data as it moves through an endpoint system, allowing for improved security and monitoring of data integrity.
Entities that handle sensitive data or are subject to regulatory requirements, such as businesses in healthcare, finance, or technology sectors, are typically required to file provenance-based endpoint detection.
To fill out provenance-based endpoint detection, organizations must gather data about their systems, document data flows, record any manipulation, and prepare a report that summarizes the provenance of data at various endpoints.
The purpose of provenance-based endpoint detection is to ensure data accuracy and integrity, enhance security measures, and provide a clear audit trail for regulatory compliance and incident response.
The report must include details about data sources, data lineage, transformation processes, user access logs, and any anomalies detected during the monitoring of endpoints.
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