Explore how AI-driven anomaly detection enhances the security of Model Context Protocol (MCP) deployments, protecting AI infrastructure from evolving threats with real-time insights.
Explore strategies for managing combinatorial explosion in high dimensional anomaly detection to enhance data observability ...
A good way to see where this article is headed is to take a look at the screenshot in Figure 1 and the graph in Figure 2. The demo program begins by loading a tiny 10-item dataset into memory. The ...
Dr. James McCaffrey from Microsoft Research presents a complete program that uses the Python language LightGBM system to create a custom autoencoder for data anomaly detection. You can easily adapt ...
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Secure data warehousing in ERP environments: An AI-based multimodal threat detection framework
In an era where data has become one of the most valuable assets for organizations, protecting that data is a strategic ...
The NDR market is expanding due to increased cloud, remote work, and IoT adoption, creating complex attack surfaces. Opportunities include AI-driven anomaly detection, integration with EDR, XDR, SIEM, ...
Traditional rule-based systems, once sufficient for detecting simple patterns of fraud, have been overwhelmed by the scale, ...
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