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What is Sweet?

We harness crucial runtime insights to cut through the excess noise surrounding cloud security and focus on the most critical risks. In the current landscape, cybercriminals are adeptly breaching cloud infrastructures, and the rise of runtime vulnerabilities is a growing concern. Equip your organization with Sweet’s cutting-edge, eBPF-based sensor, which helps establish a robust benchmark for normal activities within your cloud environment. This innovative tool offers vital runtime insights that optimize operations across the entire cloud security architecture. By leveraging Sweet’s dynamic profiling capabilities, you can swiftly pinpoint and address runtime anomalies, allowing for effective management of live threats within the cloud. The eBPF-based sensor provides comprehensive, real-time insights without sacrificing performance or adding extra costs. Instantly identify zero-day cloud attacks, obtain actionable intelligence regarding these threats, and experience minimal interruptions. Sweet’s approach significantly enhances the ability of security teams to rapidly counteract cloud threats as they emerge, ensuring high accuracy and minimal impact on business functions. This proactive methodology not only fortifies defenses but also equips organizations to stay ahead of evolving threats in an increasingly complex digital landscape. As a result, companies can maintain operational resilience while navigating the challenges posed by modern cybersecurity threats.

What is LotusEye?

LotusEye provides a cloud-based solution for detecting anomalies through AI, automatically learning typical behavior from numerical or sensor data uploaded in CSV format, while continuously generating anomaly scores to pinpoint irregularities that may indicate faults or unexpected events, all without requiring users to possess machine learning knowledge. The platform supports both wide-format CSV files, where each row represents sensor readings at distinct timestamps, and long-format CSV files that contain columns for timestamps, sensor names, and values. Users can easily upload their data using an intuitive drag-and-drop interface or through an API that facilitates automated processing on a predetermined schedule. After training an AI model with data from standard operations, users can submit test data to receive calculated anomaly scores and visualize the outcomes on dashboards equipped with time-series charts, threshold indicators, and filtering capabilities. This functionality aids teams in quickly recognizing unusual patterns and investigating potential issues, ultimately boosting operational efficiency. By transforming raw data into actionable insights, the platform empowers organizations to make informed decisions based on real-time analytics.