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Introduction
App connectors are a critical component of the Netskope secure access service edge (SASE) platform, offering visibility into user activities based on their interactions with cloud applications. These connectors monitor various types of user actions, such as uploads, downloads, and sharing events in apps like Google Drive and Box, by analyzing network traffic patterns. With this visibility, security administrators can then configure and enforce real-time policies to prevent malware, data theft and exfiltration.
However, app connectors may occasionally fail to detect certain activities due to factors such as app updates or network disruptions. To mitigate the impact of these issues for our customers, it’s essential to proactively detect the changes in the app behavior and alert our engineers when adjustments to the connectors may be needed. The main challenge lies in distinguishing actual app connector failures from normal fluctuations in network traffic. To address this, we’ve developed a patent-pending app activity monitoring system that leverages advanced machine learning algorithms to automatically identify significant anomalies in app event counts. This system has been fine-tuned to flag issues early, while minimizing false alerts, ensuring efficient and accurate detection of potential app connector problems.
Time series data
Hourly event counts from the app connector are collected via the data pipeline and grouped by data center, tenant, application, and activity type. No personally identifiable information (PII) is captured in this process. The time series data undergoes further aggregation, cleaning, and enrichment during feature engineering. Additional features, such as time of day, day of the week, and country-specific holiday calendars,