Elastic Security and Google Cloud Integration for Security Analytics

7/21/2026

This post extends the Elastic Security and Google Cloud integration narrative by focusing on its application within higher education. It elaborates on how a unified data platform, powered by Elastic, can address data silos prevalent in universities, enabling "ultra-intelligence." The post details how this integration supports student services, IT operations, and cybersecurity by providing real-time visibility and actionable insights. It also introduces specific use cases and customer examples (Georgia State University, Texas A&M University System, MISI) to illustrate the benefits of connected data for analytics and AI-powered solutions in this sector. The post emphasizes the foundational role of a unified data platform for AI adoption in higher education, requiring access to trusted data, strong institutional governance, and accessibility.

7/16/2026

This post announces and details the integration of Elastic Security with Axonius to provide unified asset intelligence. It describes how the integration collects normalized asset context from Axonius, including application visibility (software, SaaS, licenses, extensions, audit logs, secrets), user data (normalized to ECS user fields, identifying orphaned or service accounts), and network data (asset inventory and enrichment). The integration supports agentless setup and periodically queries Axonius APIs to write normalized documents into Elasticsearch.

7/9/2024

This post introduces a new integrated security analytics solution combining Elastic and Google Cloud. It highlights the ingestion of Mandiant threat intelligence into Elastic, the use of Elastic CSPM with Google Cloud SCC for posture management, and the data ingestion capabilities via Elastic Agent (managed by Fleet) and Google Cloud Dataflow/Logstash. It emphasizes the unified approach to securing hybrid workloads and improving incident response.

2/14/2023

This post introduces a reference architecture for building comprehensive customer financial profiles by integrating Elastic Cloud and Google Cloud. It details the process of moving data from mainframes to Google Cloud using a Mainframe Connector, processing and enriching it in BigQuery, and then ingesting it into Elastic Cloud via Dataflow. The architecture also covers cloud orchestration from the mainframe and provides strategies for mainframe capacity planning, including remote dataset transcoding on Compute Engine VMs and data transfer via FICON and Interconnect. Specific use cases like customer experience, Customer 360, partnership management, cost optimization, and risk reduction are highlighted, with examples of Elastic's Graph feature for fraud detection.