Title

AWS re:Invent 2023 - Security analytics and observability with Amazon OpenSearch Service (ANT350)

Summary

• Speakers: Muhammad Ali and Hajar Buafif, both OpenSearch SA at AWS.
• Key Topics: Observability, security analytics, machine data insights, reference architectures, demos, and AWS resources.
• Data Growth: IDC predicts 129 zettabytes of data in 2023, mostly machine-generated.
• Machine Data Value: Data from applications, security tools, and IoT devices can provide valuable insights.
• OpenSearch: An open-source search and analytics suite, popular for handling machine data, with many new features and versions released.
• Amazon OpenSearch Service: A managed service by AWS for running OpenSearch at scale, securely and reliably.
• Data Ingestion and Analysis: Data is collected in JSON format, indexed, and analyzed using OpenSearch Dashboard.
• Observability Use Case: OpenSearch features for observability include collecting and analyzing application and infrastructure data to minimize downtime.
• Security Analytics Use Case: OpenSearch can detect suspicious activities and trigger protective actions.
• Reference Architecture: Applications produce signals collected by agents or OpenTelemetry, passed to a buffering layer, and then written to OpenSearch for analysis.
• Demo: An e-commerce application on EKS with a planted bug demonstrates observability in action using OpenSearch Dashboard and Grafana.
• Security Analytics Plugin: Bundled with OpenSearch, it offers tools for detecting, investigating, and resolving security issues.
• Amazon Security Lake: A managed service that centralizes security log data in OCSF format, integrating with OpenSearch for analysis.
• Takeaways: Centralize observability and security data, leverage open standards, and use managed services to focus on core tasks.

Insights

• Machine Data Insights: The vast amount of machine-generated data presents a challenge for organizations to extract actionable insights. OpenSearch and Amazon OpenSearch Service provide tools to help organizations harness this data for observability and security analytics.
• OpenSearch Popularity: OpenSearch's rise in popularity and its position as the fourth most popular search engine highlight its effectiveness in handling large volumes of machine data.
• Managed Services: The emphasis on using managed services like Amazon OpenSearch Service and Amazon Security Lake indicates a trend towards outsourcing infrastructure management to cloud providers, allowing teams to focus on application development and data analysis.
• Open Standards: The session underscores the importance of open standards like OpenTelemetry and OCSF, which facilitate interoperability and community collaboration, making it easier for teams to adopt and share best practices.
• Real-time and Historical Data Analysis: The ability to analyze both real-time and historical data is crucial for comprehensive observability and security analytics. This capability allows teams to respond quickly to issues and understand long-term trends.
• Cost Optimization: The session highlights cost optimization strategies such as using storage tiers (hot, ultrawarm, and cold) to store large volumes of data cost-effectively.
• Security Focus: The increasing complexity and frequency of security threats necessitate advanced tools for log analytics. The Security Analytics plugin for OpenSearch provides a robust solution for detecting and investigating security incidents.
• Community Knowledge: Leveraging community knowledge through open-source projects and standards is a recurring theme, emphasizing the collective effort in improving security and observability practices across the industry.