OpenSearchCon India 2025

In an era where cyberattacks are growing in sophistication and scale, traditional security tools are no longer sufficient. AI-driven threat detection is enhancing how organizations safeguard their digital assets. This session will showcase how OpenSearch, combined with cutting-edge AI and machine learning, can transform raw log data into actionable security insights.

Discover how OpenSearch’s ML-powered analytics can:
* Automatically detect anomalous behavior and insider threats in real-time.
* Identify patterns indicative of fraud, ransomware, and zero-day attacks.
* Enable predictive threat hunting by correlating data across multiple sources.

We’ll also explore trending advancements, such as integrating Generative AI for threat intelligence enrichment and leveraging vector search to identify similar attack patterns across datasets. Attendees will walk away with practical strategies to implement AI-driven security analytics, reduce false positives, and enhance their organization’s cyber resilience in an increasingly hostile digital landscape.

Learn with examples the variety of OpenSearch driven - Vector, Neural, Conversational, Semantic and Multimodal Search and the difference to cost , resiliency and response time in a Managed Vs Self run Cluster. Lessons, Challenges and Solutions would enable you to architect and run it successfully to be the data exhaust fingerprint for your product.

In high-performance computing (HPC) environments, the need for robust observability mechanisms is critical to ensure optimal performance, fault tolerance, and resource utilization. Airgapped systems, often used in sensitive or high-security domains, require innovative solutions to capture, analyze, and visualize system metrics, application performance, and infrastructure health without compromising security.

Challenges arise in airgapped HPC environments, where network isolation adds complexity to monitoring and diagnostics. I explore methodologies for establishing observability in airgapped HPC environments, focusing on the unique challenges of data collection, real-time monitoring, and offline analytics. Additionally, I discuss the role of automated anomaly detection, resource optimization, and performance tuning in maintaining the efficiency and reliability of these systems. The findings highlight the importance of maintaining a balance between high security and comprehensive observability, paving the way for more resilient and performant airgapped HPC environments.

Explore how Generative AI enhances OpenSearch by improving search relevance, personalization, and contextual understanding. This session covers implementing vector search, integrating LLMs for smarter query processing, and real-world use cases of AI-powered search applications. Learn how to bring next-gen AI capabilities to OpenSearch for better user experiences.

OpenSearch is a powerful open-source search and analytics engine, but running it effectively on Kubernetes requires a well-structured approach. In this session, I will explore how to deploy, configure, and operate OpenSearch in a Kubernetes-native way. I will dive into best practices for maintaining OpenSearch clusters, optimizing performance, and handling scaling challenges.

Additionally, I will cover how to ship Kubernetes logs to OpenSearch, enabling centralized log management for a single cluster and also across multiple clusters. I will discuss scalable log ingestion strategies involving off the shelf as well as custom made tools. The session will also include practical demonstrations on building insightful dashboards for observability and setting up alerts to proactively monitor system health.

This talk will provide a comprehensive guide to managing OpenSearch effectively within Kubernetes.