March 2026 Summaries

Elastic Cloud Hosted has achieved FedRAMP® High status on AWS GovCloud (US), the highest security baseline from the US Federal Risk and Authorization Management Program, which requires over 400 security controls to protect sensitive unclassified data. This authorization underscores Elastic's commitment to robust security solutions for US federal agencies, enhancing their ability to manage sensitive data through scalable, AI-powered tools for search, observability, and security. Elastic has also collaborated with the Cybersecurity and Infrastructure Security Agency (CISA) to offer SIEM-as-a-Service, standardizing cybersecurity monitoring across federal civilian executive branch agencies, and partnered with the US General Services Administration on a volume-based discount program. The platform provides a secure foundation for federal organizations handling critical data, enabling real-time mission search, AI experiences, and Zero Trust architecture while reducing costs associated with data access and retention. Elastic’s open-source technology supports interoperability and efficiency, allowing federal agencies to share code and architecture and comply with regulations like M-21-31, while offering deployment options that range from self-managed to serverless solutions.

ECK 3.3.1 introduces significant updates to enhance the management of Elasticsearch on Kubernetes, addressing challenges like cluster health monitoring, support for air-gapped environments, and scalable configuration management. The release includes AutoOps Cloud Connected, which automates health monitoring and root cause analysis, reducing manual interventions and operational burdens. It also provides native support for air-gapped environments through a self-hosted Elastic Package Registry, allowing seamless Fleet integrations without needing public registry access. Additionally, the update features composable Stack Configuration Policies, enabling multiple policies to target a single cluster with prioritized layering, thus offering greater flexibility and ease in managing configurations across numerous clusters. These enhancements are aimed at improving efficiency and consistency for platform teams operating Elasticsearch at scale in diverse environments.

Guilherme Xavier's reflection on his onboarding experience at Elastic highlights the unique challenges and practices associated with integrating into a complex and globally distributed tech company. Engineers at Elastic encounter a distinctive use of industry-standard technologies like Kubernetes and Terraform, interwoven with custom code developed over the years. A comprehensive onboarding process is emphasized, featuring an onboarding document with key information and checklists, a dedicated Slack channel for onboarding-specific communication, and runbooks for practical learning. Elastic's commitment to a collaborative onboarding experience is further demonstrated through encouraging new hires to have individual chats with team members to build rapport, which Xavier found beneficial despite the global distribution of the team. This process not only facilitated his swift adaptation but also prepared him to contribute effectively to the team's goals and eventually participate in onboarding newcomers himself.

The US Cybersecurity and Infrastructure Security Agency (CISA) has introduced a SIEM-as-a-Service (SIEMaaS) offering for federal civilian agencies, utilizing Elastic Security on Elastic Cloud to enhance cybersecurity measures. This cloud-based platform provides advanced, AI-driven threat analytics, incident response, and standardized cybersecurity data ingestion, aimed at improving the security posture of Federal Civilian Executive Branch (FCEB) agencies with speed, scale, and operational consistency. Delivered at no cost to the agencies through the FedRAMP-authorized Elastic Cloud, the service is managed by CISA's Continuous Diagnostics and Mitigation Program Management Office, ensuring seamless infrastructure operations. SIEMaaS is designed to foster a collaborative and standardized approach to national cybersecurity, reducing costs associated with data access and retention while providing next-generation capabilities like advanced investigative tools and AI-driven cyber defense. The initiative also supports migration from legacy systems and offers free training and workshops to ensure agencies can efficiently operationalize the service. Elastic, in partnership with CISA, aims to establish a repeatable, cost-efficient model for shared national cyber defense, with a large FCEB already set to be the first tenant, providing a blueprint for future deployments across government agencies.

The cybersecurity landscape is undergoing a dramatic transformation due to the use of AI by attackers, requiring Security Operations Centers (SOC) to evolve rapidly to meet these new challenges. The introduction of AI has democratized sophisticated cyber threats, which were once the domain of nation-states, allowing adversaries to scale attacks with unprecedented speed and efficiency. This shift necessitates a move away from traditional SOC models, which heavily rely on manual alert triage, towards an "agentic" model, where AI handles routine tasks, allowing human analysts to focus on high-level threat engineering. The concept of an agentic SOC involves leveraging AI as an integral part of security operations to provide automated response and prevention measures that operate at machine speed, thereby compressing the time from detection to remediation to seconds. Elastic Security exemplifies this approach by integrating AI into workflows, ensuring that the SOC is equipped to handle the accelerated pace of modern cyber threats, and emphasizing the importance of data-driven security solutions for survival in this rapidly changing environment.

Elastic Security XDR has revolutionized endpoint protection by eliminating per-endpoint pricing, allowing organizations to build their security strategies based on risk rather than budget constraints. This approach enables comprehensive visibility and prevention across entire environments, addressing the rapid and sophisticated nature of AI-driven cyber threats that operate at the kernel level. Elastic's platform integrates seamlessly with existing tools and infrastructures, providing a unified console for real-time alert correlation, contextual data analysis, and automated response actions, without the need for complex rollouts. The efficacy of Elastic’s prevention-first strategy is backed by independent validation, maintaining a consistent 100% protection rate in AV-Comparatives' tests, which contributed to its recognition as a Leader in the IDC MarketScape assessment. The platform supports various deployment options, including Elastic Cloud Serverless, ensuring flexibility and scalability in managing security operations while reducing costs associated with traditional endpoint solutions.

Elastic Workflows introduces native automation to Elastic Security, eliminating the need for separate SOAR tools and reducing the complexity and cost associated with security operations. By integrating directly within the Elastic Security platform, Workflows provides seamless access to alerts, cases, and investigation data, allowing AI agents to execute tasks and reason through complex investigations more efficiently. This integration helps security teams manage growing alert volumes and AI-driven threats without the typical overhead of maintaining brittle integrations with standalone SOAR systems. Workflows are defined in YAML, event-driven, and can synchronize context across various security tools, enhancing the reliability and speed of threat response. This approach allows for consistent execution of defined tasks from playbooks while leveraging AI for adaptive reasoning when scenarios deviate from known patterns. Elastic Workflows is currently in technical preview, with a promise of general availability, offering a streamlined solution for security operations centers aiming to automate processes without adding additional tools to their stack.

UNIHACK 2026, sponsored by Elastic, showcased the innovative prowess of over 1,010 participants from more than 20 universities, who utilized Elasticsearch and vector databases to create scalable AI solutions for real-world challenges. The event emphasized a shift from academic theory to practical, high-performance technology development, with standout projects demonstrating sophisticated uses of geospatial data, hybrid search, and data-handling capabilities. Participants, mentored by industry experts, learned to see large language models (LLMs) as part of a broader data-and-workflow challenge rather than a complete solution. Notable projects included the Hot Steppers' Hooked App, which used advanced search technology to enhance context comprehension, and others that tackled sensitive data management and big data categorization. UNIHACK 2026 highlighted the potential of the next generation of developers to transform industries with innovative AI applications.

Version 9.3.2 of the Elastic Stack has been released, addressing potential security vulnerabilities that make it a recommended upgrade over the previous version, 9.3.1. Users are encouraged to refer to the security advisory and release notes for comprehensive details on the fixes and a complete list of changes for each product included in this update.

Version 9.2.7 of the Elastic Stack has been released, addressing potential security vulnerabilities and recommending users to upgrade from the previous version, 9.2.6. For comprehensive information on the issues resolved and a detailed list of changes for each product included in this release, users are encouraged to consult the release notes and the security advisory.

Elastic's platform can operate fully in air-gapped and disconnected environments, providing capabilities such as security, search, and observability without needing an internet connection. This functionality is crucial for sectors like government, military, finance, and healthcare, where maximum security and isolation from the public internet are often required. Elastic offers various deployment models, including self-managed, Elastic Cloud Enterprise, and Elastic Cloud for Kubernetes, which can be used in diverse environments like on-premises data centers and public or government clouds. The platform supports local hosting of essential services, ensuring all components are operational offline, and provides robust options for secure communication through TLS, including using self-signed certificates or those signed by an internal Certificate Authority. Elastic's architecture allows organizations to maintain consistent operations across connected and disconnected networks, reducing complexity and tool proliferation while ensuring data protection and secure communications.

Version 8.19.13 of the Elastic Stack has been released, bringing important updates and fixes for potential security vulnerabilities. Users are advised to upgrade from the previous version, 8.19.12, to take advantage of these improvements. Detailed information on the fixed issues and a comprehensive list of changes for each product included in this version can be found in the release notes.

Autonomous IT platforms, integrating observability data and artificial intelligence, are revolutionizing the management of complex, distributed systems by shifting from reactive monitoring to predictive and self-healing operations. These platforms unify logs, metrics, traces, and events, leveraging machine learning to reduce noise, detect anomalies, and identify root causes more efficiently, thereby enhancing incident response for Site Reliability Engineering (SRE) teams. The evolution from traditional monitoring to autonomous systems is marked by a closed-loop automation process, where detection, diagnosis, and remediation occur with minimal human intervention, enhancing scalability and reliability. The 2026 Constellation ShortList™ for Autonomous IT Platforms emphasizes the transition towards actionable intelligence, with leading solutions like Datadog, Dynatrace, and Elastic offering advanced capabilities such as AI-assisted incident summarization, context-aware intelligence, and predictive operations. This shift is pivotal for modern CIOs and SRE leaders aiming to reduce manual work and focus on innovation, as these platforms are key to the upcoming generative AI innovations in observability, positioning organizations to handle increasing complexity while maintaining reliability.

Autonomous IT platforms are revolutionizing observability by integrating artificial intelligence and machine learning to transform IT operations from reactive monitoring to predictive, self-healing systems. These platforms unify logs, metrics, traces, and events, leveraging AI to reduce noise, detect anomalies, and expedite root cause analysis, thus enhancing incident response for site reliability engineering (SRE) teams. The shift towards closed-loop automation allows for detection, diagnosis, and remediation with minimal human intervention, emphasizing predictive insights and context-aware intelligence to preemptively resolve issues. As the complexity of distributed systems increases, these platforms are essential for scaling reliability and reducing manual tasks, enabling IT teams to focus on innovation. Constellation Research's 2026 Constellation ShortList™ highlights the market's shift towards actionable intelligence, with vendors like Datadog, Dynatrace, and Elastic leading the charge in offering advanced capabilities such as AI-assisted incident summarization and automated remediation. These developments mark a significant step in the evolution from traditional AIOps to fully autonomous IT operations, positioning them as central to building resilient digital strategies.

AI is increasingly pivotal in cybersecurity, being utilized by both threat actors and defenders to enhance or undermine security measures. The adoption of AI has led to a 15.5% increase in generic threats in 2025, as adversaries leverage large language models to create malware with minimal effort. In response, AI-powered tools such as behavioral analytics, anomaly detection, and automated alert triage are helping security analysts identify and respond to threats more swiftly. Elastic Security integrates AI into security operations to reduce alert noise and accelerate investigations, emphasizing the importance of strategic AI implementation that includes auditing existing tools and ensuring human oversight in decision-making. As the digital attack surface expands, AI aids security teams in navigating complex threat landscapes by automating routine tasks and providing enhanced threat intelligence, but human analysts remain crucial for making context-rich decisions. Best practices for integrating AI include auditing tools, mapping processes for automation, and maintaining data quality to ensure AI contributes to a seamless, proactive defense system rather than adding to the workload.

Elastic and Dell Technologies have partnered to create the Dell Data Search Engine, integrated within the Dell AI Data Platform, to enhance enterprise AI capabilities by providing a high-performance, GPU-accelerated stack for efficient data processing and retrieval. Powered by Elasticsearch and accelerated by NVIDIA cuVS, this vector database offers significantly faster vector indexing performance, enabling organizations to effectively manage and leverage massive amounts of unstructured data. The platform simplifies the deployment and scaling of enterprise AI solutions by integrating hardware and software into a cohesive system, thereby reducing complexity and costs associated with AI workloads. The Dell Data Search Engine supports advanced vector search and hybrid keyword retrieval, essential for real-time insights and accurate decision-making in AI applications. By facilitating the transition from batch to real-time pipelines, the platform empowers AI agents to access and utilize the most current data for enhanced reasoning and action capabilities.

Elastic, Red Hat, and NVIDIA have collaborated to create a powerful AI infrastructure that combines Elastic's GPU-accelerated vector search capabilities with Red Hat's AI platform on OpenShift, leveraging NVIDIA's cuVS for enhanced performance. This integration allows enterprises to deploy scalable retrieval augmented generation (RAG) and intelligent AI agents across various environments, including on-premises, cloud, and hybrid architectures. The partnership addresses the challenges of indexing large volumes of unstructured data by significantly accelerating the process, enabling up to 12 times faster indexing and reducing CPU utilization. This solution is designed to meet data sovereignty requirements, providing organizations with the flexibility to manage and secure their data while deploying AI solutions that can efficiently retrieve context and execute operational workflows. By utilizing Elastic's Agent Builder and Workflows, enterprises can develop autonomous agents capable of real-time decision-making and action-taking, exemplified by use cases such as financial institutions deploying customer-facing AI assistants. Overall, the collaboration empowers businesses to maintain control over their data, ensuring security and compliance while harnessing the full potential of AI technologies.

Elastic and NVIDIA have partnered to introduce GPU-accelerated vector indexing utilizing NVIDIA cuVS, enhancing Elasticsearch's capabilities within the NVIDIA AI Factory validated design to accelerate AI applications. This collaboration allows enterprises to vectorize large volumes of unstructured data significantly faster than traditional CPU-based methods, addressing the growing need for efficient and scalable AI infrastructures. The integration aims to overcome challenges associated with high-performance vector databases and semantic search, which are critical for modern AI applications like generative AI and retrieval augmented generation. By leveraging GPU acceleration, organizations can reduce operational costs, optimize resources, and improve real-time query performance while maintaining seamless scalability. The NVIDIA cuVS integration is currently in technical preview for Elastic self-managed enterprise customers, with general availability anticipated in April 2026.

In the March 2026 "Cyber Strategy for America," the White House emphasizes the need for AI-driven cybersecurity innovations to protect federal, state, local, and Tribal agencies, structured around six key pillars. Elastic, with its AI-powered platform, is highlighted as a prominent partner in helping government agencies modernize their cybersecurity measures, focusing on three primary areas: reducing barriers to entry for modern security technology, integrating agentic and generative AI for efficient threat detection, and achieving unified visibility across IT and operational technology environments. Elastic supports initiatives such as CISA's Continuous Diagnostics and Mitigation dashboard and SIEM-as-a-Service, enabling real-time threat detection and incident response, while their partnership with GSA's OneGov program offers streamlined procurement processes. The strategy also underscores the importance of leveraging all layers of government and building talent in cyber technologies to sustain superiority in critical and emerging technologies, with a focus on securing critical infrastructure and supply chains across various sectors.

The March 2026 Elastic DevRel newsletter introduces several key updates and resources for users, including the announcement that AutoOps, a tool for operational insight, is now freely available to all users through Elastic Cloud Connect. AutoOps provides enhanced capabilities over Stack Monitoring by automating root cause analysis and offering remediation recommendations, allowing engineers to manage clusters more efficiently. Additionally, the newsletter highlights the release of the jina-embeddings-v5-text models within the Elastic Inference Service, which offer compact, multilingual embeddings optimized for various search and semantic tasks, using Matryoshka representation learning for efficient storage and retrieval. The newsletter also features blogs, videos, and links to resources on topics such as Elasticsearch vs. OpenSearch performance benchmarks, context engineering, hybrid search, and observability, alongside information about upcoming events like the Elastic{ON} Tour and opportunities to engage with the Elastic community through local user groups.

Elastic Cloud Serverless represents a significant shift from the traditional versioned Elastic Stack model, offering a fully managed, scalable service that eliminates the need for version-specific upgrades and maintenance windows. Unlike Elastic Cloud Hosted or self-managed setups, the Serverless model provides stable, backward-compatible APIs that are continuously updated without user intervention, akin to other SaaS platforms like Stripe or GitHub. Its architecture decouples compute from storage, enabling automatic scaling based on usage, and is designed for large-scale production workloads, making it suitable for complex tasks such as security analytics, observability pipelines, and AI-powered search applications. Despite operational differences, users can leverage their existing Elasticsearch knowledge for queries and data models, although they no longer need to manage infrastructure specifics like cluster settings or index lifecycle policies. This model is particularly advantageous for organizations seeking to focus on data and application development rather than infrastructure management, offering usage-based pricing that aligns with actual consumption.

Sean Handley, a Senior Engineering Manager at Elastic, draws from his childhood curiosity for understanding how things work to lead innovative projects in tech. Based in Manchester, England, Sean has a background in developing cutting-edge technologies, including a cloud platform and now focuses on machine learning and large language models (LLMs) with the Search Inference team at Elastic. He emphasizes the importance of maintaining focus and a conducive work environment, using tools like a standing desk, an LED lamp, and his Apple gadgets to stay energized. Sean's passion for technology is reflected in his work on the Elastic Inference Service, which integrates machine learning models to enhance semantic search and agentic workflows. Looking ahead, Sean is optimistic about the role of AI agents in software engineering, predicting they will become more sophisticated and integral to the industry.

Orange France has chosen Elastic as its new security information and event management (SIEM) partner, replacing its legacy system to align with evolving security and business needs. The decision came after a comprehensive evaluation process that included over 500 requirements and a rigorous Proof of Concept (POC), highlighting Elastic's advantages such as flexible deployment, usability, and native SOAR integration. Elastic's continuous innovation, transparent AI roadmap, and strong technical support were key factors in its selection, with the company providing an open architecture for data ingestion and an intuitive user interface that integrates SIEM, endpoint, and cloud security. The partnership is built on trust, technical excellence, and strategic alignment, with Orange expecting to benefit from Elastic's robust community and advanced features. As one of the world's leading telecommunications operators, Orange aims to leverage this collaboration to enhance its cyber activity and maintain its leadership in service quality.

New research from Elastic Australia highlights how inadequate website search experiences are leading to significant revenue losses for businesses, as 72% of shoppers abandon brands due to poor search functionality. The rise of generative AI has set higher expectations, with 62% of consumers demanding natural language capabilities in search bars, and when these expectations are not met, 78% of frustrated users are redirected to competitors. This study underscores the importance of AI-driven search systems to maintain customer loyalty and prevent revenue loss, as consumers are increasingly willing to pay more on competitor sites to avoid poor search experiences. Additionally, the findings reveal that even a single failed search can cause 11% of consumers to permanently abandon a brand, illustrating the critical role of search as not just a utility but a strategic revenue driver in the digital age.

Agentic AI is increasingly becoming a part of the public sector, offering potential benefits such as improved IT workflows, faster analysis, and enhanced citizen services, while also presenting challenges related to regulatory compliance, data security, and governance. The successful deployment of agentic AI in government requires intentional integration of responsibility and governance, highlighting the importance of secure infrastructure, clearly defined success metrics, and a compelling business case. A notable example is a Dutch defense organization that developed an air-gapped large language model to ensure data security. To address common obstacles like fragmented data, retrieval augmented generation (RAG) can be implemented, allowing AI to generate responses based on an organization's verified knowledge base. The article underscores the importance of intelligent integration that aligns with current systems, gradual deployment, and maintaining human oversight to ensure observability and accountability, while cautioning against the risks associated with unrestricted AI tool use.