March 2025 Summaries

Public sector organizations are increasingly adopting OpenTelemetry (OTel), an open-source observability framework, to enhance their IT infrastructure's performance, reliability, and security. As a Cloud Native Computing Foundation project, OTel offers a vendor-agnostic approach to collecting and processing telemetry data, addressing the challenges of complex system environments and diverse technology stacks typical in the public sector. Elastic's integration with OTel streamlines observability by enabling real-time insights through a distributed, scalable platform that supports a wide range of data sources. By combining OTel with Elastic's AI and machine learning capabilities, public sector entities can improve system reliability, expedite problem resolution, and ensure compliance with regulatory requirements. This integration not only enhances observability but also offers significant opportunities for cybersecurity applications by unifying observability and security data, ultimately helping government agencies deliver efficient and reliable services.

Attackers often use the MITRE ATT&CK T1564 - Hide Artifacts technique to conceal their activities within systems, utilizing hidden files, concealed processes, and manipulated registry keys to evade detection and persist undetected. This technique encompasses various sub-techniques such as hidden files and directories, the creation of hidden users, the use of NTFS alternate data streams, and executing malicious code in virtual instances, all aimed at avoiding triggering alerts and extending the attackers' dwell time in an environment. To counteract these tactics, monitoring a wide range of data sources is crucial for uncovering such stealthy techniques, and Elastic Security provides tools to detect these hidden threats through integrations that enhance the visibility of files, processes, registry keys, user accounts, email communications, and network traffic. Queries using Elastic Stack ES|QL are employed to identify suspicious activities, such as monitoring file attributes, registry modifications, and email rules, to reveal hidden artifacts that adversaries use to evade detection. By understanding and detecting T1564 activities, organizations can fortify their defenses, mitigate risks to confidentiality, integrity, and availability, and maintain a robust security posture by ensuring continuous vigilance against evolving threats.

J.P. Morgan and Elastic are collaborating to empower developers in revolutionizing payment processing by providing the necessary tools and resources, such as J.P. Morgan's Payments Developer Portal and Elastic's search technology, Elasticsearch. This partnership aims to support developers in creating seamless, efficient, and innovative payment systems that meet the growing demand for quick, easy, and secure transactions. The Payments Developer Portal, with its self-service access to APIs, documentation, and a sandbox environment, allows developers to experiment and integrate payment solutions into business operations. Elastic's role in enhancing data accessibility through Elasticsearch further aids developers in managing complex payment systems. By investing in developer-friendly environments, both companies are enabling developers to push the boundaries of payment technology, thereby shaping the future landscape of digital transactions.

ElasticGPT is an internal generative AI assistant developed by Elastic, utilizing a retrieval augmented generation (RAG) framework powered by its proprietary technology stack to facilitate secure and scalable knowledge discovery for its employees. Central to its architecture is SmartSource, an internally fine-tuned RAG model that leverages Elasticsearch for vector search and data storage, enabling context-rich information retrieval from Elastic's internal data sources, while integrating with OpenAI's GPT-4o models hosted securely on Azure. The generative AI application features real-time streaming responses, robust security protocols, and seamless integration with Elastic's ecosystem, including its Search AI Platform, Elastic Cloud for scalability, and Elastic Observability tools for performance monitoring. The frontend uses React and Elastic's design library EUI for a cohesive user experience, while LangChain orchestrates the RAG pipeline and API facilitates efficient communication between the frontend and backend. Beyond SmartSource, ElasticGPT offers private access to OpenAI's models for broader generative tasks, maintaining stringent security and compliance standards. This strategic platform approach enhances employee efficiency and prepares Elastic to adapt swiftly as AI technology evolves, with plans to incorporate specialized AI agents and expand capabilities to include other large language models.

Version 8.17.4 of the Elastic Stack has been released, bringing improvements over the previous version, 8.17.3. Users are encouraged to upgrade to this latest version to benefit from the updates and fixes it offers. For a comprehensive understanding of the issues addressed and the specific changes made to each product within this release, users should consult the detailed release notes.

Observability metrics are essential for understanding the performance, behavior, and health of applications and systems, as they enable organizations to make sense of their operations and develop proactive monitoring processes. These metrics are part of a broader observability framework, traditionally composed of three pillars—metrics, logs, and traces—with a fourth pillar, profiling, emerging as technology advances. Key types of observability metrics include application, system, and business metrics, each providing insights into different aspects of a technology stack's performance. The Site Reliability Engineering (SRE) community emphasizes the "four golden signals" of latency, traffic, errors, and saturation as critical for effective observability. Challenges in implementing observability metrics include managing data heterogeneity and noise, which can be addressed by defining clear objectives, using open standards like OpenTelemetry, leveraging automation, and customizing visualizations. Elastic Observability offers a unified solution for collecting, monitoring, and analyzing these metrics, enhancing operational efficiency and enabling faster resolution of issues.

Elastic Stack version 8.16.6 was released on March 25, 2025, and users are encouraged to upgrade to this latest version over the preceding 8.16.5 release. This update includes various fixes and changes, details of which can be found in the release notes. The announcement was made by Vincent Deuschle, highlighting the advantages of the new version and suggesting users consult the release notes for comprehensive information on the updates.

Artificial intelligence (AI) is gradually being integrated into government operations, promising to enhance decision-making, streamline processes, and improve services for citizens. Despite the potential benefits, AI adoption in the public sector lags behind the private sector due to challenges such as data privacy concerns, varying digitization levels, and complex regulatory environments. AI applications in government range from improving public services in healthcare, education, and transportation to supporting data-driven decision-making and automating administrative tasks. Generative AI specifically offers advanced data processing and content creation capabilities, facilitating more natural interactions with machines. However, the implementation of AI in government requires robust governance frameworks to ensure ethical deployment, fairness, and transparency, while addressing security concerns associated with handling sensitive data. Efforts such as the development of AI governance frameworks and the establishment of ethical standards are underway to promote responsible AI usage. As government agencies worldwide explore AI's potential, strategic implementation, clear regulations, and real-time data visibility are essential for overcoming obstacles and fostering innovation.

Elastic's AI-driven security analytics provide significant economic benefits for organizations by enhancing security measures, reducing costs, and improving business continuity. A study by Enterprise Strategy Group, commissioned by Elastic, examined the impact of Elastic Security on a composite organization, revealing key benefits such as improved security analytics through machine learning and AI, lower costs from tool consolidation and resource optimization, and enhanced business continuity by reducing security incidents. The study found substantial reductions in annual risk exposure, false positives, and security incident rates, alongside faster investigation and remediation times. Additionally, Elastic Security helped reclaim a significant portion of full-time security employees' hours, allowing more focus on strategic initiatives, with notable reductions in total cost of ownership and employee turnover. These findings demonstrate that organizations using Elastic Security gain greater visibility and efficiency, achieving a substantial return on investment while ensuring better protection of their intellectual property.

Elastic and Tines have collaborated to offer an integrated product that enhances security and observability workflows by combining Elastic's analytics capabilities with Tines' AI-powered workflow orchestration and automation. This collaboration aims to help organizations manage the increasing complexity of data and alerts by providing seamless automation and orchestration for security operations centers (SOC) and site reliability engineering (SRE) teams. The combined solution allows real-time insights and efficient incident response, reducing costs and operational burdens. Elastic's Search AI Platform offers advanced log management and analytics, while Tines provides tools for building, running, and monitoring workflows, thus enabling organizations to enrich data and streamline processes. This partnership, serving over 60 joint customers, seeks to minimize the time it takes to turn insights into actions, leading to faster issue resolution and improved operational efficiency.

In the digital realm, adversaries use the OS Credential Dumping technique (T1003) from the MITRE ATT&CK framework to extract sensitive credentials, posing a significant threat to network security. This method enables attackers to impersonate users, escalate privileges, and move laterally within a network, making the detection and prevention of such activities crucial to maintaining system integrity and confidentiality. The text emphasizes the need for a proactive threat hunting approach using Elastic Security tools and ES|QL queries to monitor various indicators of credential dumping, such as suspicious process activities, file access patterns, and unauthorized registry changes. By leveraging a combination of logs, monitoring tools, and data sources, security teams can enhance their detection capabilities, ensuring the protection of critical credentials and fortifying defenses against potential intrusions. The document underscores the ongoing nature of this threat and encourages continued vigilance and refinement of threat-hunting strategies to stay ahead of adversaries.

Elasticsearch is becoming a pivotal technology in the aviation industry due to its ability to manage and analyze large volumes of data in real time, which enhances operational efficiency, customer experience, and safety measures. European airlines are increasingly adopting Elasticsearch to streamline operations, such as tracking flight statuses and providing real-time updates to passengers, as well as optimizing baggage handling and enhancing customer service through personalized digital interactions. The technology also supports predictive maintenance and compliance by efficiently processing structured and unstructured data, helping airlines anticipate technical issues and address regulatory requirements. Additionally, Elasticsearch aids in fraud detection and cybersecurity by leveraging machine learning to identify anomalies and potential threats. As the aviation sector continues to embrace digital transformation, Elasticsearch is set to become an essential tool in achieving improved efficiency, safety, and customer satisfaction.

Elastic's blog post details their journey in developing a generative AI application to enhance customer support, showcasing the creation of a proof of concept utilizing Vertex AI integrated with Salesforce Service Cloud. The initiative aims to automate case summaries and draft initial responses to improve efficiency and customer satisfaction while easing the workload on support agents. Initial user feedback highlighted a need for improved AI accuracy, particularly in handling technical queries due to limitations in the language model's training data. Consequently, Elastic plans to refine input data and set higher accuracy thresholds to enhance AI performance, with future iterations aiming to integrate Elasticsearch for better response precision. The ultimate goal is to develop a scalable Support AI Chat Assistant, facilitating a self-service experience that could reduce response times and enhance customer satisfaction, while also freeing up resources for more strategic tasks. The ongoing blog series promises further updates on this project, emphasizing Elastic's focus on customer-centric solutions.

Artificial intelligence (AI) and generative AI (GenAI) are becoming integral to the public sector, shifting from theoretical applications to real-world implementations with a focus on specific use cases that align with organizational missions and key performance indicators. A recent webinar with industry experts highlighted the current GenAI adoption in government, education, and defense, emphasizing the importance of data preparedness, stewardship, and governance for successful implementation. Public sector organizations are prioritizing high-impact use cases to enhance operational efficiency, reduce errors, and improve service delivery, while grappling with challenges such as governance, risk, security, and data readiness. The retrieval augmented generation (RAG) approach is crucial for grounding AI responses in authoritative information, enhancing accuracy and trustworthiness. Responsible AI practices, including risk assessment, bias detection, and public engagement, are essential for ethical and transparent AI use. Additionally, workforce preparation through training and collaboration with academic and research institutions is vital to leverage AI effectively, emphasizing a practical understanding of AI's capabilities and limitations.

Public sector agencies face challenges in managing vast and distributed datasets, often resulting in data silos that impede real-time and efficient data access. Data mesh offers a solution by decentralizing data management, empowering domain-specific teams to take ownership of their data, and treating datasets as products with clear documentation and quality standards. It enables more efficient data handling and governance through self-service platforms and federated governance, allowing data to be accessed and analyzed securely and swiftly across various domains. Unlike data lakes and data fabrics that can lead to silos or require data duplication, data mesh provides a unified, searchable platform that democratizes data access and enhances AI-driven operations. Its architecture supports a user-centric approach, improving collaboration and decision-making in sectors such as defense, public health, and transportation. Elastic, an analytics platform, exemplifies this approach by offering features like cross-cluster search and role-based access control, thus maximizing data value for government, healthcare, and education sectors.

Detecting covert data exfiltration is crucial for maintaining network security, as adversaries often use the MITRE ATT&CK technique T1048, known as "Exfiltration Over Alternative Protocol," to smuggle sensitive data out of environments undetected. This technique involves using alternative protocols like FTP, SMTP, HTTP/S, DNS, and SMB to bypass standard security measures. Attackers may also use encryption or obfuscation to hide their activities, making it challenging for defenders to identify exfiltration attempts. By analyzing network traffic patterns, scrutinizing DNS queries, and leveraging tools like ES|QL queries, security analysts can uncover hidden threats and enhance their detection capabilities. Elastic Security provides integrations and tools to monitor various data sources, such as application logs, cloud storage access logs, and network traffic logs, to identify unusual activities indicative of potential data exfiltration. Continuous vigilance and the use of advanced threat-hunting techniques are essential to staying ahead of adversaries who constantly refine their methods to evade detection.

In the context of increasing cyber threats, Zero Trust is a critical strategy that requires a comprehensive approach to ensure security through continuous verification, especially in the public sector. Despite its benefits, implementing Zero Trust poses challenges due to the complexity of integrating with diverse and often outdated IT environments, as well as balancing security with usability. Public sector agencies, in particular, face difficulties with data silos and legacy systems that hinder efficient threat detection and response. Elastic offers a solution by providing a unified data layer that connects disparate data sources, enabling real-time insights, reducing infrastructure costs, and enhancing operational resilience. This approach allows agencies to maintain compliance with federal mandates and improve security without centralizing data. Elastic's offerings support public sector organizations by integrating search and AI capabilities, providing cost-effective scaling, and ensuring compatibility with existing systems, making it a favored choice for Zero Trust implementation.

Elastic has announced the technical preview of its Elastic Cloud Serverless offering on Google Cloud, available in the Iowa (us-central1) region, enabling users to quickly start and scale observability, security, and search solutions without managing infrastructure. Built on the Search AI Lake architecture, this platform leverages Google Cloud Storage to provide vast storage, separate storage and compute, low-latency querying, and advanced AI capabilities. Elastic Cloud Serverless offers a streamlined workflow with guided onboarding and a flexible usage-based pricing model, allowing users to pay only for what they use. The company plans to expand this service to additional Google Cloud regions and introduce new features to enhance performance and usability, aiming to deliver uncompromised speed, scalability, and cost-efficiency.

The telecommunications industry is increasingly integrating artificial intelligence (AI) to enhance customer experiences and optimize operations, yet many leaders face challenges in managing and utilizing their vast data effectively. Despite the potential of AI, a significant number of leaders in telecommunications, technology, and media industries struggle with real-time, scalable data use, with only 34% leveraging data insights daily for business decisions. To address this, 61% of executives are prioritizing investments in data analytics and data science tools. Generative AI is rapidly gaining traction, with 88% of C-suite executives planning to invest in or having already invested in it for applications such as chatbots and network optimization. However, the effectiveness of AI applications hinges on a robust data strategy that ensures all data types are accessible and analyzable, emphasizing the necessity of a unified data view to avoid fragmented insights.

Elastic Security has been awarded the AV-Comparatives 2024 Enterprise Approved Product Award for its exceptional performance in malware defense, system performance, and minimal false positives. This recognition highlights its effective 99.8% malware protection rate and seamless integration with the Elastic Search AI Platform, which ensures robust security without compromising system speed or stability. AV-Comparatives, a well-respected independent testing lab, conducts rigorous assessments that simulate real-world attacks, and Elastic Security's performance in these tests underscores its commitment to providing world-class security solutions. The award validates Elastic Security's approach to maintaining high performance and low resource consumption while delivering advanced security features, making it a trusted choice for businesses seeking reliable cybersecurity protection.

The March 2025 Elastic DevRel newsletter highlights several key developments, including the first pre-release of Elasticsearch and Elastic Stack 9.0 and the technical preview of Elastic Cloud Serverless on Microsoft Azure. The 9.0.0-beta1 release introduces new features such as ES|QL's lookup joins, KQL filtering, and performance enhancements, as well as the inclusion of statistical functions and commands for improved query execution. The newsletter also covers a range of educational content, including blogs and videos on topics like vector search and open-source tools, and lists numerous upcoming events globally, such as ElasticON conferences in Sydney and Singapore. Additionally, it encourages community engagement through meetups and offers insights into recent advancements and applications of Elastic's technologies.

Efficient management of Elasticsearch Service (ESS) expenses is facilitated by the Elasticsearch Service Billing integration, which allows organizations to track, customize, and alert on their billing data. By deploying this integration on an Elasticsearch cluster with a Fleet-enabled Elastic Agent, users can leverage preconfigured dashboards to visualize and analyze their spending. The integration can be tailored to align with organizational structures, enabling the attribution of costs to specific teams through custom ingest pipelines. Users can further enhance their financial oversight by setting up alerts in Kibana to monitor team-specific expenditures and prevent unexpected budget overruns. This setup ensures real-time visibility into cloud spending, helps enforce budget policies, and offers the capability to act swiftly in response to spending trends, thereby promoting financial accountability across teams.

Version 8.16.5 of the Elastic Stack has been released, with a recommendation to upgrade from the previous version, 8.16.4. For a comprehensive understanding of the issues that have been addressed and a complete list of changes for each product in this release, users are advised to consult the release notes.

Version 8.17.3 of the Elastic Stack was released on March 4, 2025, by Stamatis Kourkoutas. Users are encouraged to upgrade to this latest version, which is recommended over the previous version 8.17.2. For a comprehensive overview of the fixed issues and detailed changes across each product in this release, users are advised to consult the release notes.

Generative AI (GenAI) is transforming the cybersecurity landscape by enhancing efficiency, productivity, and defensive capabilities without replacing human jobs. Instead, it automates routine tasks, allowing security professionals to focus on strategic and creative problem-solving, thereby improving their overall effectiveness. GenAI aids in various roles, from security engineers to analysts and SOC leaders, by automating data integrations, alert triage, and threat investigation, as well as providing predictive insights for better communication with stakeholders. Despite expanding attack surfaces due to malicious actors' use of AI, GenAI enables security teams to prioritize critical incidents, reduce alert fatigue, and respond swiftly and accurately to threats. Although not a replacement for human expertise, GenAI acts as a powerful tool that shifts the focus of cybersecurity professionals towards more strategic tasks, enhancing their ability to maintain robust security postures.

Since the introduction of OMB M-21-31 in 2021, US federal agencies have faced significant challenges in complying with its advanced event logging requirements, which aim to enhance centralized visibility into logging data for better cybersecurity incident management. A study by the US Government Accountability Office (GAO) in December 2023 highlighted ongoing obstacles such as lack of staff, technical challenges in event logging, and limitations in cyber event information sharing, which are still pertinent in 2025. However, advancements in AI and technologies like Elasticsearch have rendered compliance more attainable, enabling cost-effective data management, efficient use of AI for automating tasks, and secure data sharing across agencies. Elasticsearch's features, such as data tiering, searchable snapshots, and the Elastic Common Schema, facilitate budget optimization, skills gap bridging, and overcoming technical challenges within federal agencies, thereby supporting their efforts to comply with M-21-31 requirements.