May 2023 Summaries

Universities are increasingly required to address a myriad of priorities, from academic excellence to digital innovation, despite facing declining government funding per student in the UK. Instead of cutting courses, universities can leverage data to enhance efficiency and decision-making. Data is crucial across multiple areas, including student engagement, resource allocation, and environmental impacts, but often exists in silos across departments, leading to inefficiencies and duplicated costs. By adopting a unified data platform, such as Elastic, universities can centralize and access data easily, facilitating collaboration and reducing tool sprawl and expenses. The University of Edinburgh's use of Elasticsearch exemplifies how real-time data analysis can inform decisions on student engagement and resource use, while Elastic's platform allows for scalable data applications, from cybersecurity to generative AI, driven by a community committed to continuous innovation.

Elastic has strengthened its partnership with AWS by signing a new three-year global strategic collaboration agreement, which is set to enhance mutual technology innovation and support for organizations migrating to the cloud. Despite economic challenges, the demand for cloud services continues to grow, and Elastic is committed to aiding this transition with Elastic Cloud on AWS. The collaboration emphasizes Elastic's achievements, such as gaining the AWS Security ISV Competency designation and providing comprehensive security analytics for AWS. Key integrations include Elastic APM for AWS Lambda, Amazon Kinesis Data Firehose, and AWS Marketplace Vendor Insights, which streamline services and enhance performance monitoring. Elastic's recognition as a Lambda Service Ready Partner and the integration with Amazon Linux 2023 further emphasize their technical expertise. The partnership also simplifies onboarding through AWS Marketplace, allowing users to quickly deploy Elastic services across various regions, with the opportunity to start a seven-day free trial.

Ully Sampaio highlights the collaborative nature of the Elastic community, where experts and novices alike share knowledge across various platforms such as Discuss, Slack, and YouTube. This spirit of collaboration is further encouraged through the Elastic Contributor Program, which provides a platform for members to showcase their projects, presentations, tutorials, and written content related to Elastic technologies. Participants can contribute by coding, presenting, writing articles, and engaging on forums like Stack Overflow, and these activities allow them to earn recognition and rewards. The program has attracted over 367 contributors who have submitted nearly 5,000 contributions across four cycles, with new rules and scoring methods introduced for the 2023 cycle. Participants can view regional leaderboards to see top contributors and are encouraged to continue engaging with the community to earn points and recognition.

Elastic has achieved AWS Security Independent Software Vendor (ISV) Competency Partner status, focusing on Threat and Detection Response, showcasing its commitment to providing advanced security capabilities for cloud environments. This recognition required Elastic to undergo a rigorous validation process, including a technical audit and customer case studies. Elastic Security, built on Elasticsearch technology, offers fast and scalable search and analytics, integrating with AWS services like Security Hub, WAF, and GuardDuty for real-time threat detection and response. It unifies SIEM, EDR, and cloud security functions to protect against sophisticated threats, reduce risk, and streamline investigation and response. Elastic Security's features include anomaly detection, machine learning analytics, and threat intelligence feeds, helping organizations identify security incidents in real time. It provides endpoint protection, SIEM capabilities, and cloud security features, including CSPM, KSPM, and vulnerability management. By partnering with AWS, Elastic helps organizations meet security and compliance requirements, with companies like Karbon, Personal Capital, and DISH already benefiting from their solutions. Elastic offers a 7-day free trial via the AWS Marketplace, integrating seamlessly with AWS environments to transform data into actionable insights.

Elastic Security 8.8 introduces a range of new features aimed at enhancing the efficiency of security analysts dealing with increasing cyber threats. Notable updates include an improved alert triage experience with customizable controls for better prioritization, and the Execute command feature, enabling remote diagnosis and remediation of security issues on hosts. The release also introduces a Data Quality dashboard for ensuring data accuracy and completeness, and Cloud Native Vulnerability Management (CNVM) for efficient detection and mitigation of cloud security risks. Additionally, the Container Workload Protection (CWP) solution offers comprehensive protection for containerized environments, helping teams safely deploy containers without security concerns. These features are accessible through both Elastic Cloud and self-managed deployments, with the CNVM boasting an easy installation process using AWS CloudFormation. Elastic Security 8.8 aims to streamline security operations and improve response times, ensuring organizations can effectively protect their digital assets.

Data has become essential for businesses, necessitating robust data pipelines to ensure reliable decision-making tools such as dashboards. The partnership between Elastic and Kyndryl focuses on transforming data pipeline monitoring from a reactive to a proactive approach by leveraging the Elastic platform's capabilities, such as alert-triggering features like Watcher. This collaboration combines Elastic's advanced analytics platform with Kyndryl's enterprise data management expertise to detect and address data source issues before they affect dashboards, ensuring data integrity and fostering trust in data-driven decisions. This proactive monitoring is crucial in today's business landscape, where maintaining the integrity and completeness of data from its source to visualization is vital for informed decision-making.

Elastic Observability 8.8 unveils several advancements, including the general availability of GitOps-integrated synthetic monitoring and the capability to ingest data directly from Amazon Kinesis Firehose into Elastic Cloud without an agent, enhancing operational efficiencies and improving customer experiences. The update introduces GitOps-based workflows and a global managed testing infrastructure for synthetic monitoring, allowing users to manage and optimize web experiences and streamline log aggregation. Additionally, the release features new beta capabilities for service level objectives (SLOs) management, enabling organizations to set and monitor clear performance targets using various data sources. The enhanced integration with AWS Kinesis Firehose supports direct data streaming into Elastic, simplifying centralized analytics, while a new reroute processor in technical preview aids in data categorization by organizing incoming signals into dedicated streams. These innovations aim to refine application performance monitoring, data processing, and business operations, with existing Elastic Cloud customers able to access these features directly.

Elasticsearch 8.8 introduces significant advancements in vector search capabilities, enabling developers to use AI-powered technologies in search applications with minimal effort. The release includes the Elastic Learned Sparse EncodeR (ELSER) for enhanced semantic search and the Reciprocal Rank Fusion (RRF) algorithm for simplified hybrid scoring. It also supports facets with dense vector retrieval and introduces a new Radius Query for improved customer search experiences. The update allows for the storage of OpenAI model embeddings and offers a reroute ingest processor for better log management. Additionally, enhancements to time_series indices, including new compression methods and increased dimensional field limits, improve metrics data handling. The Health report API now includes a Shards Capacity indicator to help manage cluster capacities. Elastic 8.8 also offers customization options for the Kibana experience and continues to expand machine learning integration within workflows. Existing Elastic Cloud customers can access these features, while new users are encouraged to start a free trial to explore the updates.

Elastic Enterprise Search 8.8 introduces enhanced AI-powered search capabilities, including the new Elastic Learned Sparse EncodeR (ELSER) model, which improves search relevance without extensive expertise in machine learning. This release features an expanded catalog of open code database and storage connectors, allowing seamless integration and syncing of data from popular systems like MongoDB, MySQL, Postgres, and Microsoft SQL, which are optimized for advanced search applications. Elastic has also enhanced its Web and Search Analytics tools, providing out-of-the-box dashboard visualizations that help measure search success by analyzing query terms and user behavior. Additionally, the release includes a technical preview of Search Applications, enabling developers to simplify search architecture by using preset templates that combine lexical searches and ELSER-powered queries. Available on Elastic Cloud, this version offers a comprehensive suite of tools for creating personalized, robust search experiences and includes numerous usability improvements alongside new connectors for workplace content like Atlassian Jira and Microsoft SharePoint.

Elastic 8.8 introduces significant updates, including the general availability of a new Learned Sparse EncodeR model and synthetic monitoring, enhancing the capabilities of the Elasticsearch platform. The release facilitates modern search and discovery through Elastic native connectors and supports the latest generative AI models with tighter ML integration into Kibana. Elastic Observability now offers direct data ingestion from Amazon Kinesis Data Firehose and unifies observability across digital ecosystems, while Elastic Security enhances threat detection and response with a new endpoint response capability. These updates are available on Elastic Cloud, though the release and timing of features are subject to Elastic's discretion.

Kibana 8.8 introduces several new features aimed at enhancing user customization, machine learning integration, and collaboration capabilities. Users can now set dark mode individually and customize branding elements like logos and browser icons, while advanced settings provide more flexibility without relying on complex CSS. Machine learning is more accessible with features like log pattern categorization directly in Discover, and users can filter logs for specific patterns easily. Alert management is improved with the ability to suppress notifications during maintenance windows, reducing unnecessary alerts. Collaboration on cases is enhanced with new features for managing artifacts and activities, including the ability to attach files to cases. The release also includes various enhancements in machine learning job management and introduces new Elasticsearch features like Elastic Learned Sparse EncodeR for better semantic search. These updates are available on Elastic Cloud and can be experienced through a free trial for new users.

Elastic has expanded its Knowledge Center and Support Hub to provide enhanced support and resources for all Elastic Cloud users, allowing them to access technical support during cloud trials and raise billing cases. The initiative aims to prioritize user success by offering articles authored by internal experts, such as support engineers and solution architects, to help users optimize, scale, and troubleshoot their deployments. Previously available only to paying customers in the Gold tier or above, the support hub is now accessible to all Elastic Cloud accounts, making it a centralized source for product documentation and blogs. A content-first homepage experience in the Support Hub provides personalized recommendations, and the OmniSearch feature, built on Elasticsearch, allows users to search across all resources from a single bar. Elastic is committed to continuously improving its resources and plans to add new topics and insights to the Knowledge Center, encouraging user engagement through feedback and community interaction.

Generative artificial intelligence (GAI) is gaining traction in the government and public sector for its potential to enhance citizen services and employee productivity when integrated with internal data. While GAI can generate impressive results, its effectiveness is often limited by the public data it is trained on, which can lead to inaccuracies. However, by combining GAI with an organization's proprietary data, the output becomes more accurate and actionable due to the added context. The Elasticsearch platform is highlighted as a tool that can unify data storage and leverage GAI, providing secure, cost-effective, and efficient access to mission-critical information. The platform's recent introduction of the Elasticsearch Relevance Engine (ESRE) further enhances search relevance by integrating with large language models. For successful implementation, it is crucial that data is stored in a centralized, accessible platform, ensuring privacy and security while democratizing access to insights and facilitating real-time decision-making.

Generative AI models, while primarily designed for benign purposes, present potential dual-use risks as they can be repurposed by attackers to assist in certain phases of cyberattacks, though they lack the autonomy and intent to carry out sophisticated attacks independently. These models, such as GPT-4, have evolved significantly, possessing a vast number of parameters that parallel the human brain's synapses, sparking concerns over their misuse in cybersecurity. While there are no widely publicized cases of generative AI being used to execute cyberattacks, the technology's ability to generate realistic media and simulate personas could facilitate phishing and social engineering. Experiments reveal that AI, like ChatGPT, can provide insights for some attack steps but often fails to produce useful malicious code, highlighting its current limitations. Conversely, generative AI holds promise for enhancing cybersecurity defense by improving comprehension, guiding incident response, and boosting human capabilities. Ultimately, the responsibility to use AI technologies ethically lies with humans, as these tools remain enablers rather than autonomous agents in both offensive and defensive contexts.

Elastic and Microsoft have strengthened their partnership by integrating Elastic solutions with Microsoft Azure services, aiming to enhance data management and infrastructure for users. This collaboration has led to the recognition of Elastic as part of the Azure Native ISV Service, which facilitates the provisioning and integration of independent software vendor solutions on Azure, streamlining operations and accelerating value delivery. Users can deploy managed instances of the Elastic Stack directly via the Azure marketplace, consolidating Azure logs into Elastic with ease. The partnership highlights successful use cases like Mr. Turing and Cranfield University, which have leveraged Elastic on Azure for efficient data management and cost reductions. Elastic Cloud on Azure offers scalable, unified solutions for various challenges, including SIEM for threat detection, log management, and cognitive search, with a straightforward onboarding process through Azure Marketplace. Elastic's features and functionalities are subject to its discretion, with no guaranteed release timeline.

As enterprises increasingly adopt Kubernetes for its scalability and self-managing capabilities, traditional monitoring approaches must adapt to address the complexities of this dynamic and distributed system. Kubernetes presents challenges due to its ephemeral nature and the extensive volume of data generated by containerized microservices across numerous pods, necessitating new strategies for effective observability. This includes the potential use of machine learning to correlate disparate signals and provide actionable insights. Additionally, the rise of managed Kubernetes services from major cloud providers offers varying levels of management and access, which influence monitoring needs. Tools like Elastic Observability aim to address these challenges by providing integrated solutions that consolidate telemetry data, offer customizable dashboards, and enable automated alerts, thereby facilitating better Kubernetes monitoring without requiring deep expertise.

Eddie Mitchell’s guide details the process of setting up the Elastic Stack using Docker Compose for local development and testing, focusing on rapid deployment with minimal effort. This first part of a two-part series covers configuring essential components of the Elastic Stack, including Elasticsearch, Logstash, Kibana, and Beats (ELK-B), with a particular emphasis on using Docker for quick setups and proof-of-concept environments. The article provides detailed instructions on setting up Docker containers, configuring security with certificates, and addresses common troubleshooting issues like virtual memory configuration. It explains the setup of individual components like Elasticsearch and Kibana, emphasizing the importance of security practices even in local environments. Additionally, it covers the use of Metricbeat and Filebeat for monitoring and log ingestion, respectively, using Docker's capabilities to integrate and manage these components efficiently. The tutorial is designed to provide a foundational understanding of establishing a local Elastic Stack environment, serving as a playground for testing Elastic ecosystem features.

The advent of 5G networks signifies a major technological advancement, transitioning from traditional physical infrastructure to virtualized, software-based solutions, and distributed cloud-based architectures, which present several challenges for operators. To address these, the Network Data Analytics Function (NWDAF) defined by 3GPP plays a crucial role in providing standardized data analytics and machine learning capabilities to manage complex network operations and enhance customer experience. Elastic and Wilab have partnered to offer robust, scalable solutions that integrate NWDAF, enabling operators to achieve end-to-end network observability, automation, and subscriber-centric analytics. Their collaboration focuses on providing vendor-independent, flexible approaches to network management, leveraging a library of predefined use cases for network assurance and optimization. This partnership empowers operators to handle the vast data generated by 5G networks, offering advanced analytics and automation capabilities to streamline operations, reduce costs, and deliver superior user experiences.

The Network Data Analytics Function (NWDAF) is a pivotal component in 5G networks that offers vendor-agnostic data analytics, providing telecom service providers with unbiased insights into network performance. This capability is essential as it helps optimize network efficiency, enhance customer experience, and support innovative service offerings across various industries such as healthcare, logistics, ecommerce, and smart cities. By leveraging data-driven decision-making, NWDAF enables service providers to stay competitive in an increasingly complex market. Its integration with vendor-agnostic analytics ensures a comprehensive view of network performance, allowing operators to make informed decisions without bias from specific technologies or vendors. As technology advances, NWDAF's potential to revolutionize connectivity and data analytics is significant, promising further industry transformation through artificial intelligence, machine learning, and cloud-based solutions.

Julie Baxter-Rudd, Elastic's Vice President of Global Customer Support, shares her insights and experiences in building a successful career in technical support over more than two decades. Her journey began as a support engineer intern at CA Technologies, where her curiosity and adaptability allowed her to advance through various roles, emphasizing the importance of a supportive team environment. Her career at Elastic, known for its strong team mentality and customer-centric approach, focuses on empowering her team to succeed through structured career mapping and development plans. Julie highlights the significance of change management, maintaining a growth mindset, providing calm support during crises, and finding the right work environment. She stresses the importance of self-awareness and pursuing work that energizes and fulfills, encouraging those in technical support to embrace opportunities for personal and professional growth.

In his blog post, John Uhlmann presents a taxonomy for endpoint security products, emphasizing the need for a nuanced understanding of their complementary feature sets, such as Endpoint Firewall, Antivirus (AV), Endpoint Protection Platform (EPP), Endpoint Detection and Response (EDR), and Data Loss Protection (DLP), rather than using "EDR" as a catch-all term. He highlights that endpoint security has evolved, with each layer having specific strengths and weaknesses, and discusses the importance of understanding which detection features are bypassed in security breaches. Uhlmann argues that while AV focuses on point-in-time scanning, EDR's strength lies in its retrospective detection capabilities, allowing historical analysis of threats. He also critiques the lack of comprehensive kernel callbacks in Windows, suggesting improvements for better defense. The blog underscores the importance of each security layer in providing defense-in-depth and encourages ongoing assessment of endpoint security products to ensure they meet current needs.

Artificial Intelligence for IT Operations (AIOps) is becoming increasingly vital for DevOps and site reliability engineering teams to manage the complexities and data volumes of modern cloud-native applications. As traditional IT monitoring tools struggle with the ephemeral and distributed nature of these environments, a unified full-stack observability platform like Elastic Observability, which integrates AIOps and machine learning, offers enhanced capabilities for real-time monitoring, anomaly detection, alert correlation, and root cause analysis. These technologies help reduce mean time to detection and resolution, decrease service downtime, and improve SLAs and customer experiences by filtering out noise, detecting anomalies, and automating issue resolution. As AI and ML continue to evolve, AIOps is poised to play a more autonomous role in data collection, analysis, and remediation, offering organizations a competitive advantage through improved operational efficiency and data-driven decision-making.

Industrial control systems (ICS) have traditionally operated in isolation, but the advent of Industry 4.0 has increased their connectivity, introducing new security challenges. These systems, crucial to sectors like energy, transportation, and telecommunications, integrate both information and operational technologies, necessitating unique security measures. Elastic Security and Zeek offer solutions to these challenges by providing robust network security, asset inventory management, and threat intelligence integration. Elastic's machine learning capabilities enhance anomaly detection, while Zeek's network detection frameworks ensure comprehensive monitoring of ICS protocols without disrupting operations. Additionally, Elastic Defend provides endpoint security, addressing the specific vulnerabilities of ICS environments, such as outdated software and lack of internet access. The use of threat intelligence and the MITRE ICS ATT&CK matrix further aids in understanding and mitigating potential threats, making ICS security more resilient.

The integration of Elastic with the Robot Operating System (ROS) offers a robust solution for managing the vast amounts of data generated by connected devices and sensors in the rapidly growing robotics industry, which is expected to reach a global market size of US$ 230 billion by 2025. As automation and robotics become more prevalent across industries such as manufacturing and healthcare, the need for effective data management and analysis is paramount. By utilizing a distributed architecture of nodes and a publish-subscribe messaging system, ROS simplifies the development of complex robotic systems, while Elastic provides a scalable platform for real-time data ingestion and analysis. This integration allows developers to harness insights from sensor data to improve robot performance, as exemplified by companies like MM Karton, REHAU, and Clearpath Robotics. The proposed architecture involves creating a ROS node that communicates sensor data to an MQTT broker, which is then ingested by Elastic's Filebeat into Elasticsearch, enabling real-time visualization and analysis. This collaboration between Elastic and ROS empowers developers to optimize operations and innovate within the robotics field, underscoring the importance of integrating modern data solutions with robotics technology.

Designing an observability solution for both public cloud and on-premise implementations involves multiple considerations to ensure effective monitoring, troubleshooting, and analysis capabilities, particularly important with the rise of microservices architecture. Key components include determining the types of data to collect, such as metrics, logs, events, traces, synthetic testing, and user feedback, and establishing efficient data collection methods. Architects must also devise a storage and retention strategy that balances cost, compliance, and query-ability, while ensuring scalability to handle large data volumes. Effective alert generation and notification systems are crucial for timely issue resolution, and data security measures such as encryption and access controls are essential to protect sensitive information. The solution should comply with relevant regulations, offer intuitive data visualization for insightful analysis, and integrate seamlessly with existing tools to enhance workflow efficiency. Ultimately, a well-designed observability solution not only improves system visibility and health but also strengthens security across both cloud and on-premise environments.

Version 8.7.1 of the Elastic Stack was released on May 2, 2023, and is recommended over previous versions 8.7.0 and 8.6.x due to its fixes for potential security vulnerabilities. Users are encouraged to upgrade to this latest version for enhanced security. More details about the fixed issues and a comprehensive list of changes for each product in this release can be found in the release notes.

Version 7.17.10 of the Elastic Stack was released on May 2, 2023, with a recommendation for users to upgrade to this latest version over earlier patch versions in the 7.17.x series. The release addresses several issues, and users can refer to the release notes for a comprehensive list of changes and fixes across the various products included in this update.

Public sector organizations are increasingly focusing on optimizing their log management due to heightened compliance requirements, recognizing logs as essential for mission-critical tasks, early issue detection, and problem resolution. However, managing logs is becoming more complex and costly, necessitating strategies like centralizing data onboarding, integrating logging with mission data, and employing automation for efficient data analysis. Organizations should also ensure historical data is both compliant and readily accessible, and break down information silos by unifying metrics, logs, and traces in a single observability solution. By establishing a robust logging foundation, these organizations can expand into application performance monitoring and advanced operations, enhancing their overall observability and decision-making capabilities.