We’re pleased to announce the availability of Elastic Cloud Serverless on Google Cloud in the following regions: 

• GCP Belgium (europe-west1)

• GCP Mumbai (asia-south1)

Elastic Cloud Serverless provides the fastest way to start and scale observability, security, and search solutions without managing infrastructure. Built on the industry-first Search AI Lake architecture — which leverages Google Cloud Storage — it combines vast storage, separate storage and compute, low-latency querying, and advanced AI capabilities to deliver uncompromising speed and scale.

When it comes to generative AI, enterprises need to think big. Shaving a few seconds off the time needed to draft an email is helpful, but the journey to real value begins when you apply AI at the enterprise level. A new partnership between Accenture and Elastic combines technical expertise and strategic excellence, enabling businesses to build the data foundations for a successful AI future.

Optimizing search relevance with retrieval and reranking

Mayzak adds, “Deploying a vector database and transforming enterprise data into embeddings is only the first step in making RAG and LLM workflows effective. The real challenge lies in optimizing search relevance and ensuring that AI retrieves the most contextually appropriate and high-value information.” 

To enhance retrieval quality, Elastic uses multistage retrieval, where an initial recall step using vector search or a combination of keyword and vector-based techniques, a hybrid approach, is followed by reranking models that evaluate the retrieved documents for accuracy, contextual fit, and informativeness. 

“Elastic puts heavy emphasis on fine-tuned transformer models to filter out noise, ensuring that the AI system prioritizes the most useful, trustworthy responses,” says Mayzak. 

Tools such as Learning to Rank also support result accuracy, whether at the individual or cohort level, giving organizations flexibility when targeting different audiences. As the volume of data increases, the system learns which features have the greatest impact on relevance, allowing them to be prioritized in the model.

Accenture takes an equally diligent approach to search relevance. Rodriguez says, “We spend a lot of our time evaluating RAG and generative AI applications. To achieve 90%–95% levels of accuracy, you need a holistic process that shines light into every corner of the process.”  

A good example is Accenture’s AI-powered search “operating room” process, which brings together experts from various domains (data ingestion, query construction, prompting, business) to diagnose and resolve accuracy issues using automated and insight-driven methods. 

Rodriguez draws a parallel with a neurosurgeon operating on a patient. “Experts act like surgeons, poking and prodding the application, while other specialists observe and analyze.” This approach enables the team to pinpoint and address obstacles to search accuracy, which often relate to data quality, context, or the way queries are formulated. Automated methods can then be implemented to monitor the performance of the application over time.

Elastic’s developer experience is also fundamental to the partnership. “Elastic prioritizes how developers move from initial setup to production deployment. We strive to provide everything they need to achieve results quickly,” says Mayzak. This includes tools like Elasticsearch AI Playground that streamlines the process of building prototypes and launching production applications.  

Many organizations are already reaping the benefits. Reed, the UK’s largest recruiter, is using Elastic vector search technology to save employers 20% of the cost per hire. Korea’s leading IT services company, LG CNS, has deployed Elastic generative AI, boosting search relevance by 95% and accelerating retrieval by 50% as a result. 

“Real industry reinvention demands deep intellectual investment, and that's precisely what the Accenture-Elasticsearch partnership delivers,” says Mayzak. “We’re combining data-led technology with deep industry knowledge to get generative AI projects into production fast.”

Rodriguez agrees with the need to deliver measurable business value. By combining Elastic’s AI-native search capabilities with Accenture’s industry expertise, businesses can move beyond the hype and into an AI-powered future that’s both transformative and profitable.

Learn more about generative AI on Elastic’s Search AI Platform, or start a free 14-day trial.

Source:

1. Seagate, “Seagate’s ‘Rethink Data’ Report Reveals That 68% Of Data Available To Businesses Goes Unleveraged,” 2020.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use. 

Elastic, Elasticsearch, and associated marks are trademarks, logos or registered trademarks of Elasticsearch N.V. in the United States and other countries. All other company and product names are trademarks, logos or registered trademarks of their respective owners.

Within the US government defense and intelligence space, there is an increasing need to integrate artificial intelligence (AI) and machine learning (ML) into monitoring and IT resilience for complex mission systems. However, teams must first overcome substantial data challenges, such as silos, security gaps, and legacy IT. To do so, many national security organizations rely on developers stepping into site reliability engineering (SRE) roles, who then need to balance performance optimization, cost-efficiency, and system reliability amid exponential data growth. 

The strategic collaboration between Anthropic, Amazon, and Elastic provides advanced AI capabilities for enhanced observability, anomaly detection, and root cause analysis for our joint Top Secret missions. The partnership brings AI and ML together to overcome data challenges through automation, faster problem resolution, and contextual insights. As a result, SREs can better manage performance across distributed systems and ensure system reliability.

Elastic's Search AI Platform, with its unified approach, affordable data tiering, and AI-driven automation, significantly improves user satisfaction (by 69%) and developer productivity (by 75%).¹ This integration of AI and ML in observability is crucial for organizations to achieve operational resilience, security risk mitigation, and enhanced customer experience in today's complex IT landscapes.

The modern cyber battlefield doesn't respect organisational boundaries. Across defence networks, critical structured, unstructured, and semi-structured data sits distributed and siloed in specialised environments — from classified intelligence systems to operational command platforms and tactical edge devices to headquarters. In the public sector, for example, 65% of leaders struggle to use data continuously in real time and at scale, according to a recent Elastic study.

The defence establishment faces just such challenges, and the growth in the volume of security data generated across multi-domain operations isn’t slowing. When threats move at machine speed across networks, human analysts need to collaborate effectively across interoperable, if disparate, systems. The need is to improve visibility into individual domains and enable genuine collaboration across them, without compromising security or operational control.

Rather than centralising data — and wrestling with all the challenges of that approach — a data mesh instead embraces a distributed model built on four principles:

• Domain ownership ensures that the teams most familiar with the data maintain responsibility for it.

• Data as a product means information is well documented and accessible to authorised users.

• Self-service platforms enable teams to discover and use data without IT bottlenecks.

• Federated governance ensures security and compliance across the entire ecosystem.

Cross-cluster search is a key feature in Elastic’s data mesh approach, allowing teams to search across distributed environments without moving data. Analysts can execute a single query that securely retrieves results from multiple sources while respecting data access controls. This approach eliminates expensive data duplication across systems and offers up to 90% productivity improvements in IT operations. Unlike traditional approaches that simply forward queries to disparate systems, cross-cluster search provides a unified indexing layer: Data is indexed once and then available to any authorised user. This eliminates performance bottlenecks and inconsistent security models that plague other approaches, creating faster collaboration with stronger security. Data owners maintain control of their assets.

Shared awareness accelerates threat response

For organisations like the MOD, a global data mesh approach offers significant advantages, allowing data to remain at its source while being searchable. Cross-cluster search excels in these challenging environments. It enables interoperability between previously disconnected systems, making it a technical enabler of the broader interoperability goal. 

Queries can span geographical and organisational boundaries so that when an analyst needs to correlate threat intelligence across multiple domains, they can run a single search that returns unified results. This dramatically reduces response times during critical incidents. The data itself never moves, limiting or removing the requirement for duplication. Only the query and its matching results traverse the network, significantly reducing bandwidth requirements and maintaining data sovereignty. 

For defence teams facing constrained network environments, this efficient approach to data management delivers both operational and cost benefits through a unified platform approach instead of multiple disconnected tools.

We’re thrilled to share that Elastic Security achieved a score of 100% in the recent AV-Comparatives Business Security Test.

Why the AV-Comparatives Business Security Test matters

AV-Comparatives is a highly respected organization that conducts rigorous, independent testing specifically for business endpoint security solutions. Unlike consumer antivirus tests, AV-Comparatives evaluations go beyond basic malware detection. The Real-World Protection Test simulates real-world attack scenarios, including malicious websites, in a multipronged approach that evaluates a product’s ability to safeguard businesses from contemporary threats. Earning top honors in AV-Comparatives' Business Security Test signifies a solution's effectiveness in protecting organizations.

The test simulates 220 distinct and complex attack scenarios that replicate the tactics and techniques of contemporary threat actors. The Malware Protection Test assesses a security product’s ability to protect a system against infection by malicious files before, during, or after execution. The evaluation utilized a substantial dataset of 1,018 unique and recently identified malware samples, representing the current threat landscape.

Elastic Security earned perfect scores in both critical categories, demonstrating its robust capabilities to accurately identify and prevent a wide spectrum of sophisticated threats, including both targeted attacks and prevalent malware.

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Real-World Protection Test: Elastic Security excelled in the Real-World Protection Test, achieving 100% coverage and demonstrating exceptional defense against current cyber attacks. This demonstrates how Elastic gives your business the necessary protection to effectively combat the newest threats, reducing the likelihood of data breaches and operational interruptions.

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100% protection in Malware Protection Test: Elastic Security was the sole participant among 17 vendors to achieve a perfect 100% score in both the Real-World Protection Test and the Malware Protection Test. Our advanced threat detection engine is exceptionally effective at identifying and mitigating malware, proactively combating the increasingly sophisticated malware environment. This perfect score across both critical evaluation criteria highlights not only the efficacy of Elastic Security’s solutions in practical, real-world scenarios but also its comprehensive capabilities in identifying and neutralizing a broad spectrum of malicious software.

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Our consistently excellent results demonstrate our ongoing commitment to delivering dependable protection for businesses of all scales. Elastic Security is a proven solution for safeguarding your organization's data against threats.

Many defence organisations operate in an environment where dozens of disjointed security tools create financial and operational inefficiency. Much of the organisation’s spending is dedicated to simply managing this complexity. There’s also considerable complexity in ensuring compliance across MOD and NATO standards. Managing multitenant cybersecurity contracts adds an additional administrative burden, while legacy infrastructure demands increased operation and maintenance costs. Meanwhile, defence contractors face mounting threats from malicious cyber attacks and sophisticated ransomware.

Fragmentation like this creates non-interoperable, siloed systems, forcing manual correlation across platforms. Worse, it can slow threat response and create security blind spots. When incidents occur, costs multiply as analysts navigate multiple interfaces to form a complete picture of the threat.

Breaking silos and reducing defence costs through a data mesh approach

The MOD faces unique challenges with data silos across classification levels and operational domains. Elastic’s data mesh approach addresses siloed data issues by enabling secure queries across multiple data repositories without moving it, copying it, or compromising security boundaries. This approach aligns with the MOD's Defence Data Strategy¹ by breaking down contractual and technical silos while maintaining appropriate access controls.

Elastic’s Search AI Platform eliminates data silos by giving all your data a common language, making it easier to search, understand, analyse, and act on information from different sources. The result is interoperability between data formats and classified and unclassified networks, which is critical for defence operations that must maintain separation while enabling appropriate information sharing.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use. 

Elastic, Elasticsearch, and associated marks are trademarks, logos or registered trademarks of Elasticsearch N.V. in the United States and other countries. All other company and product names are trademarks, logos or registered trademarks of their respective owners.

Adhish Thite’s tech journey has taken him from India to Silicon Valley, North Carolina, and back. Now based in one of India’s top IT hubs, Pune, Adhish values the global perspective he has gained over the years.

Adhish’s natural curiosity and passion for innovation led him to Elastic, where he’s the lead AI engineer managing an equally passionate team while finding time to work on his personal passions, too.

https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/bltd4fd3d86327763f8/68420a9a22d569099b358397/1-adhish-thite-720x420-text-rounded.png,1-adhish-thite-720x420-text-rounded.png

Very early on, Adhish played the role of his family’s personal tech support. Growing up, he found himself dismantling the electronic gadgets his father often brought home, trying to understand how they worked. Picture a young Adhish, surrounded by wires and parts, wearing a giant grin.

"My dad was a connoisseur of all good electronic items," Adhish recalls. "I used to break them apart, and I got scolded a lot, but that was how my curiosity [started] ticking. Over time, this translated into expertise … I got to know how things work at a very young age." His fascination with technology earned him the nickname “Tech-nificent Adhish.”

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Though he is still guided by his childhood curiosity and bolstered by his creativity, Adhish's workspace mirrors his organized and methodical approach to work. He’s not the type to work with his laptop alone. "I cannot work without my setup," he insists.

His desk, equipped with a MacBook Pro M3 with 64GB of RAM hooked up to an Apple Studio Display, has been specifically selected to meet his needs. Acknowledging that this might be a hot take to some, he notes, "I just don't know any other system, because I've been using [a MacBook] for a decade now. I think it's the best for any developer to get started, because [it has] everything that a developer needs. You don't need anything else if you have a Mac." 

Adhish went through at least five monitors before finding the one that met all his criteria. The 27-inch Apple Studio Display is the centerpiece of his workspace, allowing him to focus on coding for hours in 5K resolution. His secret to avoiding eye strain? The anti-reflective, nano-texture glass overlay. 

But it's not always about having the latest technology. “Nothing beats pen and paper,” he says while holding up a small journal he keeps on his desk. Every day, he journals three wins: a physical win (today it was working out), a spiritual win (he meditates or plays the piano every day), and a mental win (15 minutes learning German). “I need to get those in to mark it as a complete day.”

To accomplish the day's goals, Adhish employs the Pomodoro Technique to enhance his productivity. One of the most cherished items on his desk is a timer he uses to track his working time. "I work for 25 minutes and then take a 5-minute break," he explains. This method not only helps him maintain focus but also helps to prevent burnout, a lesson he learned during the pandemic.

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In contrast to his strict work schedule, music gives Adhish a creative outlet that feeds his intellectual curiosity. "I play five instruments, all self-taught," he shares. From the piano to the Indian bamboo flute and the tabla, Adhish occasionally shares his musical endeavors on YouTube. However, most of his performances remain private. "Music is deeply personal to me," he says, so he treats it like meditation. During the pandemic, he even picked up the ukulele.

But even music takes a backseat when he works. “When I'm locked in coding or developing, I just prefer silence,” he says. All the elaborate tricks and tools fall away and it’s just Adhish and his code. “I need the doors closed, the windows closed. I need just one focus light and myself. That's all I need.”

As the lead AI engineer at Elastic, Adhish’s responsibilities include ElasticGPT, an internal, secure, and private generative AI solution designed specifically for Elasticians by Elasticians. "My boss Jay had this vision. Because we are at the forefront of analytics and RAG, why not build something of our own?" he explains. "Like drinking our own champagne. So that’s how the idea was born." The positive feedback from users has been a source of pride for Adhish and his team. "It makes us proud of what we are doing," he shares. To date, ElasticGPT has facilitated over 80,000 conversations with a 98% employee satisfaction rate.

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An active member of the developer community, Adhish cultivates his curiosity by sharing it with others. He engages with fellow developers through various platforms, including the Elastic Developer Community, GitHub, LinkedIn, and X. He also participates in the "build in public" movement, where developers build and deploy a project on day one and then collect continuous feedback from the community to help polish their products.

This collaborative spirit blends his expertise and his creativity. The result? A leader who knows how to ask for help. "When I'm working on side projects, I often reach out to the community for help," he explains. "The responses are pretty great, and it's a valuable resource."

Even after all his years in tech, Adhish is still that boy, surrounded by wire parts, curious and excited about the possibilities of tech."There has never been a better time to get started with Elasticsearch," he asserts. With advancements in AI and machine learning, the possibilities are endless. "If you're serious about using GenAI and turning data into actionable insights, Elasticsearch is the way to go."

I was recently introduced to a TV show called Young Sheldon. I’ve never seen The Big Bang Theory, so I wasn’t sure what to expect. That said, this post isn’t really about the show. I’ll be using a dataset related to it, but the actual data could’ve been anything: business-related, observability-related, security-related — you name it. For those who know me, you know I’m always going to find a way to infuse a little semi-chaotic fun. So, let’s get into it.

https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/bltbb9f6702b3952fc5/6841a4da116bcfc2812211a1/blog-create-data-view.png,blog-create-data-view.png

Sheldon selects the data view and confirms that the custom data view ID is correctly set.

Note: This can be quickly verified through the browser URL.

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Georgie creates a data view using the demo-young-sheldon index. The following parameters are used:

• Name: demo-young-sheldon-test

• Index Pattern: demo-young-sheldon

• Custom Data View ID: N/A (Blank)

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Georgie selects the data view and confirms that the data view ID, because it was left blank, has been set to the random automatically generated string.

Note: This can be quickly verified through the browser URL.

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Sheldon (demo-young-sheldon) and Georgie (demo-young-sheldon-test) each create visualizations using their respective data views and add them to a shared dashboard.

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Sheldon and Georgie accidentally delete their data views and when they navigate back to the dashboard, they notice their visualizations are broken.

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Sheldon strategically assigned a custom data view ID to his data view, making recovery simple. To restore his visualization, he simply recreates the data view and reuses the same custom data view ID: demo-young-sheldon.

The following parameters are used:.

• Name: demo-young-sheldon-recreated

• Index Pattern: demo-young-sheldon

• Custom Data View ID: demo-young-sheldon

Note: In this walkthrough, I’ve slightly modified the data view name for clarity and to differentiate it from earlier steps (Name: demo-young-sheldon → demo-young-sheldon-recreated).

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Sheldon returns to the shared dashboard and confirms that his visualization has been successfully restored.

Georgie can follow a similar process to restore his visualization. However, he must set the custom data view ID to match the randomly generated string shown in the image on the right.

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You might be thinking, "Well, I could just recreate the `demo-young-sheldon-test data view and manually assign the same randomly generated data view ID referenced in the visualization."

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And you’d be absolutely right. That approach does work — in theory.

But now, let’s scale the scenario.

Imagine you have 30 users, and 10 of them each create their own variation of the demo-young-sheldon data view, each with a different, randomly generated data view ID. They build hundreds of visualizations using these various data views, which are then distributed across multiple dashboards.

Later, someone reviewing the environment sees 10 data views with similar names and the same index pattern associated with all of them. Assuming they’re duplicates, they delete 9 of them unaware that each one is uniquely tied to specific visualizations via data view ID.

Now, you’ve got a problem. Many of these visualizations are spread across multiple dashboards, and in many cases, the users are unaware that the visualizations within a single dashboard are not using the same data view. For example, a user notices the following data views: zeek-1, zeek-2, zeek-3, zeek-4, and zeek-5.

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Since all of the data views point to the same index pattern, users often don’t think twice when building a visualization and adding it to a shared dashboard.

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As a result, the dashboard ends up containing visualizations that rely on different data views, each with its own randomly generated ID.

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These inconsistencies can cause visualizations to break when one of the associated data views is deleted, as demonstrated earlier in the Young Sheldon walkthrough.

https://static-www.elastic.co/v3/assets/bltefdd0b53724fa2ce/bltc0747f6590193050/68409bfb3010e372a4e231ab/spaces.png,spaces.pngStep 3: Document it and keep it simple

Document the naming convention and strategy in a shared knowledge repository for easy access and reference. A consistent naming approach helps keep your environment organized and makes it significantly easier to recreate or identify data views, especially when working in collaborative teams.

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Note: If you want to get a little fancy, you can document the strategy directly in Kibana using a text panel as shown in the image above. Alternatively, you can use a team wiki, GitHub repo, or even a simple spreadsheet (whatever works best for your workflow).

We’re thrilled to share that Elastic has achieved the AWS Education ISV Partner Competency. This prestigious designation recognizes Elastic as an Amazon Web Services (AWS) partner that has proven expertise in delivering high-quality solutions that help education institutions support successful student outcomes while protecting security and privacy.

Achieving the AWS Education ISV Partner Competency underscores Elastic's commitment to excellence and reliability in the education sector. As a recognized partner, Elastic is proven to support a wide range of learning organizations around the world, including K–12 and higher education institutions, to use Elastic for search, generative AI, data analytics, IT monitoring, cybersecurity, and more.

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Why are education organizations around the world choosing the Search AI Platform to empower their students and faculty?

• Generative AI optimized: Tailor GenAI experiences with the industry's first Search AI Lake. Experience low-latency, AI-optimized architecture, including a native vector database.

• Deployment, your way: Run our platform and solutions however you like, or leave the operational overhead to us and concentrate on getting the relevant answers, faster.

• Speed, scale, relevance: Harness the power of search and AI to find the right answers that matter from petabytes of data in milliseconds.
  Open and extensible: Accelerate insights using your tools and solutions that fit seamlessly into our flexible platform, adaptable to the unique needs of your organization.

Competency collection

At Elastic, we recognize that the technology landscape is always evolving. To ensure our customers stay ahead, we are constantly striving to help you build transformative applications, proactively resolve observability issues, and address complex security threats — all with the power of Search AI.

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This dedication has resulted in a stream of competency designations from AWS: 

1. AWS Government Competency: Demonstration of Elastic’s adeptness in helping you advance digital transformation in government institutions through Search AI and cloud innovation

2. AWS Financial Services Competency: Evidence of Elastic’s ability to assist you with the technology and regulatory nuances of banking, capital markets, and insurance use cases

3. AWS Generative AI Competency: Recognition of Elastic's expertise in providing you with secure, scalable AI solutions that deliver documented success

4. AWS Security Competency: Verification of Elastic’s ability to help you protect your most sensitive data and applications in the cloud with a specialization in threat detection and response (SIEM, SOAR, and XDR)

5. AWS Data and Analytics Competency: Validation of Elastic’s expertise to guide you on how to collect, store, govern, and analyze your data at any scale

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