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Splunk Expands Machine Learning Across Portfolio

Splunk announced expanded machine learning capabilities across its product portfolio with the release of Splunk Enterprise 7.0, Splunk IT Service Intelligence (ITSI) 3.0, Splunk User Behavior Analytics (UBA) 4.0 and updates to Splunk Cloud.

Splunk also introduced an updated suite of solutions that apply analytics and machine learning to fraud and cloud monitoring use cases.

“Machine learning is critical to customer success and to the evolution of Splunk. Our seamlessly integrated capabilities open up machine learning to everyone, enabling our customers to better predict future outcomes and more effectively analyze their data,” said Richard Campione, Chief Product Officer, Splunk. “Data is a strategic advantage and organizations are looking for the fastest, most efficient way to turn data into answers. With machine learning and metrics advancements that anyone can use, Splunk Enterprise 7.0 and Splunk Cloud powerfully deliver mission-critical answers faster and easier than ever before.”

Splunk Enterprise 7.0 and Splunk Cloud help customers better monitor, investigate and gain intelligence with their data, while delivering massive improvement in performance and scale. Support for metrics accelerates monitoring and alerting by at least 20x, and optimizations to core search technology deliver 3x speed improvement. With these enhancements, customers can use the Splunk platform to predict future IT, security and business outcomes through integrated machine learning techniques backed by powerful, extensible algorithms. These machine learning advances enable users to collect, prepare, transform, explore, visualize and publish data insights.

Splunk also announced new, advanced machine learning capabilities for its existing premium-packaged solutions, including:

- Splunk ITSI 3.0: The latest version of Splunk ITSI revolutionizes event monitoring by combining service context with machine learning to help identify existing and potential issues, prioritize restoration of business-critical services and deliver analytics-driven IT operations. Splunk ITSI 3.0 applies service context, including dependencies, to events and employs machine learning to reduce the noise of alert fatigue and surface only the most critical information.

- Splunk UBA 4.0: The new version of Splunk UBA enables customers to create and load their own machine learning models to identify custom anomalies and threats via Splunk UBA’s new software development kit (SDK). This capability opens up Splunk UBA to the world, giving users more power to detect insider attacks and automate correlation of anomalous behavior into high fidelity threats.

- Machine Learning Toolkit: Free to any customer, the Splunk Machine Learning Toolkit (MLTK) is a data science application that anyone can use to predict future IT, security and business outcomes. Recent updates include machine learning model management, which integrates user permissions via an intuitive user interface. In addition, the MLTK now includes public machine learning APIs for open source and proprietary algorithms, and a data prep module to help customers prepare and clean their data before initiating machine learning modeling.

With increased demand for premium-packaged solutions, Splunk is also announcing new and updated solutions to tackle specific customer security needs in security and IT operations:

- Splunk ES Content Update: Bringing human intelligence and machine data closer, Splunk Enterprise Security (ES) Content Update is a new subscription service that offers pre-packaged security content to Splunk ES customers. Splunk ES Content Update will regularly deliver dynamic new content to security practitioners to detect specific threats, help security teams investigate threats and manage the decision-making process to make it easier to choose responses.

- Security Essentials for Fraud Detection: Splunk Security Essentials for Fraud Detection is a free Splunk app that guides customers on how to use Splunk to identify and investigate different types of fraud, including healthcare, payment card and transactional fraud. Using Splunk MLTK and a range of Splunk data analysis functions, Splunk Security Essentials for Fraud Detection makes it easier for customers to understand and adapt fraud use cases for their own environments.

- Splunk Insights for AWS Cloud Monitoring: Also available in the Amazon Marketplace as an Amazon Machine Image (AMI), Splunk Insights for AWS Cloud Monitoring provides organizations with an analytics-based approach to cloud monitoring. This solution offers real-time awareness of performance, security, operational and cost management insights from Amazon Web Services (AWS).

- Splunk Insights for Ransomware: Splunk Insights for Ransomware is a solution priced per user that provides organizations with real-time insights for proactive assessment and rapid investigation of potential ransomware threats.

- Booz Allen Hamilton Cyber4Sight for Splunk: A solution which empowers security analysts and threat hunters with actionable threat intelligence, Cyber4Sight for Splunk is now generally available. The new app combines cyber insights and security intelligence from Booz Allen’s threat intelligence service with analytics-driven security insights from Splunk ES.

Splunk Enterprise 7.0 is available today, with Splunk ITSI 3.0 and UBA 4.0 available next month. The new version of Splunk Cloud will be available by January 2018.

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New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

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Splunk Expands Machine Learning Across Portfolio

Splunk announced expanded machine learning capabilities across its product portfolio with the release of Splunk Enterprise 7.0, Splunk IT Service Intelligence (ITSI) 3.0, Splunk User Behavior Analytics (UBA) 4.0 and updates to Splunk Cloud.

Splunk also introduced an updated suite of solutions that apply analytics and machine learning to fraud and cloud monitoring use cases.

“Machine learning is critical to customer success and to the evolution of Splunk. Our seamlessly integrated capabilities open up machine learning to everyone, enabling our customers to better predict future outcomes and more effectively analyze their data,” said Richard Campione, Chief Product Officer, Splunk. “Data is a strategic advantage and organizations are looking for the fastest, most efficient way to turn data into answers. With machine learning and metrics advancements that anyone can use, Splunk Enterprise 7.0 and Splunk Cloud powerfully deliver mission-critical answers faster and easier than ever before.”

Splunk Enterprise 7.0 and Splunk Cloud help customers better monitor, investigate and gain intelligence with their data, while delivering massive improvement in performance and scale. Support for metrics accelerates monitoring and alerting by at least 20x, and optimizations to core search technology deliver 3x speed improvement. With these enhancements, customers can use the Splunk platform to predict future IT, security and business outcomes through integrated machine learning techniques backed by powerful, extensible algorithms. These machine learning advances enable users to collect, prepare, transform, explore, visualize and publish data insights.

Splunk also announced new, advanced machine learning capabilities for its existing premium-packaged solutions, including:

- Splunk ITSI 3.0: The latest version of Splunk ITSI revolutionizes event monitoring by combining service context with machine learning to help identify existing and potential issues, prioritize restoration of business-critical services and deliver analytics-driven IT operations. Splunk ITSI 3.0 applies service context, including dependencies, to events and employs machine learning to reduce the noise of alert fatigue and surface only the most critical information.

- Splunk UBA 4.0: The new version of Splunk UBA enables customers to create and load their own machine learning models to identify custom anomalies and threats via Splunk UBA’s new software development kit (SDK). This capability opens up Splunk UBA to the world, giving users more power to detect insider attacks and automate correlation of anomalous behavior into high fidelity threats.

- Machine Learning Toolkit: Free to any customer, the Splunk Machine Learning Toolkit (MLTK) is a data science application that anyone can use to predict future IT, security and business outcomes. Recent updates include machine learning model management, which integrates user permissions via an intuitive user interface. In addition, the MLTK now includes public machine learning APIs for open source and proprietary algorithms, and a data prep module to help customers prepare and clean their data before initiating machine learning modeling.

With increased demand for premium-packaged solutions, Splunk is also announcing new and updated solutions to tackle specific customer security needs in security and IT operations:

- Splunk ES Content Update: Bringing human intelligence and machine data closer, Splunk Enterprise Security (ES) Content Update is a new subscription service that offers pre-packaged security content to Splunk ES customers. Splunk ES Content Update will regularly deliver dynamic new content to security practitioners to detect specific threats, help security teams investigate threats and manage the decision-making process to make it easier to choose responses.

- Security Essentials for Fraud Detection: Splunk Security Essentials for Fraud Detection is a free Splunk app that guides customers on how to use Splunk to identify and investigate different types of fraud, including healthcare, payment card and transactional fraud. Using Splunk MLTK and a range of Splunk data analysis functions, Splunk Security Essentials for Fraud Detection makes it easier for customers to understand and adapt fraud use cases for their own environments.

- Splunk Insights for AWS Cloud Monitoring: Also available in the Amazon Marketplace as an Amazon Machine Image (AMI), Splunk Insights for AWS Cloud Monitoring provides organizations with an analytics-based approach to cloud monitoring. This solution offers real-time awareness of performance, security, operational and cost management insights from Amazon Web Services (AWS).

- Splunk Insights for Ransomware: Splunk Insights for Ransomware is a solution priced per user that provides organizations with real-time insights for proactive assessment and rapid investigation of potential ransomware threats.

- Booz Allen Hamilton Cyber4Sight for Splunk: A solution which empowers security analysts and threat hunters with actionable threat intelligence, Cyber4Sight for Splunk is now generally available. The new app combines cyber insights and security intelligence from Booz Allen’s threat intelligence service with analytics-driven security insights from Splunk ES.

Splunk Enterprise 7.0 is available today, with Splunk ITSI 3.0 and UBA 4.0 available next month. The new version of Splunk Cloud will be available by January 2018.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...