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Splunk AI Released

Splunk announced Splunk AI, a collection of new AI-powered offerings to enhance its unified security and observability platform.

Splunk AI combines automation with human-in-the-loop experiences, so organizations can drive faster detection, investigation and response while controlling how AI is applied to their data. Leaning into its lineage of data visibility and years of innovation in AI and machine learning (ML), Splunk continues to enrich the customer experience by delivering domain-specific insights through its AI capabilities for security and observability.

Splunk AI strengthens human decision-making and threat response through assistive experiences. The offerings empower SecOps, ITOps and engineering teams to automatically mine data, detect anomalies and prioritize critical decisions through intelligent assessment of risk, helping to minimize repetitive processes and human error. Splunk AI optimizes domain-specific large language models (LLMs) and ML algorithms built on security and observability data, so SecOps, ITOps and engineering teams are freed up for more strategic work - helping to accelerate productivity and lower costs. Looking forward, Splunk is committed to remaining open and extensible as it integrates AI into its platform, so organizations can extend Splunk AI models or use home-grown and third party tools.

“Splunk’s purpose is to build a safer, more resilient digital world, and this includes the transparent usage of AI,” said Min Wang, CTO at Splunk. “Looking forward, we believe AI and ML will bring enormous value to security and observability by empowering organizations to automatically detect anomalies and focus their attention where it’s needed most. Our Splunk Al innovations provide domain-specific security and observability insights to accelerate detection, investigation and response while ensuring customers remain in control of how AI uses their data.”

Splunk AI Assistant leverages generative AI to provide an interactive chat experience and helps users author Splunk Processing Language (SPL) using natural language. The app preview fosters an immersive experience where users can ask the AI chatbot to write or explain customized SPL queries to increase their Splunk knowledge. Splunk AI Assistant improves time-to-value and helps make SPL more accessible, further democratizing an organization’s access to, and insights from, its data.

The embedded AI offerings, highlighted below, enable organizations to drive more accurate alerting to build digital resilience:

■ With a few clicks, Splunk App for Anomaly Detection provides SecOps, ITOps and engineering teams with a streamlined end-to-end operational workflow to simplify and automate anomaly detection within their environment.

■ The IT Service Intelligence 4.17 features greater detection accuracy and faster time-to-value:

- Outlier Exclusion for Adaptive Thresholding detects and omits abnormal data points or outliers (such as network disruptions or outage spikes) for more precise dynamic thresholds to drive accurate detection within one’s technology environment.

- The new ML-Assisted Thresholding preview uses historical data and patterns to create dynamic thresholds with just one click, helping to provide more accurate alerting on the health of an organization's technology environment.

The ML-powered foundational offerings provide organizations access to large, richer sets of information by extending solutions built on the Splunk platform, so they can drive data-driven decisions:

■ The Splunk Machine Learning Toolkit (MLTK) 5.4 provides guided access to ML technology to users of all levels and is one of the most downloaded Splunkbase apps, with over 200k downloads. Through leveraging techniques like forecasting and predictive analytics, SecOps, ITOps and engineering teams can unlock richer ML-powered insights. The new release builds on the open, extensible nature of Splunk AI by enabling customers to bring their externally trained models into Splunk.

■ Now available on Splunkbase, Splunk App for Data Science and Deep Learning (DSDL) 5.1 extends MLTK to provide access to additional data science tools to integrate advanced custom machine learning and deep learning systems with Splunk. This release includes two AI assistants that allow customers to leverage LLMs to build and train models with their domain specific data to support natural language processing.

All new offerings within Splunk AI are now generally available, with the exception of Splunk AI Assistant and ML-Assisted Thresholding which are available in preview.

The Latest

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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 ...

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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 ...

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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 AI Released

Splunk announced Splunk AI, a collection of new AI-powered offerings to enhance its unified security and observability platform.

Splunk AI combines automation with human-in-the-loop experiences, so organizations can drive faster detection, investigation and response while controlling how AI is applied to their data. Leaning into its lineage of data visibility and years of innovation in AI and machine learning (ML), Splunk continues to enrich the customer experience by delivering domain-specific insights through its AI capabilities for security and observability.

Splunk AI strengthens human decision-making and threat response through assistive experiences. The offerings empower SecOps, ITOps and engineering teams to automatically mine data, detect anomalies and prioritize critical decisions through intelligent assessment of risk, helping to minimize repetitive processes and human error. Splunk AI optimizes domain-specific large language models (LLMs) and ML algorithms built on security and observability data, so SecOps, ITOps and engineering teams are freed up for more strategic work - helping to accelerate productivity and lower costs. Looking forward, Splunk is committed to remaining open and extensible as it integrates AI into its platform, so organizations can extend Splunk AI models or use home-grown and third party tools.

“Splunk’s purpose is to build a safer, more resilient digital world, and this includes the transparent usage of AI,” said Min Wang, CTO at Splunk. “Looking forward, we believe AI and ML will bring enormous value to security and observability by empowering organizations to automatically detect anomalies and focus their attention where it’s needed most. Our Splunk Al innovations provide domain-specific security and observability insights to accelerate detection, investigation and response while ensuring customers remain in control of how AI uses their data.”

Splunk AI Assistant leverages generative AI to provide an interactive chat experience and helps users author Splunk Processing Language (SPL) using natural language. The app preview fosters an immersive experience where users can ask the AI chatbot to write or explain customized SPL queries to increase their Splunk knowledge. Splunk AI Assistant improves time-to-value and helps make SPL more accessible, further democratizing an organization’s access to, and insights from, its data.

The embedded AI offerings, highlighted below, enable organizations to drive more accurate alerting to build digital resilience:

■ With a few clicks, Splunk App for Anomaly Detection provides SecOps, ITOps and engineering teams with a streamlined end-to-end operational workflow to simplify and automate anomaly detection within their environment.

■ The IT Service Intelligence 4.17 features greater detection accuracy and faster time-to-value:

- Outlier Exclusion for Adaptive Thresholding detects and omits abnormal data points or outliers (such as network disruptions or outage spikes) for more precise dynamic thresholds to drive accurate detection within one’s technology environment.

- The new ML-Assisted Thresholding preview uses historical data and patterns to create dynamic thresholds with just one click, helping to provide more accurate alerting on the health of an organization's technology environment.

The ML-powered foundational offerings provide organizations access to large, richer sets of information by extending solutions built on the Splunk platform, so they can drive data-driven decisions:

■ The Splunk Machine Learning Toolkit (MLTK) 5.4 provides guided access to ML technology to users of all levels and is one of the most downloaded Splunkbase apps, with over 200k downloads. Through leveraging techniques like forecasting and predictive analytics, SecOps, ITOps and engineering teams can unlock richer ML-powered insights. The new release builds on the open, extensible nature of Splunk AI by enabling customers to bring their externally trained models into Splunk.

■ Now available on Splunkbase, Splunk App for Data Science and Deep Learning (DSDL) 5.1 extends MLTK to provide access to additional data science tools to integrate advanced custom machine learning and deep learning systems with Splunk. This release includes two AI assistants that allow customers to leverage LLMs to build and train models with their domain specific data to support natural language processing.

All new offerings within Splunk AI are now generally available, with the exception of Splunk AI Assistant and ML-Assisted Thresholding which are available in preview.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...