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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.

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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

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

APMdigest's Predictions Series continues with 2026 Data Center Predictions — industry experts offer predictions on how data centers will evolve and impact business in 2026 ...

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