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Splunk Introduces Advanced AI Enhancements for Observability, Security and IT Service Intelligence

Splunk introduced a collection of AI tools across its product portfolio to enable organizations to speed up routine tasks and enhance their ability to get insights from data fast.

Splunk’s generative AI-powered Assistants can help every user become a security and observability expert.

Splunk has expanded its AI capabilities with new generative AI assistants in Observability Cloud and Security, providing customers with improved IT visibility and enhanced proactive threat mitigation capabilities. Additionally, the company has made its Splunk AI Assistant for SPL generally available, helping customers derive insights from Splunk using natural language. Moreover, Splunk unveiled new AI capabilities for IT Service Intelligence (ITSI), including Configuration Assistant, aimed at streamlining configuration processes and optimizing operational efficiency, along with Drift Detection for KPIs and entity-level Adaptive Thresholds for more accurate detection.

“AI is the cornerstone of Splunk’s strategy for driving enhancements with our industry-leading security and observability solutions,” said Hao Yang, VP, Head of AI at Splunk. “Our AI Assistants are designed to help users do their jobs easier and faster. We are currently adding generative AI tools to our product portfolio to accelerate detection, investigation and response workflows. Looking ahead, we aim to further refine the integration of advanced AI technologies, and continue to deliver more reliable and more secure IT outcomes for our customers.”

The AI Assistant in Observability Cloud introduces a generative AI-powered feature designed to streamline detection, exploration and investigation for engineering teams, with a natural language interface. By analyzing metrics, traces and logs, the AI Assistant helps enable software developers and Site Reliability Engineers (SREs) to quickly extract valuable insights, accelerating their day-to-day tasks, root cause analysis and issue resolution. This removes barriers and reduces expertise required to solve problems, making troubleshooting and data exploration journeys more accessible, and increasing operational efficiency.

Splunk also unveiled the AI Assistant in Security. Designed to expedite security analysts’ investigations and daily workflows by leveraging generative AI capabilities, this assistant streamlines the investigative process with analyst guidance and summarizes incident data. Offering AI-assisted security workflow guidance and generating security-specific Splunk Search Processing Language (SPL) accelerates investigations and response times, empowering analysts to fortify defenses against evolving threats with simplified processes and enhanced efficiency.

Splunk AI Assistant for SPL helps customers interact with Splunk’s data analytics platform using natural language, bridging the gap between human intuition and machine-driven analytics. This innovative tool allows analysts to execute complex analysis using natural language, understand existing Splunk Processing Language (SPL) queries with step-by-step explanations and search through product documentation to answer how-to questions.

By translating between natural language and SPL queries, Splunk AI Assistant improves analyst productivity and decision-making effectiveness. It further empowers organizations to optimize data analysis and incident response, positioning them to thrive in today’s data-driven world.

“We are integrating AI capabilities (assistants) into our solutions,” said Bongsu Cho, VP of AI & Big Data Division at LG Electronics. “More than ever before, it’s a competitive advantage to quickly and efficiently gain critical insights from your data which significantly enhances operational efficiency and proactive threat mitigation, ensuring robust digital resilience. A strategic AI approach across solutions ultimately drives innovation and strengthens business continuity in an increasingly complex digital landscape."

Splunk recently introduced a Configuration Assistant to its IT Service Intelligence (ITSI) solution, which leverages advanced AI and machine learning capabilities to manage and optimize configurations, proactively surfacing insights into the health of ITSI knowledge objects, such as KPIs, services, and entities. It provides a centralized console that helps IT administrators maintain accurate alerting by providing insights into outdated threshold models and optimizing those with a guided remediation, giving them a more accurate view of their IT health and reducing false positive alerts.

Splunk also added two new features as part of ITSI: Drift Detection for KPIs and entity-level Adaptive Thresholds. Drift Detection for KPIs helps users spot potential issues early by showing KPIs that exhibit gradual changes or sudden deviations from normal patterns. Additionally, Splunk’s entity-level Adaptive Thresholds allows users to create dynamic baselines at an entity level, and generate alerts if they behave abnormally. Both features are in public preview.

The AI Assistant in Observability Cloud is now in private preview.

The AI Assistant in Security will become available in private preview in August 2024.

Splunk AI Assistant for SPL is now generally available to Splunk Cloud Platform customers with AWS globally.

IT Service Intelligence (ITSI) and its Configuration Assistant feature are generally available, with Drift Detection for KPIs and entity-level Adaptive Thresholds in public preview.

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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 ...

Splunk Introduces Advanced AI Enhancements for Observability, Security and IT Service Intelligence

Splunk introduced a collection of AI tools across its product portfolio to enable organizations to speed up routine tasks and enhance their ability to get insights from data fast.

Splunk’s generative AI-powered Assistants can help every user become a security and observability expert.

Splunk has expanded its AI capabilities with new generative AI assistants in Observability Cloud and Security, providing customers with improved IT visibility and enhanced proactive threat mitigation capabilities. Additionally, the company has made its Splunk AI Assistant for SPL generally available, helping customers derive insights from Splunk using natural language. Moreover, Splunk unveiled new AI capabilities for IT Service Intelligence (ITSI), including Configuration Assistant, aimed at streamlining configuration processes and optimizing operational efficiency, along with Drift Detection for KPIs and entity-level Adaptive Thresholds for more accurate detection.

“AI is the cornerstone of Splunk’s strategy for driving enhancements with our industry-leading security and observability solutions,” said Hao Yang, VP, Head of AI at Splunk. “Our AI Assistants are designed to help users do their jobs easier and faster. We are currently adding generative AI tools to our product portfolio to accelerate detection, investigation and response workflows. Looking ahead, we aim to further refine the integration of advanced AI technologies, and continue to deliver more reliable and more secure IT outcomes for our customers.”

The AI Assistant in Observability Cloud introduces a generative AI-powered feature designed to streamline detection, exploration and investigation for engineering teams, with a natural language interface. By analyzing metrics, traces and logs, the AI Assistant helps enable software developers and Site Reliability Engineers (SREs) to quickly extract valuable insights, accelerating their day-to-day tasks, root cause analysis and issue resolution. This removes barriers and reduces expertise required to solve problems, making troubleshooting and data exploration journeys more accessible, and increasing operational efficiency.

Splunk also unveiled the AI Assistant in Security. Designed to expedite security analysts’ investigations and daily workflows by leveraging generative AI capabilities, this assistant streamlines the investigative process with analyst guidance and summarizes incident data. Offering AI-assisted security workflow guidance and generating security-specific Splunk Search Processing Language (SPL) accelerates investigations and response times, empowering analysts to fortify defenses against evolving threats with simplified processes and enhanced efficiency.

Splunk AI Assistant for SPL helps customers interact with Splunk’s data analytics platform using natural language, bridging the gap between human intuition and machine-driven analytics. This innovative tool allows analysts to execute complex analysis using natural language, understand existing Splunk Processing Language (SPL) queries with step-by-step explanations and search through product documentation to answer how-to questions.

By translating between natural language and SPL queries, Splunk AI Assistant improves analyst productivity and decision-making effectiveness. It further empowers organizations to optimize data analysis and incident response, positioning them to thrive in today’s data-driven world.

“We are integrating AI capabilities (assistants) into our solutions,” said Bongsu Cho, VP of AI & Big Data Division at LG Electronics. “More than ever before, it’s a competitive advantage to quickly and efficiently gain critical insights from your data which significantly enhances operational efficiency and proactive threat mitigation, ensuring robust digital resilience. A strategic AI approach across solutions ultimately drives innovation and strengthens business continuity in an increasingly complex digital landscape."

Splunk recently introduced a Configuration Assistant to its IT Service Intelligence (ITSI) solution, which leverages advanced AI and machine learning capabilities to manage and optimize configurations, proactively surfacing insights into the health of ITSI knowledge objects, such as KPIs, services, and entities. It provides a centralized console that helps IT administrators maintain accurate alerting by providing insights into outdated threshold models and optimizing those with a guided remediation, giving them a more accurate view of their IT health and reducing false positive alerts.

Splunk also added two new features as part of ITSI: Drift Detection for KPIs and entity-level Adaptive Thresholds. Drift Detection for KPIs helps users spot potential issues early by showing KPIs that exhibit gradual changes or sudden deviations from normal patterns. Additionally, Splunk’s entity-level Adaptive Thresholds allows users to create dynamic baselines at an entity level, and generate alerts if they behave abnormally. Both features are in public preview.

The AI Assistant in Observability Cloud is now in private preview.

The AI Assistant in Security will become available in private preview in August 2024.

Splunk AI Assistant for SPL is now generally available to Splunk Cloud Platform customers with AWS globally.

IT Service Intelligence (ITSI) and its Configuration Assistant feature are generally available, with Drift Detection for KPIs and entity-level Adaptive Thresholds in public 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 ...