In Splunk Cloud Platform 9.3.2411, we’re excited to announce support for publish dashboards, the button input, as well as a number of small enhancements that will level up your dashboarding experience.

In a world where artificial intelligence (AI) is leaping forward — growing at a CAGR of almost 36% from 2024 to 2030 — questions about governance and ethics with the use of AI are surfacing. As humans continue to develop AI systems, it is crucial to establish proper guidelines to ensure powerful technologies like generative AI and adaptive AI are used in a responsible manner.

As businesses scale across hybrid and multi-cloud environments and integrate AI-powered technologies, complexity grows — and with it, the risk of performance degradation and cost of downtime. To avoid facing customer-impacting IT issues, organizations need better ways to correlate data across environments, detect anomalies before they escalate, and resolve incidents more efficiently. That’s where Splunk and Cisco come in.

Kubernetes is the standard for orchestrating containerized microservices — but it can present some monitoring challenges. Luckily, we’ve already covered why monitoring Kubernetes is a must-do, the basics of how to do it, and the options you have for collecting monitoring data from a K8s environment.

This year has been transformative for technology, reshaping the business landscape with groundbreaking advancements and unprecedented challenges. Generative AI continues to unlock new possibilities, while cybersecurity threats have escalated to new heights. Across APAC — a fast-emerging global innovation hub — businesses have grappled with the twin forces of regulatory evolution and technological breakthroughs.

Data drives the modern business world, and organizations capable of leveraging it effectively maintain a significant edge over their competition. Business Intelligence (BI) has emerged as a critical tool, enabling companies to turn raw data into actionable insights. But what exactly is BI? This blog explores everything you need to know about business intelligence, from its components to use cases, to implementation strategies.

Splunk AppDynamics, now part of the Splunk Observability portfolio, provides critical observability for traditional 3-tier/n-tier applications and helps IT Operations teams quickly discover root causes of issues before end-users even notice. AppDynamics complements Splunk Observability Cloud, which is optimized for observing cloud-native applications by DevOps and engineering teams.

Large language models (LLMs) are AI models with billions of parameters, trained on vast amounts of data. These models are typically flexible and generalized. The volume and distribution of training data determines what kind of knowledge a large language model can demonstrate. By training these large models on a variety of information from all knowledge domains, these models can perform sufficiently well on all tasks.

To confirm cyberattack occurrences and build or enhance cyber-defense strategies, threat intelligence teams use a lot of information, including Indicators of Compromise (IoCs). These IoCs are actually forensic data that are critical in: The relevance of IoCs cannot be downplayed, but they're not all that’s needed in building an effective cybersecurity strategy. In this article, we’ll explore indicators of compromise, their types, and their relevance to threat intelligence teams.

When you have a piece of data tucked into your logs or span tags, how do you dig for that bounty of insight today? Commonly this sort of data will be numeric, like a purchase total or number of units. Wouldn’t it be nice to easily turn that data into a metric timeseries? The Sum Connector in OpenTelemetry does just that, allowing you to create sums from attributes attached to logs, spans, span events, and even data points!