In the previous blog, we introduced what makes a bad alert and how being able to simply customise and fine-tune your detectors is critical to creating great alerts. The first category of detectors in Splunk Observability Cloud that we dived into was the out-of-the-box offering called AutoDetect. Customising and subscribing to these detectors is a great way to get up and running straight away with industry best-practice alerts and bring down MTTx.

Effectively managing both public and private Splunk Apps across multiple Splunk environments poses a considerable challenge, demanding significant time and effort with the potential for tedious and manual tasks. Recognizing this complexity, the Splunk Cloud Service has been progressively introducing additional features and capabilities to streamline and simplify these intricate administrative responsibilities.

We’ve all been guilty of it. Creating rules and filters to hide those alerts that, for the most part, are just noise. Only then to have notifications about a legitimate issue also get swept up by those same filters. There’s only so many times we can break concentration and disrupt productivity before getting fed up with false positives and ignoring everything completely.

There’s no doubt AI will radically reimagine the way we live, work and interact. It will empower new ways to solve business challenges and deliver customer value, but such a widespread impact requires a holistic approach. Building AI responsibly is one thing, but embedding trust into every aspect of our AI strategy is another entirely – and that’s what Splunk sets out to do.

Speedily minimizing the negative impact of an information security incident is a fundamental element of information security management. The risks — loss of credibility in the eyes of users and other stakeholders, loss of business revenue and critical data, potential regulatory penalties — can significantly jeopardize your organization’s mission and objectives.

The border between the management of telecommunications networks and the services that they support and the management of IT infrastructures and the applications that they support has always been a porous one. One might say that they are like two dialects of the same language rather than different languages. Nonetheless, these areas, whether characterised by technology or practice, are different and have, for the most part, been served by different vendors and products.

“Are your customers, partners and employees complaining about how slow your applications are running?" If you are not worried about the answer to this question, stop reading now.

Like physical architecture, the architecture running your business data — any and compute-intensive AI projects — is important. This data architecture governs a very important part of your business: how well users can translate raw information into real knowledge and actionable insights. Today, your data architecture is getting perhaps more attention than ever before. And that’s all thanks to usable AIs that now exist.

Here we go again! Multimodal AI is the “next big thing” in artificial intelligence technology. But what exactly does multimodal mean, and how does it differ from the AI models we’ve all grown familiar with? Let’s take a look.

The modern IT landscape consists of many distributed systems, which can pose a challenge if you are responsible for the end-to-end performance of these systems. As a platform engineer today, that is exactly what the job requires. You must juggle between dozens of tools to meet SLAs. This is why a modern solution is needed to bridge the gap between disjointed infrastructure and application stacks…and this is why the Splunk Observability platform was born.