Security is an essential part of any modern IT foundation, whether in smaller shops or at enterprise-scale. It used to be sufficient to implement rules-based software to defend against malicious actors, but those malicious actors are not standing still. Just as every aspect of IT has become more sophisticated, attackers have continued to innovate as well. Building more and more rules-based software to detect security events means you are always one step behind in an unsustainable fight.

According to the Association of Certified Fraud Examiners, the money lost by businesses to fraudsters amounts to over $3.5 trillion each year. The ACFE's 2016 Report to the Nations on Occupational Fraud and Abuse states that proactive data monitoring and analysis is among the most effective anti-fraud controls.

OpenTelemetry hit an exciting milestone this week. The tracing specification has achieved v1.0.0, and the general availability of tracing APIs and SDKs is imminent!

It’s no secret that Amazon Web Services is a powerhouse Cloud provider, and one of the market pioneers in Cloud operations. They do, after all, power some of the world’s biggest and most modern systems we all use and love today. It’s natural then that they attract a lot of users both big and small to deliver high quality and effective solutions. With growing user demand comes the need for new methods of visibility and intelligence.

As promised in part 1, I’m back with a short blog about our five key technology predictions for the UK public sector this year.

Here at Splunk we’re passionate about helping our customers get as much value from their data as possible. Recently Lila Fridley has written about how to select the best workflow for applying machine learning and Vinay Sridhar has provided an example of anomaly detection in SMLE.

The importance of the security of the Department of Defense’s (DoD’s) networks is no secret (well, of course a lot of it is secret!). This is evidenced by the Department’s IT/cybersecurity budget request that annually tops $40 billion dollars. Last year’s IT and Cyberspace Activities Budget Overview perhaps said it best.

Maybe you have used the previous blog post about generating smarter episodes in ITSI using graph analytics and want to know what else you can apply ML to. Maybe you’re still swamped in alerts even after using the awesome content pack for monitoring and alerting. Maybe your boss has told you to go read up on AIOps…. Whatever the reason for finding yourself here this blog is intended to help you identify the “unknown unknowns” in your alert storms.

Like champagne and party hats, Splunk and Microsoft just go together. Here at Splunk, one of our New Year’s resolutions is to continue to empower our customers with data — in this case, Microsoft data. From cloud, to security, to troubleshooting, we’re back with the latest round of new integrations designed to help you do more with Splunk and Microsoft.

In my previous Link Analysis blogs, "Visual Link Analysis with Splunk: Part 1 - Data Reduction" and "Visual Link Analysis with Splunk: Part 2 - The Visual Part," I used techniques that work well when we have a controlled data set. However, as we know, real data can be messy. When analyzing links in fraud data, the data can be very noisy. Let’s say we want to use IP addresses for link analysis in the Splunk platform. It is not unusual for two people to share an IP address.