Many businesses are now talking about artificial intelligence (AI), and specifically machine learning, as a way to solve data problems more effectively. In theory, this sounds easy. What could be better than using AI to get a computer to learn how to solve a problem over time, without manual intervention? The reality is very different, however.

First, what is ransomware? Ransomware is a form of malware that encrypts files on the victim’s machine and demands a ransom for restoring access to the files. The instructions on how to pay the ransom are put in a ransom note.

Cloud API logs are a significant blind spot for many organizations and often factor into large-scale, publicly announced data breaches. They pose several challenges to security teams: For all of these reasons, cloud API logs are resistant to conventional threat detection and hunting techniques.

According to a seminal Clark School study, a hacker attacks a computer with internet access every 39 seconds. What’s more, almost a third of all Americans have been harmed by a hacker at one point or another, and more than two-thirds of companies have been victims of web-based attacks. A 2020 IBM study showed that the total cost of data breaches worldwide amounted to $3.9 million, which just may sound the death knell for many businesses affected by breaches.

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.

What are the groundbreaking Machine Learning and AI projects that will likely change the existing AI/ML landscape and bring their own rules of the game?

While working with customers over the years, I've noticed a pattern with questions they have around operationalizing machine learning: “How can I use Machine Learning (ML) for threat detection with my data?”, “What are the best practices around model re-training and updates?”, and “Am I going to need to hire a data scientist to support this workflow in my security operations center (SOC)?” Well, we are excited to announce that the SplunkWorks team launched a new add-