Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

My journey to finding Kovrr had been packed with headaches and puzzles that many CISOs still face today. Within a few short years of being the CISO at Avid, a content-creation software provider, I managed to implement tighter security controls and develop a framework that enabled objective progress measurement. ‍ However, I constantly faced an impasse when attempting to communicate these achievements with the board.

One study found that 65% of SaaS applications in use are unsanctioned. And 59% of IT professionals find SaaS sprawl challenging to manage. In other words, shadow IT risks are growing—but that’s just the tip of the iceberg when it comes to hidden risks across today’s expanding attack surface. Missed software patches, outdated certificates, and stealth malware are some examples. Many security teams still struggle to keep their networks safe from ever-growing digital supply chains.

Data lineage and data provenance are related terms, but different. Lineage focuses on the origins and movements of data over time, while provenance focuses on the transformations and derivations of data from original sources. Provenance helps teams to follow the source of data and verify its authenticity, surfacing any potential risks or vulnerabilities. In other words, lineage is more about “where” data travels, and provenance is more about the “what” of data history.

The annual doctor wellness check always interests me. It’s generally the same routine every year: The doctor and I exchange pleasantries. She asks about any noticeable health changes while looking in my ears with that cool little penlight. If I’m lucky, she uses the mini-hammer to see how high my leg kicks after a gentle knee tap (I just love that for some reason). But it’s all a bit of a show, isn’t it?

In the cyber age, data has become nearly as valuable as oil. While this market shift offers many new learning and growth opportunities for professionals across industries, the immeasurable amount of data is often quite overwhelming to non-analysts, leaving them feeling more lost than when they began their inquiries. ‍ This situation often rings true for cybersecurity leaders tasked with protecting an organization's digital assets against attacks and increasingly malicious actors.

The annual doctor wellness check always interests me. It’s generally the same routine every year: The doctor and I exchange pleasantries. She asks about any noticeable health changes while looking in my ears with that cool little penlight. If I’m lucky, she uses the mini-hammer to see how high my leg kicks after a gentle knee tap (I just love that for some reason). But it’s all a bit of a show, isn’t it?

As organizations increasingly rely on external vendors and enterprise buying patterns continue to decentralize, the challenge of managing risk associated with third parties becomes critical. Unfortunately, even uncovering vendor relationships within an organization can be a struggle, with over 80% of workers admitting to using non-approved SaaS applications. This ‘Shadow IT’ is not only frustrating; it introduces tremendous risk.

AgentTesla is a Windows malware written in.NET, designed to steal sensitive information from the victim's system. It’s considered commodity malware given its accessibility and relatively low cost. Commodity malware poses a significant threat as it enables less sophisticated cybercriminals to conduct various types of cyberattacks without requiring extensive technical knowledge. AgentTesla has been a persistent and widespread threat since its emergence in 2014.

Browser extensions are a classic shadow IT concern. Assessing the reputation and security of a browser extension is crucial before installing it on a company computer, as extensions often have wide-ranging permissions that could be abused for data theft or other malicious activities. In an open environment style company, extensions generate significant shadow IT risk that needs to be managed and addressed.

Splunk Enterprise and Splunk Cloud Platform, along with the premium products that are built upon them, are open platforms, which allow third party products to query data within Splunk for further use case development. In this blog, we will cover using Amazon SageMaker as the ISV product using the data within Splunk to further develop a fraud detection use case to predict future risk scores.