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.

As part of our effort to make the world safer, SecurityScorecard has been tracking threat actor groups conducting cyberattacks on behalf of nation states.

In working with customers across different enterprises and experiencing it myself, the challenges in managing vulnerabilities effectively are felt. Drawing from the insights of customers and my experiences, I’ve learned much about using Service Level Agreements (SLAs) in the vulnerability remediation process.

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?

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.

Telecommunications, Internet Service Providers, and Cloud Providers are some of the most critical sectors on the planet. They enable global connectivity, provide access to a wealth of information in real time, and transform business operations. As the foundation of modern communication, these industries have ushered in countless innovations and propelled society forward.

As businesses have evolved, so have cybercriminals and the means they use to try and penetrate their digital assets. Every day, new threats arise and unscrupulous organisations create means to attack physical networks, cloud services, and other key business functions and this is why having a robust cyber security risk management plan is critical.

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.