Adopting a Purpose-Built Approach to Linux Security

Of course, this cannot be further from the truth: ensuring security and safety of industrial control systems has always been the most important job for OT experts. However, after decades of dealing with unique technologies and regulations and with a traditionally strong focus on human and process safety, securing IT assets was by far not their top priority. As industrial networks are becoming increasingly complex, geographically dispersed and interconnected, however, both impact and probability of numerous cybersecurity risks are growing, and the biggest challenge for CISOs nowadays is no longer how to persuade OT people to take notice, but how to evaluate a vast number of potential threats and to prioritize the actions needed to protect their networks.

Don’t miss this insightful webinar on Data Lifecycle Management (DLM) solutions and discover how this comprehensive approach can defend your organization against the threats of ransomware

Machine Learning in cybersecurity webinar presented by Sanja Nedic. THIS WEBINAR COVERED. Core concepts behind machine learning and main types of problems it can solve. Examples of real-world uses of machine learning in security to augment or replace rule-based or heuristic solutions to problems such as spam detection, intrusion detection, malware analysis, vulnerability prioritization, etc. Challenges of building reliable machine learning systems in security space.

With supply chain transparency and digital transformation being among the top organizational priorities, cyber-related issues have become one of the top risks to businesses. Most companies don't consider their supply chain vulnerability when thinking about cybersecurity. Yet, supply chain risks could include multiple functions across the flow of information, products and services – needing the attention of all entities involved. Supply chain security is every company's responsibility.