Key Advancements in Prediction Analytics

The pandemic has intensified the major trend facing cybersecurity managers: the need to do more with less. Resource-strapped CISOs need new technologies and new approaches to do their jobs, which are changing rapidly. The key cybersec megatrends – all of which make a CISO’s job harder than ever – are: - Cyber-physical systems - The move to cloud - The skills/knowledge gap In addition to the operational challenges of keeping organizations cybersecure, there also is a glaring and under-reported gap in risk management and governance. Many areas of business management need help from CISOs and they aren’t getting it. This includes Compliance/audit, business planning, even marketing. Machine Learning and Artificial Intelligence are the way out of all these dilemmas. Several notable market-leading software developers have attempted ML/AI for cybersecurity, but when examined further and implemented into production ecosystems, the ML is focused on limited vectors. This is something any advanced security ops analyst will tell you is of limited use if the actual AI activity is restricted in what it’s capable of delivering with any extensive assurance. Specifically, what hadn’t been solved to date is the numerous false-positives generated by both ML/AI. This is the same challenge faced by security ops supervisors who have invested heavily in every technology from security incident event monitors (SIEMs), network access control (NAC), network access prevention (NAP), and various monitoring and alerting platforms. Even more important are the elusive false negatives, which are the goal of advanced persistent threats (ATPs) and the goal that advanced and nation-state attacks strive toward. In this webinar, InsightCyber CEO Francis Cianfrocca and Chief Intelligence Officer Joan Ross describe how today's market challenges led them to consider cybersecurity from a completely new perspective: through the lens of ML/AI.