Nopsec
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.
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With each new year’s planning, organizations must deal with both new and longstanding trends that have an impact on their cybersecurity profiles. This process is made even more difficult by an increasingly sophisticated threat landscape and a chronic cyber-skills shortage that impacts all organizations.
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BitSight
In today’s interconnected technology ecosystem, companies increasingly rely on third-party vendors to meet their operational needs. However, the current state of vendor risk management (VRM) is bleak. More than half of all information security breaches are caused by third-party vendors, and according to Deloitte, 83% of today’s business leaders lack confidence in third-party VRM processes. Understanding and managing cyber risk posed by vendors, suppliers, and third parties has proven to be a difficult task. The right mix of people, processes, and technology result in the most effective and comprehensive program.
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Carbon Black
Enterprise security teams struggle to get their hands on the endpoint data they need to investigate and proactively hunt for abnormal behavior. Security and IT professionals currently lack the ability to see beyond suspicious activity and need a way to dive deeper into the data to make their own judgments. Start leveraging unfiltered data to make the most of your threat hunt. CB ThreatHunter - on our Predictive Security Cloud - allows for just that. Join our webinar to learn about: Our best-of-breed threat hunting. How to utilize unfiltered data gathered from all protected endpoints.
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