Although the concepts of artificial intelligence and machine learning are not new, they have recently garnered mainstream attention. As is normal in the technology space, the initial hype makes it sound like a panacea and cybersecurity vendors are scrambling to hitch their marketing messages to it.
Cloud-first initiatives are commonplace, driving more data and application migrations to the cloud. Increased agility, resource elasticity, and cost savings are key goals; however, this shift places new stress on IT to secure and support those applications and the associated data. Private, public, and hybrid cloud deployments are dispersing data, which leads to limited visibility and increased concerns over security. New approaches must be evaluated to manage and secure these cloud architectures effectively.
The Actionable Data You Need For Cybersecurity Incident Response Must Come From SIEM + Network Traffic Analysis
As a security professional, you're likely using SIEM to aggregate and correlate syslog data from your security tools to identify and prioritize events. The challenge we consistently hear is that while SIEM does a great job at identifying problems, it lacks the actionable data needed to know what to do next. This session will discuss how network traffic analysis (NTA) complements your SIEM to provide multi-dimensional data for efficient incident response.