Traditional endpoint and infrastructure security approaches are tackling changes to OS, application and communication by monitoring these through dedicated solutions installed as agents onto the actual system. Often these solutions search for specific violations and act upon predefined white listed applications / processes or blacklisted identified threats.
Due to their architecture, virtualization platforms and cloud infrastructures have completely different access to security-relevant information. When intelligently executed, real-time data and current threats can be correlated. But much more is possible from the central and unique perspective these virtualized architectures allow. Observing the behavior of components in the software-defined network, comparing this with their expected behavior and identifying unexpected deviations allows the detection and treatment of previously unknown threats up to zero-day attacks.
Manufacturers such as Citrix and VMware are working at full speed to provide high-performance, integrated security infrastructures as part of their platform. These may be delivered, for example, not only as a component of hypervisor, but also as a component of a hybrid security architecture between cloud, virtualization and bare metal.
By going beyond traditional “known good” and “known bad” approaches through black-listing and whitelisting, such solutions provide an intelligent approach for infrastructure security. The approach of capturing the actual runtime behavior of existing software systems to learn expected and appropriate behavior while applying algorithmic control and monitoring in later phases has the potential to be able to cover a vast number of systems, including homegrown and enterprise-critical systems. Earlier this year, KuppingerCole published an Executive View research document on VMware AppDefense as a representative of this innovative security approach. And just this week VMware announced the availability of AppDefense in EMEA as well as extended capabilities to protect containerized workloads.
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