Cisco Brings New Capabilities to Tetration

Businesses are using connected technologies at a large and growing rate.

They’re moving workloads to the cloud. Actually, not just the cloud – multiple clouds. Some workloads might be a better match for Amazon Web Services, for example, and some for Microsoft Azure. And, in the multicloud world, businesses have the option to pick and choose among different cloud service providers.

Of course, the more connectivity and cloud solutions business employ, the more they put themselves at risk of cyberattack. That’s something they need to address.

Plus, container-based workloads, and microservices and virtualized architectures, create more moving pieces. That – and the fact that businesses are employing multicloud strategies – makes it difficult to know where everything is, how it’s performing, and whether it’s safe.

So, Cisco is addressing all that by introducing new security capabilities to its Tetration platform. New features include:

• Application segmentation using whitelist that enables zero-trust

• Baselining process behavior and identifying deviations

• Identification of software vulnerabilities and exposures, and 

• Monitoring processes running on servers in real-time

That helps organizations implement consistent workload protection policies. Yogesh Kaushik recently shared this attaches the security policy to the workload – a practice known as microsegmentation. That means that as the workload moves, the policy moves with it.

And Cisco Tetration enables organizations to fine-tune their policies over time based on what’s happening with their workloads and trends it helps uncover.

The system collects more than 100 attributes from thousands of workloads, infrastructure (including the network, load balancers, AWS), orchestration systems, and other systems of record in real-time, the company explains.

“This includes metadata about every process, every software package, and every flow/packet to name a few,” Cisco explains. “Based on these attributes, Tetration maps out all application components and dependencies in a zero-knowledge environment using unsupervised machine learning. Think of this as a fingerprint of your application, based on behavior such as what’s running on the workloads, who they talk to, how often, when, and in what pattern.”

Edited by Mandi Nowitz