Increasingly complex data centers have created an opportunity for smart technology, artificial intelligence (AI), and machine-learning technology to advance rapidly. As an IT professional, when you have Internet of Things (IoT) devices that are gathering data center environment metrics such as temperature (cooling, heating, and humidity), you realize there is and will continue to be a tetraton of data to collect, monitor, and manage. This is where machine learning can help.

Power consumption was a gateway for machine learning to enter the data center scene. It allowed algorithms to predict overheating, shut down devices, or change the settings for temperature points to better manage consumption.

What about the rest of the data? Black hats and many InfoSec security engineers have been using this type of data to better create security templates based on business needs. Behavior anomaly analysis, automated policy enforcement, and application insight are a security designer’s best friends. Security teams need to have a 10,000-ft view of a data center and its operations; this need spawned the development of machine-learning algorithms and software that have access to data.

Event correlation has been the key to success. However, when a breach or a security issue occurs, the event correlation does not mean you know when the incident occurred. We are able to see the reaction and know what this behavior looks like, but not the initial ground zero trigger.

This is where machine learning can analyze data in real-time and learn any shifting behaviors in networking, hardware, applications, operating systems, and more. This allows us to better defend these massive and complex data centers and grow with them more on a flexible model than we have today.

DATA CENTER INTEGRATION

The integration of learned behaviors is vital for the future of data centers. Big data is only going to get, well, bigger. This means large-scale malicious attacks will trend up with it. If we think about how monitoring metrics have been gathered for years, we can start to understand why we should invest in these stockpiles of data collections.

Keep in mind, machine learning is not just for the “good guys.” Anyone can potentially create an application that uses machine learning to attack businesses to find loopholes and cause disruptions.

Would you want an outsider having access to your IoT devices that can make changes to your infrastructure? Integration and acceptance of this type of technology is beneficial now, and will be even more essential in the future, with more learned patterns and behaviors.

Security models would have the flexibility to apply policies and templates to devices that would allow them to be reactive to possible security infiltrations. Tracking patterns from malicious software and unwanted access users allows for the creation of better reaction and proactive strategies.

When an application is interrupted, it has the potential of becoming a multi-million-dollar interruption in less than an hour’s time, depending on the business and application or service down. Security is a critical factor for uptime and for a business’s reputation.

Most data centers are planning to integrate machine learning into some portion of their infrastructure. Decisions are now being made and evaluated at a security level about if you want your data to be shared publicly with others to help fine tune machine learning. The more data, the more accurately algorithms can be created and tested.

There’s a constant give and take when it comes to security. Public and private sectors are trying to get a better handle on these copious amounts of data and the potential knowledge that can be attained from within it. Learning from the past will prevent degradation in the future.

CONCLUSION

Security is no longer an IT issue, it’s a business issue. Ideally, it should be woven in the very fabric of our business infrastructure and daily transactions. The highest positions to the lowest positions should have a clear and concise business security mission statement. This would help ensure that everyone is on board and willing to grow with a more security-oriented mindset.

Until that day comes, we need more proactive, intelligent ways to address security concerns in our data center environments. Adopting machine learning and AI resources will help aid in the ever-changing digital experiences that data centers provide.

We need flexibility as we globally expand operations and abilities. Machine learning is bridging the gap between human-understandable information and real-time understanding of packet consumption with automated responses. This is a great time to be involved with the technology that will be shaping our everyday lives at home and, most importantly, work.