AI understands when servers have drifted into the danger zone

“One of these things is not like the others,” the television show Sesame Street taught generations of children. Easy. Let’s move to the next level: “One or more of these things may or may not be like the others, and those variances may or may not represent systems vulnerabilities, failed patches, configuration errors, compliance nightmares, or imminent hardware crashes.” That’s a lot harder than distinguishing cookies from brownies.

Looking through gigabytes of log files and transactions records to spot patterns or anomalies is hard for humans: it’s slow, tedious, error-prone, and doesn’t scale. Fortunately, it’s easy for artificial intelligence (AI) software, such as the machine learning algorithms built into Oracle Management Cloud. What’s more, the machine learning algorithms can be used to direct manual or automated remediation efforts to improve security, compliance, and performance.

Consider how large-scale systems gradually drift away from their required (or desired) configuration, a key area of concern in the large enterprise. In his Monday, October 2 Oracle OpenWorld session on managing and securing systems at scale using AI, Prakash Ramamurthy, senior vice president of systems management at Oracle, talked about how drift happens. Imagine that you’ve applied a patch, but then later you spool up a virtual server that is running an old version of a critical service or contains an obsolete library with a known vulnerability. That server is out of compliance, Ramamurthy said. Drift.

Drift is bad, said Ramamurthy, and detecting and stopping drift is a core competency of Oracle Management Cloud. It starts with monitoring cloud and on-premises servers, services, applications, and logs, using machine learning to automatically understand normal behavior and identify anomalies. No training necessary here: A variety of machine learning algorithms teach themselves how to play the “one of these things is not like the others” game with your data, your systems, and your configuration, and also to classify the systems in ways that are operationally relevant. Even if those logs contain gigabytes of information on hundreds of thousands of transactions each second.

Learn more in my article for Forbes, “Catch The Drift With Machine Learning — Before The Drift Catches You.”