Security News > 2021 > April > Machine learning-powered cybersecurity depends on good data and experience

This is a common issue that is driving the high demand for machine learning-based analytics, as it helps security teams sift through massive amounts of data to prioritize risks and vulnerabilities and make more informed decisions.

If your data is bad, then your machine learning tools will be insufficient, making your security infrastructure vulnerable to attack and putting your organization at risk for a wide-spread security breach.

Machine learning-powered cybersecurity must also go beyond good data and incorporate extensive industry experience and defined rule sets to harness the power behind these security insights.

The most effective machine learning-based security solutions collect and effectively make use of high-quality telemetry to deliver risk visibility across the entire cloud infrastructure stack to include the application layer, containers-as-a-service, Kubernetes orchestration, container runtimes, host machines, and so on.

The continuous collection of this data will set your machine learning-based cloud security strategy apart.

Monitor the unknown - machine learning for anomaly detection: machine learning techniques excel at surfacing unknown risk within your environment.

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