According to IDG’s 2020 Cloud Computing Study, 92% of organizations have at least some sort of cloud footprint in regard to their IT environment. Therefore, traditional cloud security approaches must evolve to keep up with the dynamic infrastructure and challenges that cloud environments present – most notably, the inundation of data insights generated within the cloud.
Machine learning-powered cybersecurity
More than one-third of IT security managers and security analysts ignore threat alerts when the queue is full. 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.
However, a word of caution when using machine learning-based technology: the age-old garbage-in, garbage-out applies to security-focused machine learning engines. 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.
Strive for a security strategy that is rooted in data science
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. By having a security strategy firmly rooted in data science driven by human expertise, organizations will have complete visibility into the security and compliance risk of their cloud environments.
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 ( machine learning powered cybersecurity depends experience security