Security analytics is an approach to digital security that analyzes data to detect anomalies, unusual user behavior, and other threats. It aggregates data from across the entire ecosystem and turns that data into actionable insights — so that IT can quickly act to minimize risks. Advanced features like artificial intelligence (AI) and machine learning (ML) further help by automating the detection and remediation process.
A security analytics solution should be able to monitor performance as well as analyze data for potential threats. The three main performance areas a security solution should be able to report on include network, applications, and device performance.
If performance is poor in any of these areas, there is a greater likelihood that malware will slip past threat detection solutions and work undetected in the infrastructure. By using security analytics equipped with AI and ML, along with security policies and best practices, organizations can make big strides toward reducing risk.
What are the business and IT needs for security analytics?
Cyberattacks and breaches continue to rise, which is why security is a top business concern. Whether through malicious activity, insider threats, or unintentional leaks, organizations suffer as a result of lost data. Negative repercussions can include loss of revenue or brand reputation, expensive lawsuits, massive governance and compliance fines, and disruptions to operations.
Breaches can wreak havoc for IT teams as well. Remediation after a breach is time-consuming, uses valuable personnel hours, and eats into budget intended for other purposes.
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