Anomaly detection plays an increasingly important role in data and storage management, as admins seek to improve security of systems.
In response to these developments, more vendors incorporate storage anomaly detection capabilities into their products, often including them as part of a larger management platform.
Anomaly detection refers to the process of identifying items, events, patterns, data points, observations or changes that differ significantly from the expected behavior.
Storage anomaly detection can help organizations identify and react to unusual behavior much faster than with traditional monitoring alone.
Anomalies often indicate some type of problem, such as malfunctioning equipment, faulty software or compromised data.
An anomaly could represent a business opportunity rather than a potential problem.
An anomaly that stands out in some significant way from the expected pattern or behavior, such as a brief spike in I/O activity on a disk array with no discernable cause.
An anomaly whose meaning is derived from multiple data points that collectively indicate an unusual pattern.
By employing real-time anomaly detection, IT teams can strengthen their security posture and minimize operational and business risks.
Anomaly detection can play a key role in reducing the disruptive effects of storage-related hardware and software issues.
Storage anomaly detection makes it possible for IT teams to identify unusual events and circumstances that represent a departure from normal storage and data operations.
With anomaly detection, IT can discover these changes before full disk failure occurs.
Anomaly detection can help evaluate system logs to better understand service disruptions.
Storage and data security go hand in hand with network security, particularly as it applies to NAS or a SAN. For example, a team might deploy an intrusion detection system that monitors incoming and outgoing network traffic in real time to identify anomalies that represent potential security risks.
Vendors have added anomaly detection features to their platforms as the technology continues to grow more important to storage and data management.
Because of the size and diversity of the data, most storage anomaly detection approaches incorporate machine learning algorithms that can handle various types and amounts of data.
CloudIQ uses ML and predictive analytics to identify anomalies in its monitored systems.
The product's anomaly detection features use ML to uncover performance changes in processing patterns and behaviors in areas such as latency, utilization and IOPS. Microsoft Windows Server 2019 and 2022.
System Insights includes disk anomaly detection, which identifies when the server's disks are behaving unusually.
Other vendors that include anomaly detection include AWS, with services such as SageMaker, Kinesis and Quick Start, and Nutanix Prism, with its ML predictive monitoring features.
This Cyber News was published on www.techtarget.com. Publication date: Wed, 06 Dec 2023 14:43:06 +0000