The deployment of 5G networks promises to exponentially increase the amount of IoT data available for modern enterprises. The number of global 5G subscriptions is expected to hit 1.5 billion in the next few years, according to GlobalData research, only escalating the number of IoT devices and sensors that are producing constant streams of data.
But with this data and connectivity boom comes a heightened threat of fraud and network breaches. Nearly half of enterprises sacrificed mobile security in 2019 and are twice as likely to suffer a security compromise as a result, according to Verizon’s 2020 Mobile Security Index report. As 5G connectivity and IoT devices expand into unexplored territories, opportunities for cybercriminals to become more creative and exploit new and unforeseen vulnerabilities will inevitably continue to grow.
In order to combat increasing IoT security risks, organizations must move away from traditional post-event batch processing for reconciliation that fails to detect threats in real time. In these post-event processing situations, threats go undetected and aren’t addressed until their negative effects are noticed when it’s too late.
An antiquated and reactive fraud detection approach — detecting fraudulent behavior after it occurs — risks compromising the network or organizational trust, and often causes millions of dollars in lost revenue and recovery efforts. With increasing security concerns, organizations must look to just-in-time anomaly detection for proactive threat prevention.
The era of the anomaly economy
In order to capitalize on streaming 5G IoT data, organizations must look to AI and machine learning to analyze these extensive data streams in real time to garner the insights required for faster and more informed decision making. By applying AI to streaming IoT data, organizations can vastly improve data visibility and agility as well as act faster on AI-based analysis, often impacting business in critical ways.
This is where anomaly detection using AI and machine learning to detect abnormalities in patterns that could indicate a previously hidden threat or opportunity comes into play. By detecting critical deviations earlier on, it becomes easier for organizations to correct and avoid failures, breaches, revenue losses or worse. Though businesses can utilize anomaly detection to leverage positive trends to enhance hyper personalization and increase user engagement, it will also prove a vital part of preventing fraud and security attacks.
Anomaly detection and security
Anomaly detection enables organizations to monitor network security and identify unusual patterns through IoT devices. Irregular behavior that could lead to a security breach or cyberattack is relayed in real time, providing a proactive response. Whether it’s within the financial services or telco industries, spotting suspicious behavior before an act occurs enables businesses to stop savvy cybercriminals and dangerous threat actors in their tracks, ultimately saving organizations money, time and reputation.
IIoT networks can be scanned and monitored while also capturing and analyzing data within the stream. Investing in edge computing that provides intelligence to the communication between IoT devices enables organizations to use anomaly detection as a critical piece of their security infrastructure. By analyzing what normal server traffic looks like, an intelligent monitoring platform can spot something out of the ordinary and notify security teams immediately.
Anomaly detection is changing the standards for security across industries. But it’s not only making waves in the security space. Organizations around the world will be impacted by its benefits in manufacturing, banking and more. For example, organizations will benefit from anomaly detection’s ability to help improve productivity and operational efficiencies, reduce unplanned downtime, and provide deeper customer and process insights to help shape future business decisions.
As 5G networks continue to replace older networks and intelligent connectivity becomes ubiquitous, organizations will need to focus on identifying both positive and negative patterns that anomaly detection brings to the forefront to become and remain competitive.
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