An enterprise can strengthen its defenses

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How can enterprises protect their networks, which sometimes have dozens or hundreds of printers of different models connected to them? It is important to understand that the attack surface expands in direct proportion to the number of network devices: the more of them, the higher the risks. One of the options for protection is self-healing methods. We are talking about artificial intelligence-powered intrusion detection systems that autonomously track and suppress threats before they spread across the network. But NCC Group experts are skeptical about such methods. According to them, in most cases, an attacker exploits a vulnerability that developers have overlooked, but how can autonomous detection systems know about them if the developers themselves do not know about them? “An attacker will develop an exploit for a printer they control so that it can bypass any binary vulnerability protection methods or any detection system that the printer may have,” the experts explained.

with AI that detects abnormal network behavior. For example, it can detect when a printer fails to communicate with the network, prompting the IT department to respond, but such software does not guarantee 100% protection. “Anomaly detection systems are probabilistic, and sophisticated attacks can still bypass them,” security experts warn.

Vijay Kurkal, chief operating officer at chile mobile database Systems, an AIOps and automation solutions company, says the shortcomings of probabilistic modeling can be addressed through data enrichment and automated diagnostics. “Data analytics is enhanced when it’s backed by contextual business information, such as data collected during sales days, to determine metrics like traffic volume over time,” he said. “Interdependencies in IT systems help you see cause-and-effect relationships rather than relying on patterns or raw data. Data enrichment is complemented by automated diagnostics that help separate false alarms from those that require urgent attention.”