Artificial intelligence has arguably overstayed its welcome as a buzzword in the technology realm, leading to debates around the efficacy of the tool and definition of the term for the better part of two decades. But in the world of cybersecurity, businesses are just beginning to reap the benefits of advanced machine learning models that can actually keep up with ever-changing threats from cybercriminals with nothing but time on their hands to break algorithm-based defenses.
Historically, cybersecurity as a service has provided some protective measures to customers every few hours or once a day or once a week, as it was, and has since evolved all the way to 24-7-365 hands-on, human-led threat hunting and threat activity.
Machine Learning’s Role in Cybersecurity
Both periodic and constant threat detection services are valuable, but they’re not good enough to keep up with a threat actor who is able to infiltrate an environment in the time it takes a tier-1 security analyst to escalate a ticket to their superior in a Security Operations Center. But using machine learning to automate that process to operate at “the speed of data” can help. In practice, this looks like pushing machine-learning-based threat detection out to the edge of an environment, whether it’s a network sensor or endpoint agent. When detections are done at the edge, without a need for human intervention on the customer or practitioner side, everybody wins.
There’s hardly been a better time for critical advancements in the cybersecurity industry. Despite a recent downturn in ransomware attacks worldwide, largely due to the Russia/Ukraine conflict disrupting the most prolific threat groups in the world, companies in virtually all sectors are still at risk of having their data exfiltrated or held hostage by malicious actors who they’ll never see. Cyber attacks on organizations in the healthcare, education, financial and manufacturing industries have risen dramatically over the past two years despite high-profile breaches raising cybersecurity awareness worldwide.
In order to have a chance at an adversarial confrontation with a talented hacking group, businesses must be able to react to an attack faster than their attackers can adapt their approach to hacking, and in 2023, that means enlisting machine learning models to compile intelligence for them. Between threat bulletins published by the federal government, proprietary threat detection tools and information-sharing in the cybersecurity industry, there are plenty of knowledge sources tracking the latest threat models in use by malicious actors, even when they’re changing minute-by-minute.
When the data from those various streams of information is dissected by a machine learning model and combined with real-world context, cyber practitioners have a better understanding, and faster reflexes, for defending digital ecosystems against the bad guys.
The concept of operating at the speed of data sounds like a quote from Buzz Lightyear, but it’s not a Disney fantasy. Rather, ensuring that threat intelligence can be delivered in real time to vessels that can leverage it to detect or protect a certain asset is a goal that can and should be on the horizon for the security industry.
This article originally appeared in Forbes.
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