Artificial intelligence has arguably overstayed its welcome as a buzzword in the technology realm, leading to debates around the efficacy of the tool and the definition of the term for the better part of two decades. In the world of cybersecurity, however, businesses are just beginning to reap the benefits of advanced machine learning models that can actually keep up with ever-changing threats from cybercriminals who have 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, once a day or once a week, and it has since evolved all the way to 24/7/365 hands-on, human-led threat hunting and threat activity.
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. However, 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, partly 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 they'll never see. Cyberattacks 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.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/10/13/keeping-up-with-the-joneses-is-your-ai-fast-enough/?sh=7d1ee7a1d3f3
Historically, cybersecurity as a service has provided some protective measures to customers every few hours, once a day or once a week, and it has since evolved all the way to 24/7/365 hands-on, human-led threat hunting and threat activity.
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. However, 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, partly 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 they'll never see. Cyberattacks 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.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/10/13/keeping-up-with-the-joneses-is-your-ai-fast-enough/?sh=7d1ee7a1d3f3
