Despite, or perhaps because of, the abundance of information and commentary regarding artificial intelligence (AI), specifically as it applies to cybersecurity, it can be difficult to understand how AI \u2013 or more specifically, machine learning (ML) \u2013 is actually augmenting endpoint protection for individuals and organizations.<\/p>\n
Fundamentally, the difference between traditional and AI-based cybersecurity systems is a matter of \u201cteaching\u201d versus \u201clearning.\u201d Older antivirus software has been programmed to recognize specific security threats \u2013 phishing emails or ransomware, for instance \u2013 based on heuristics and specific digital signatures. The software has been instructed on exactly what to look for as it scans through files. Of course, this means it\u2019s powerless against new threats that it doesn\u2019t recognize, which is why the system\u2019s maintainers are constantly adding new instructions to reflect the latest cyber threats. You know those notifications you constantly get reminding you to update your antivirus software? That means programmers have updated the system to protect a newly discovered vulnerability (which is why it\u2019s important to keep your antivirus up to date).<\/p>\n
Simple enough. At least it was for most of the internet\u2019s history. The problem with the traditional approach is that in recent years cyber threats have become too numerous and too sophisticated for legacy antivirus to keep up. For example, in 2017\u00a0experts discovered<\/a>\u00a0over 7.4 million new malware specimens, leading some to call it the \u201cNightmare Year<\/a>\u201d for cybersecurity. To put that figure in perspective, it represents a 5,600% increase over the past decade.<\/p>\n Thankfully, recent advancements in machine learning \u2013 the building of algorithms that use statistical techniques to improve their own code \u2013 are enabling a more effective approach for endpoint security. Rather than trying to \u201cteach\u201d software all of the individual threats to look for, engineers can instead feed machine learning algorithms millions and millions of examples of cyber threats, allowing the systems to \u201clearn\u201d for themselves how to distinguish friend from foe. Thus, when hackers produce new variants of viruses and malware to avoid the signature-detection of traditional antivirus, machine-learning-based endpoint security, understanding the patterns and anomalies that characterize malware, is rarely fooled. One recent study found that an AI-powered system identified zero-day threats correctly 98.88% of the time, compared to the 71.16% success rate of traditional AVs.<\/p>\n So, does this mean machine learning is the silver bullet of endpoint protection? Not quite. The technology has limitations, most notably that it takes enormous amounts of data and computing power to make it work properly. But machine learning is so effective that even hackers are using the technology, training their programs to evade even the most sophisticated defenses. Increasingly, cybersecurity is becoming a matter of AI versus AI.<\/p>\n As HBR recently put it, \u201cAI is the future of cybersecurity, for better or for worse.<\/a>\u201d Stay tuned for more on AI in endpoint protection in the coming weeks.<\/p>\n Learn more about how Ziften is expanding its Zenith endpoint protection platform with artificial intelligence:\u00a0https:\/\/ziften.com\/zenith\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Despite, or perhaps because of, the abundance of information and commentary regarding artificial intelligence (AI), specifically as it applies to cybersecurity, it can be difficult to understand how AI \u2013 or more specifically, machine learning (ML) \u2013 is actually augmenting endpoint protection for individuals and organizations. Fundamentally, the difference between traditional and AI-based cybersecurity systems… Continue reading The Growing Role of Artificial Intelligence in Cybersecurity<\/span><\/a><\/p>\n","protected":false},"author":9,"featured_media":1554,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[20],"tags":[],"class_list":["post-1553","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-secure-workplace","entry"],"_links":{"self":[{"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/posts\/1553","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/comments?post=1553"}],"version-history":[{"count":0,"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/posts\/1553\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/media\/1554"}],"wp:attachment":[{"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/media?parent=1553"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/categories?post=1553"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.insentragroup.com\/gb\/wp-json\/wp\/v2\/tags?post=1553"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}