{"id":26635,"date":"2021-07-09T06:00:00","date_gmt":"2021-07-09T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=26635"},"modified":"2021-07-10T09:56:31","modified_gmt":"2021-07-10T16:56:31","slug":"how-predictive-ai-will-change-cybersecurity-in-2021","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/07\/09\/how-predictive-ai-will-change-cybersecurity-in-2021\/","title":{"rendered":"How Predictive AI will Change Cybersecurity in 2021"},"content":{"rendered":"\n<p>AI-enhanced cybersecurity is a must in 2021 and beyond. Clearly, the industry agrees \u2014&nbsp;you\u2019ll find an endless list of AI security platforms in the marketplace. What do vendors really mean when they use the term \u201cartificial intelligence?\u201d AI can be a fluid term, and sometimes mean different things to different people, and although marketing teams at cyber companies are using this ambiguity to their advantage, too often when it comes to the actual implementation and use of these platforms, the technology and promise falls short of AI in it\u2019s true scientific sense.&nbsp;<\/p>\n\n\n\n<p>But this isn\u2019t always the case. Some artificial intelligence is and will be groundbreaking for the cybersecurity industry. For example, predictive, \u201cThird-Wave AI,\u201d which is a term originally <a href=\"https:\/\/www.darpa.mil\/about-us\/darpa-perspective-on-ai\" target=\"_blank\" rel=\"noreferrer noopener\">coined<\/a> by DARPA to mean contextual and self-adaptable without the need for human training and tuning, can empower organizations to shut down threats before they happen, free from the restrictions and encumbrances of rules-based platforms like SIEM and other legacy AI-enhanced options.&nbsp;<\/p>\n\n\n\n<p>Before you invest in a cybersecurity platform upgrade, carefully consider your options. Second-wave AI solutions may work in the short term, but modern cyber criminals have devised <a href=\"https:\/\/securityboulevard.com\/2020\/01\/yesterdays-siem-solutions-cant-combat-todays-cyberthreats\/\" target=\"_blank\" rel=\"noreferrer noopener\">countless ways<\/a> to break these platforms and programs. To fend off data breaches, malware, ransom attacks and other cyber crimes, SOCs will need more robust, third-wave AI solutions.&nbsp;<\/p>\n\n\n\n<p>What is Third Wave AI?<strong><\/strong><\/p>\n\n\n\n<p>Predictive AI has been a part of cybersecurity for several years now, to varying degrees. The biggest distinction between legacy solutions and modern AI is that third wave, predictive AI detects and surfaces threats in real time.&nbsp;<\/p>\n\n\n\n<p>The U.S. Defense Advanced Research Projects Agency (DARPA) outlines <a href=\"https:\/\/www.darpa.mil\/about-us\/darpa-perspective-on-ai\" target=\"_blank\" rel=\"noreferrer noopener\">three eras of AI<\/a>:<\/p>\n\n\n\n<ul><li>First-wave, rules-based AI enabled \u201creasoning over narrowly defined problems\u201d with a reduced level of certainty, like early computer chess matches or tax prep software.<\/li><li>Second-wave, or machine-learning AI, is based on \u201ctraining statistical models on big data,\u201d with minimal capacity for reasoning.<\/li><li>Third-wave, or unsupervised-learning AI, is context-aware. Machines with third-wave AI \u201cadapt to changing situations.\u201d<\/li><\/ul>\n\n\n\n<p>Predictive AI is a type of machine learning that automatically collects, analyzes and tests data. As it relates to cybersecurity, this technology is often seen in applications like anomaly detection platforms, threat detection and cybercrime prevention.&nbsp;<\/p>\n\n\n\n<p>Predictive AI is patterned on the human brain, but powered by the immense power and speed made possible only through computing processes. Today\u2019s strongest systems are powered by quantum computing.&nbsp;<\/p>\n\n\n\n<p><strong>What\u2019s Wrong with Second Wave AI?<\/strong><strong><\/strong><\/p>\n\n\n\n<p>Until fairly recently, enterprises and medium-size organizations tended to work with traditional cybersecurity platforms based on first and second wave AI. One particularly popular choice has been SIEM (Security Information and Event Management) systems, which rely on a set of rules that \u201ctrain\u201d AI to detect network anomalies based on expected behavior.<\/p>\n\n\n\n<p>SIEM looks promising on paper, but as many organizations soon become aware, the approach is fundamentally flawed. One overarching issue are the ongoing costs created by SIEM. Basic log storage, incremental analytics and maintenance are all quite costly (and unavoidable).&nbsp;<\/p>\n\n\n\n<p>Security analyst talent is often wasted by SIEM platform functions, as well, due to an overabundance of false positives created in response to context limitations. There are only so many rules the human team can create, and since modern networks rely on constantly evolving baseline behavior, it would be impossible to keep up with all the necessary rules, anyway.&nbsp;<\/p>\n\n\n\n<p><strong>How Predictive AI Bolsters Network Security<\/strong><strong><\/strong><\/p>\n\n\n\n<p>Predictive AI can power modern, responsive cybersecurity platforms, outperforming previous-generation solutions in several key areas.&nbsp;<\/p>\n\n\n\n<p><strong>Data Overload<\/strong><strong><\/strong><\/p>\n\n\n\n<p>Because third wave AI-enabled security monitoring detects and surfaces threats in real time, before they can compromise your network, there\u2019s no need to accumulate and store massive amounts of data. Best-in-class AI can identify patterns and develop a human-like understanding of what normal traffic looks like, even within constantly changing conditions.&nbsp;<\/p>\n\n\n\n<p><strong>Approach to Expected Baseline Network Activity&nbsp;<\/strong><\/p>\n\n\n\n<p>Free from human tuning, self-supervised (third wave) AI learns over time how to identify and fix issues that traditional solutions can\u2019t solve. When there is a deviation from expected baseline, behavior predictive AI quickly finds it and alerts security.&nbsp;<\/p>\n\n\n\n<p>Rules-based SIEM platforms operate on a similar principle \u2014 detecting anomalous behavior by comparing activity to expected behavior. In the real world, any SOC will likely attest that \u201cexpected\u201d behavior can change on a dime.&nbsp;<\/p>\n\n\n\n<p>For example, when the world\u2019s workforce abruptly shifted to work-from-home models en masse, any notion of \u201cexpected\u201d or \u201cnormal\u201d went right out the window. Millions of new, remote connections, all at once, were certainly unexpected by most security platforms, but these connections weren\u2019t really abnormal.&nbsp;<\/p>\n\n\n\n<p>Associated behaviors were not actually anomalous. Still, security analysts working for organizations relying on SIEM faced a growing mountain of false positives they had to sort through. In the meantime, cyber criminals who had been waiting for a moment like this for years, <a href=\"https:\/\/www.cnbc.com\/2020\/07\/29\/cybercrime-ramps-up-amid-coronavirus-chaos-costing-companies-billions.html\">swooped right in<\/a>. Not only did bad actors seek out network vulnerabilities opened up by these SIEM and similar issues, but they wasted no time unleashing phishing schemes while they knew security teams would be busy addressing immediate network issues.&nbsp;<\/p>\n\n\n\n<p>On the flip side, organizations that had invested in third wave AI solutions experienced far fewer issues. These systems create an <strong><em>evolving<\/em><\/strong> baseline of normal network behavior. As a \u201cnew normal\u201d took hold for these organizations, their third wave AI solutions were able to adjust on the fly.&nbsp;<\/p>\n\n\n\n<p><strong>Zero Day Attack Capabilities<\/strong><strong><\/strong><\/p>\n\n\n\n<p>Zero day attacks like the Solarwinds attack on U.S. federal agencies, which <a href=\"https:\/\/www.nytimes.com\/2020\/12\/13\/us\/politics\/russian-hackers-us-government-treasury-commerce.html\" target=\"_blank\" rel=\"noreferrer noopener\">made headlines<\/a> at the end of 2020, can be devastating to an organization. Within minutes, an entire network can become compromised, after hackers have been inside the network for months or years, completely undetected.&nbsp;<\/p>\n\n\n\n<p>Third wave AI helps to stave off zero day attacks the instant bad actors make their move. Real time threat detection means just that. In a rules-based system, there\u2019s a much higher risk of losing precious response time. By the time a security analyst figures out what\u2019s happening, the damage may well be done.&nbsp;<\/p>\n\n\n\n<p><strong>Looking Ahead: Predictive AI in 2021 &#8230; and Beyond<\/strong><\/p>\n\n\n\n<p>To keep ahead of the current crop (and tomorrow\u2019s crop) of tech-savvy cybercriminals, organizations will need to invest in cybersecurity solutions that are streamlined, powerful and powered by predictive AI.&nbsp;<\/p>\n\n\n\n<p>For too long, modern SOCs have dumped millions of dollars into solutions that are failing at increasing rates. Enterprises and organizations of every size are losing revenue, constantly dealing with the financial and operational impacts of data loss, while failing to address the fundamental issues with their security solutions, all the same.&nbsp;<\/p>\n\n\n\n<p>Unsupervised, predictive AI is the best path forward for modern SOCs. These systems offer a centralized solution that addresses the functional requirements of anomaly detection platforms, SIEM and UBA with the added benefits of predictive, self-learning AI. Third wave AI delivers true, real-time protection for networks assets on-prem, in the cloud, and across connected devices.&nbsp;<\/p>\n\n\n\n<p><strong>About the Author<\/strong><\/p>\n\n\n\n<div class=\"wp-block-image is-style-default\"><figure class=\"alignleft size-large\"><img decoding=\"async\" loading=\"lazy\" width=\"125\" height=\"125\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2021\/07\/Igor-Mezic.jpg\" alt=\"\" class=\"wp-image-26636\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2021\/07\/Igor-Mezic.jpg 125w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2021\/07\/Igor-Mezic-110x110.jpg 110w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2021\/07\/Igor-Mezic-50x50.jpg 50w\" sizes=\"(max-width: 125px) 100vw, 125px\" \/><\/figure><\/div>\n\n\n\n<p>Dr. Igor Mezic, CTO and Chief Scientist for <a href=\"https:\/\/mixmode.ai\/\" target=\"_blank\" rel=\"noreferrer noopener\">MixMode<\/a> AI.<\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a rel=\"noreferrer noopener\" href=\"http:\/\/insidebigdata.com\/newsletter\/\" target=\"_blank\">newsletter<\/a>.<\/em><\/p>\n\n\n\n<p><em>Join us on Twitter:&nbsp;@InsideBigData1 \u2013 <a href=\"https:\/\/twitter.com\/InsideBigData1\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this contributed article, Dr. Igor Mezic, CTO of MixMode AI explains how you can identify and stop zero-day attacks like the SolarWinds attack with the invention of this Unsupervised, Predictive AI, which uses a baseline to understand what should be occurring on the network at all times in order to catch anomalies as soon as they appear. <\/p>\n","protected":false},"author":10513,"featured_media":22592,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,87,180,56,97,1],"tags":[437,324,419,236,96],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - 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