{"id":7522,"date":"2025-04-08T14:30:00","date_gmt":"2025-04-08T19:30:00","guid":{"rendered":"https:\/\/www.dnsfilter.com\/blog\/artificial-intelligence-in-cybersecurity"},"modified":"2025-04-08T14:30:00","modified_gmt":"2025-04-08T19:30:00","slug":"artificial-intelligence-in-cybersecurity-dnsfilter","status":"publish","type":"post","link":"https:\/\/ddi.mohflo.net\/index.php\/2025\/04\/08\/artificial-intelligence-in-cybersecurity-dnsfilter\/","title":{"rendered":"Artificial Intelligence in Cybersecurity | DNSFilter"},"content":{"rendered":"<p>The term \u201cartificial intelligence (AI)\u201d was <a href=\"https:\/\/www.livescience.com\/49007-history-of-artificial-intelligence.html\" rel=\"noopener\" target=\"_blank\"><span>first coined in 1956<\/span><\/a>. While progress stalled for many years, we can thank IBM for sparking real interest in AI as viable technology: First in 1997 when the computer Deep Blue defeated a chess champion and again in 2011 when Watson won <em>Jeopardy!<\/em><\/p>\n<p><!--more--><\/p>\n<p>Since 2020, artificial intelligence has rapidly evolved from a futuristic concept into a foundational element of every organization\u2019s information technology and cybersecurity infrastructures. Today\u2019s AI systems can analyze data at scale, detect novel threats in real time, and adapt quickly to an ever-changing threat landscape\u2014capabilities that are increasingly critical as cyberattacks become more sophisticated.<\/p>\n<p>At the core of most AI-driven cybersecurity tools is machine learning (ML)\u2014a subset of AI that enables systems to learn from data and improve over time without being explicitly programmed. ML allows our technology at DNSFilter to detect patterns, flag anomalies, and identify malicious domains that static, rule-based systems would miss.<\/p>\n<p><span><span>By combining <\/span><a href=\"https:\/\/www.dnsfilter.com\/blog\/ai-vs-ml-whats-the-difference\">AI and machine learning<\/a><span>, DNSFilter is able to identify deceptive domains in real time, keeping users safe from threats that evolve faster than any human-curated list could track.<\/span><\/span><\/p>\n<h2>How Machine Learning Powers AI in Cybersecurity<\/h2>\n<p>Artificial intelligence is when we give a machine the ability to perform tasks that are traditionally done by humans. While that can cover a wide range of applications, the type of artificial intelligence we want to focus on when talking about cybersecurity is related to machine learning.<\/p>\n<p>Machine learning is the method by which an AI learns. An AI will use machine learning to identify patterns and make decisions, with varying degrees of human supervision.<\/p>\n<h2>How does artificial intelligence work?<\/h2>\n<p>While machine learning can work in a variety of different ways and the steps can be complicated, this is the general process:<\/p>\n<ul>\n<li aria-level=\"1\">The AI is programmed to perform a task or goal (e.g., detecting malicious websites)<\/li>\n<li aria-level=\"1\">The AI is given the data necessary to perform its task, the larger the data set the better (e.g., examples of malicious sites and benign sites)<\/li>\n<li aria-level=\"1\">The AI performs the task it was programmed to do based on the data it was trained with. Making prediction based on the patterns it has learned&nbsp;<\/li>\n<li aria-level=\"1\">The user(s) will judge the AI on how well it performed that task. The machine learns and adjusts its output based on the the evaluation it receives<\/li>\n<li aria-level=\"1\">Based on how well it performed the task, the AI comes away with new learnings, improving how it can detect new patterns and solve problems (like identifying never-before seen threats)<\/li>\n<\/ul>\n<p>A great example of this is an AI tasked to create a picture of a hamburger. It\u2019s given thousands of pictures of cheeseburgers so it understands what a hamburger generally looks like. It then creates a picture of a cheeseburger, repeatedly.<\/p>\n<p>Here\u2019s what it looks like when an AI learns how to make a hamburger:<\/p>\n<figure class=\"w-richtext-figure-type-image w-richtext-align-fullwidth\">\n<div><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/ddi.mohflo.net\/wp-content\/uploads\/2025\/04\/artificial-intelligence-in-cybersecurity-dnsfilter.png?w=640&#038;ssl=1\" alt=\"evolution of AI learning how to make a hamburger\"><\/div>\n<\/figure>\n<p>We\u2019re not joking, <em>none<\/em> of those were real hamburgers. They were all AI generated.<\/p>\n<p>Within an AI, there are essentially two brains: The brain that learns and does the work, and the brain that takes in the data and judges the work. These two brains speak to one another after a task is complete (or after a picture of a cheeseburger is generated).<\/p>\n<p>The AI will do whatever it can to make the task it performs closely match the examples it was given.<\/p>\n<p>The part of the AI meant to judge its own work might look at those pictures of hamburgers and recommend making the burgers look a little less messy if it thinks that will make them match the original data better.<\/p>\n<h2>The Role of AI in Cybersecurity<\/h2>\n<p><span><span>AI\u2019s integration into cybersecurity has transformed how organizations identify and mitigate threats. According to IBM\u2019s <\/span><a href=\"https:\/\/www.ibm.com\/reports\/data-breach\" rel=\"noopener\" target=\"_blank\">2024 Cost of a Data Breach Report<\/a><span>, businesses using AI-driven security tools reduced breach detection times by 63% compared to manual methods. Key applications of AI in cybersecurity include:<\/span><\/span><span><\/span><\/p>\n<div data-hs-responsive-table=\"true\">\n<table>\n<tbody readability=\"5\">\n<tr readability=\"10\">\n<td><strong><span>Real-Time Threat Detection: <\/span><\/strong><span>Machine learning algorithms analyze network traffic patterns to identify anomalies like unauthorized API calls or unusual data exfiltration volumes.<\/span><\/td>\n<td><strong><span>Automated Incident Response: <\/span><\/strong><span>AI resolves a majority of low-level alerts without human intervention, freeing analysts to focus on critical threats.<\/span><\/td>\n<td><strong>Enhanced Zero Trust Architecture (ZTA):<\/strong> AI evaluates user behavior and device health in real time to enforce dynamic access controls.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>DNSFilter\u2019s AI doesn\u2019t just detect malicious domains\u2014it blocks them in real time, often before they&#8217;re even reported elsewhere. Explore our <a href=\"https:\/\/www.dnsfilter.com\/features\/malicious-domain-protection\" rel=\"noopener\">malicious domain protection capabilities<\/a> to see how we keep users safe without slowing them down.<\/p>\n<h2>AI in Action<\/h2>\n<p>By embracing <a href=\"https:\/\/www.dnsfilter.com\/blog\/using-dns-to-prevent-ai-driven-cyberattacks\"><span>artificial intelligence in cybersecurity<\/span><\/a>, companies can improve their systems more rapidly. DNSFilter\u2019s machine learning algorithm allows us to continuously increase the number of malicious sites detected on a daily basis as opposed to relying on static, human-generated lists of malicious sites.<\/p>\n<p>There were approximately <a href=\"https:\/\/www.dnib.com\/articles\/the-domain-name-industry-brief-q1-2024\" rel=\"noopener\" target=\"_blank\"><span>362.4 million new domain name registrations <\/span><\/a>across all top-level domains (TLDs) in the first quarter of 2024. \u200bThat\u2019s roughly 28,000 new domains per day!<\/p>\n<p>If you only rely on lists of malicious sites curated by humans, do you think you\u2019d be able to keep up with categorizing 28,000 new domains every day? That\u2019s a job for AI.<\/p>\n<p>DNSFilter\u2019s AI is purpose-built to scale with today\u2019s threat landscape\u2014identifying brand-new domains before they\u2019re weaponized. For example, over a six month period we detected that <a href=\"https:\/\/www.dnsfilter.com\/blog\/2025-cybersecurity-predictions-not-just-ai\">potentially risky new domain traffic <em>more than doubled<\/em><\/a>.<\/p>\n<p>Let\u2019s take a look at some of the sites our AI has categorized as deceptive.<\/p>\n<h3>\u26a0\ufe0fCompromised websites<\/h3>\n<figure class=\"w-richtext-figure-type-image w-richtext-align-fullwidth\">\n<div><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/ddi.mohflo.net\/wp-content\/uploads\/2025\/04\/artificial-intelligence-in-cybersecurity-dnsfilter-1.png?w=640&#038;ssl=1\" alt=\"example of compromised website in malicious attack\"><\/div>\n<\/figure>\n<p>A website that\u2019s compromised is one that a <a href=\"https:\/\/www.kaspersky.com\/resource-center\/threats\/black-hat-hacker\" rel=\"noopener\" target=\"_blank\"><span>black hat hacker<\/span><\/a> has taken over. Hackers will take down the original site, stripping out all of its original content, and rebrand the site as their own with a message.<\/p>\n<p>This frequently occurs on WordPress sites that are not configured correctly and are easy to infiltrate.<\/p>\n<p>Some of the more famous instances of websites being compromised were done by the group <a href=\"https:\/\/en.wikipedia.org\/wiki\/Anonymous_(group)\" rel=\"noopener\" target=\"_blank\"><span>Anonymous<\/span><\/a>, who have vandalized a large number of websites since the late 2000s.<\/p>\n<p>These sites are dangerous because hackers may decide to infect the site with malware or forced downloads that might be transferred to a visitor of the site.<\/p>\n<h3>Malware and AI in 2025<\/h3>\n<p><span>And while compromised sites remain a major risk, the tactics behind them have evolved. In 2025, we\u2019re seeing malware campaigns that not only hide behind these takeovers but are enhanced by AI\u2014delivering faster, smarter, and more evasive threats than ever before.<\/span><\/p>\n<h4><span>AI-Powered Malware Distribution<\/span><\/h4>\n<p>Today, attackers use AI to mutate malware on the fly, automatically rewriting code to evade traditional signature-based detection. These adaptive threats are often distributed via new, quickly registered domains\u2014making real-time AI detection at the DNS layer critical.<\/p>\n<h3>\u26a0\ufe0fCredential phishing<\/h3>\n<figure class=\"w-richtext-figure-type-image w-richtext-align-fullwidth\">\n<div><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/ddi.mohflo.net\/wp-content\/uploads\/2025\/04\/artificial-intelligence-in-cybersecurity-dnsfilter-2.png?w=640&#038;ssl=1\" alt=\"deceptive webpage example in credential phishing\"><\/div>\n<\/figure>\n<p>Phishing occurs when someone attempts to gain unauthorized access to someone\u2019s account, usually in an effort to steal their money. One of the more common phishing sites hackers set up are pages that mimic a company login page that someone might use daily, like Gmail or Office 365.<\/p>\n<p><span><span>In the example above, our AI caught a deceptive page that was meant to trick the user into thinking it is their OneDrive account. This method is particularly misleading as not only does the page provide <\/span><em><span>five<\/span><\/em><span> options for a person to enter personal credentials, but the URL is set up on a Google APIs site. Because this is a legitimate domain, some machine learning algorithms, and certainly <\/span><em><span>many<\/span><\/em><span> static lists, would miss this deceptive site altogether.<\/span><\/span><\/p>\n<h3><span>AI Phishing and Credential Harvesting in 2025<\/span><\/h3>\n<p><span>This new wave of phishing doesn\u2019t stop at fake login pages. With generative AI, attackers are now fabricating voices, faces, and full conversations to manipulate victims\u2014making deepfakes a real and rising threat in credential theft.<\/span><\/p>\n<h4><span>Deepfake Phishing<\/span><\/h4>\n<p>Deepfakes are no longer just a novelty. AI-generated audio and video can convincingly impersonate executives, tricking employees into approving fraudulent transactions or revealing sensitive credentials. <a href=\"https:\/\/www.dnsfilter.com\/use-case\/dns-filtering\"><span>DNSFilter\u2019s AI<\/span><\/a> can flag the domains hosting these spoofed portals and phishing landing pages.<\/p>\n<h4>AI-Generated Credential Harvesting Kits<\/h4>\n<p>Credential phishing has evolved as well. Adversaries are using AI to generate highly-targeted phishing kits, customizing login pages with dynamic content based on the victim\u2019s industry, geography, or recently breached services.&nbsp; Some of these have even been <a href=\"https:\/\/slashnext.com\/blog\/astaroth-a-new-2fa-phishing-kit-targeting-gmail-yahoo-aol-o365-and-3rd-party-logins\/\" rel=\"noopener\" target=\"_blank\"><span>configured to bypass two-factor authentication<\/span><\/a> (2FA).&nbsp;<\/p>\n<p>DNSFilter\u2019s AI can recognize the patterns and infrastructure associated with these kits\u2014even if the content looks original<\/p>\n<h3>\u26a0\ufe0fTemplates and Spam Pages<\/h3>\n<figure class=\"w-richtext-figure-type-image w-richtext-align-fullwidth\">\n<div><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/ddi.mohflo.net\/wp-content\/uploads\/2025\/04\/artificial-intelligence-in-cybersecurity-dnsfilter-3.png?w=640&#038;ssl=1\" alt=\"malicious template example page\"><\/div>\n<\/figure>\n<p>If you look closely at the image above, you\u2019ll notice something strange. This isn\u2019t a completed website. This is just a template. If you were able to click on the page, the links don\u2019t go anywhere and the text is just <em>nonsense<\/em>.<\/p>\n<p>Why is this a problem?<\/p>\n<p>Template sites, or boilerplate sites, are set up to mimic a new webpage and will have some type of malware hidden on a link within the site. You won\u2019t find anything inherently wrong on the homepage of this site, but the deeper you get the more likely you are to stumble onto something malicious.<\/p>\n<p>A human combing through sites might miss that this site is deceptive. Luckily, our well-trained AI caught this one before our customers became affected.<\/p>\n<h3>Malicious Spam and AI Scam Templates in 2025<\/h3>\n<p>While spam templates are a quick and easy way to fool users, generative AI takes them further\u2014automatically building out full scam sites that look polished, legit, and dangerously convincing at first glance.<\/p>\n<h4>Generative AI Scam Pages<\/h4>\n<p>Attackers are now using generative AI to spin up convincing scam websites and fake login pages, complete with copy, branding, and even dynamic chatbots. These pages look increasingly real and can appear on legitimate hosting services or hijacked subdomains, making detection difficult without AI-powered behavioral analysis.<\/p>\n<h2>What does this mean for you?<\/h2>\n<p>At DNSFilter, we\u2019re regularly training and perfecting our AI to notice new types of deceptive sites that haven\u2019t been reported anywhere else before. We\u2019re creating intelligent systems that block 200 million threat queries each day, and we\u2019re doing it <em>before<\/em> your systems are compromised, instead of <em>after<\/em>. That means we find them first, before our customers ever know they\u2019re there.<\/p>\n<p>Find out how you can start relying on artificial intelligence in cybersecurity and <a href=\"https:\/\/app.dnsfilter.com\/signup\" rel=\"noopener\" target=\"_blank\">start your free trial of DNSFilter today<\/a>.<\/p>\n<p><span><em>This article was originally published on February 11, 2020. It was updated on April 8, 2025 to include more recent statistics and information about DNSFilter\u2019s capabilities.<\/em><\/span><\/p>\n<p><a href=\"https:\/\/www.dnsfilter.com\/blog\/artificial-intelligence-in-cybersecurity\">Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The term \u201cartificial intelligence (AI)\u201d was first coined in 1956.<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3853,222],"tags":[3854,230],"class_list":["post-7522","post","type-post","status-publish","format-standard","hentry","category-cybersecurityit","category-featured","tag-cybersecurityit","tag-featured"],"featured_image_urls":{"full":"","thumbnail":"","medium":"","medium_large":"","large":"","1536x1536":"","2048x2048":"","chromenews-featured":"","chromenews-large":"","chromenews-medium":""},"author_info":{"display_name":"DNSFilter","author_link":"https:\/\/ddi.mohflo.net\/index.php\/author\/dnsfilter\/"},"category_info":"<a href=\"https:\/\/ddi.mohflo.net\/index.php\/category\/cybersecurityit\/\" rel=\"category tag\">Cybersecurity&amp;IT<\/a> <a href=\"https:\/\/ddi.mohflo.net\/index.php\/category\/featured\/\" rel=\"category tag\">Featured<\/a>","tag_info":"Featured","comment_count":"0","jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/ddi.mohflo.net\/index.php\/wp-json\/wp\/v2\/posts\/7522","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ddi.mohflo.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ddi.mohflo.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ddi.mohflo.net\/index.php\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/ddi.mohflo.net\/index.php\/wp-json\/wp\/v2\/comments?post=7522"}],"version-history":[{"count":0,"href":"https:\/\/ddi.mohflo.net\/index.php\/wp-json\/wp\/v2\/posts\/7522\/revisions"}],"wp:attachment":[{"href":"https:\/\/ddi.mohflo.net\/index.php\/wp-json\/wp\/v2\/media?parent=7522"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ddi.mohflo.net\/index.php\/wp-json\/wp\/v2\/categories?post=7522"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ddi.mohflo.net\/index.php\/wp-json\/wp\/v2\/tags?post=7522"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}