, a subset of AI, enables systems to learn and improve from data without explicit programming, making decisions based on patterns and large datasets. It is currently the most relevant area for cybersecurity.<\/span><\/p>\nDeep Learning (DL)<\/b>, a branch of ML, uses artificial neural networks to model complex relationships and solve problems with large datasets. Since DL falls under ML, this discussion will primarily focus on machine learning.<\/span><\/p>\n\n- Lowering the Barrier to Entry<\/b><\/li>\n<\/ul>\n
The entry barrier into this field is notorious for its high demands on technical expertise. Early tools like firewalls used simple traffic rules, but as networks grew more complex, creating and validating these rules became increasingly challenging.\u00a0<\/span><\/p>\nAI can simplify this process by writing accurate rules while providing specialists with an interface, such as a natural language processing chat system. A cybersecurity professional could describe what traffic to allow or block and the conditions under which specific rules should apply, and the AI would generate machine-readable policies, ensuring proper syntax and semantics. This streamlines rule development, making the field more accessible and reducing the effort required for security management.<\/span><\/p>\n\n- Asset Inventory and Attack Path Mapping<\/b><\/li>\n<\/ul>\n
As corporate networks grow more complex and evolve into hybrid and multi-cloud environments with global points of presence, managing and securing them has become very challenging. Modern networks can also scale automatically with demand, adding to the difficulty of inventorying assets, identifying threats, and modeling potential attack paths.<\/span><\/p>\nAI can help with these tasks by continuously scanning networks, cataloging assets, and adding contextual insights. With its ability to learn from data, AI already outperforms humans in forecasting and can analyze network architectures to identify potential attack chains. This helps cybersecurity teams prioritize efforts, shifting the focus from reactive measures to proactive defense. With AI, it becomes clearer which vulnerabilities attackers might exploit and how to fortify them effectively.<\/span><\/p>\n\n- Vulnerability Management<\/b><\/li>\n<\/ul>\n
The complexity of vulnerability management grows alongside the increasing size and intricacy of corporate networks, the number of identified vulnerabilities, available exploits, and vulnerability assessment metrics. Launching a vulnerability management program in a large network can feel like searching for a needle in a haystack for cybersecurity specialists. Traditional vulnerability scanners often produce massive reports with thousands of vulnerabilities of varying severity, accompanied by remediation recommendations that may lack relevance without business and application context.<\/span><\/p>\nAI can play several key roles in this process to support professionals:<\/span><\/p>\n\n- Correlating vulnerability data with information about exploits and related attacks.<\/span><\/li>\n
- Enriching system vulnerability data with business context.<\/span><\/li>\n
- Prioritizing vulnerabilities for remediation and automating patch deployment.<\/span><\/li>\n<\/ol>\n
Zero-day vulnerabilities are an additional challenge, but AI can assist by analyzing large volumes of information to identify and track zero-day vulnerabilities across different technologies.<\/span><\/p>\nMalware Detection and Analysis<\/b><\/p>\n
Malware is the backbone of modern cyberattacks, with its volume rising alongside cybercriminal groups, the number of attacks, and attackers’ budgets. Cybercriminals use advanced techniques to enhance malware and evade detection. Some even leverage AI to develop new malware samples more quickly and efficiently.<\/span><\/p>\nAI can help by identifying malware through behavioral analysis and assisting in reverse engineering, where specialists analyze malware to improve defenses. In reverse engineering, AI can act as a consultant, explaining code segments and the possible intentions behind malware developers\u2019 choices, streamlining the analysis process for cybersecurity professionals.<\/span><\/p>\nThreat and Attack Monitoring<\/b><\/p>\n
Cyberattacks are becoming more frequent, complex, and fast. What once took months now takes seconds. Modern attackers move laterally, steal data, and erase traces, enabling them to target more victims and maximize their impact. This behavior floods cybersecurity teams with alerts, making rapid response a deciding factor in this complex game.<\/span><\/p>\nHowever, many are false positives, leading to alert fatigue among professionals. As networks and data grow, manual log analysis is no longer feasible, especially with the ongoing shortage of skilled cybersecurity specialists.<\/span><\/p>\nThis is why delegating continuous network monitoring and threat detection to AI and automating responses to attack indicators is the best way forward. Fortunately, most cyberattacks follow common patterns AI can learn, enabling lightning-fast responses to stay ahead of attackers. AI operates 24\/7 without fatigue, quickly adapts to new data, reduces false positives, and can generate recommendations for preventive measures when attack traces are found, covering gaps that human specialists might overlook. A dream partner, at best.<\/span><\/p>\nPhishing Protection<\/b><\/p>\n
One human trait that weakens corporate cybersecurity systems is our tendency to act on emotions. Cybercriminals exploit this vulnerability through social engineering, particularly phishing, using employees as entry points into corporate networks.\u00a0<\/span><\/p>\nTo make the attacks more effective, attackers increasingly incorporate AI to craft more convincing phishing emails and target more victims. In response, cybersecurity professionals can protect employees from phishing attacks by training AI models on large datasets of known social engineering techniques.<\/span><\/p>\nBehavior Monitoring and Insider Threat Detection<\/b><\/p>\n
Protecting against insider threats is still one of the biggest challenges in cybersecurity. Insiders have legitimate access to corporate systems, making detection more difficult.\u00a0<\/span><\/p>\nAI-powered systems can automatically identify suspicious actions, such as unauthorized access to sensitive data or attempts at data theft. Using machine learning, AI adapts to changes in employee behavior, reducing false positives. Plus, AI helps predict risks by analyzing historical data and identifying patterns that signal potentially malicious actions by employees.<\/span><\/p>\nEnhanced Cybersecurity Event Search<\/b><\/p>\n
Google is a staple in the world of search engines, offering results we all rely on. However, its search results often feel more like a table of contents than a concise summary of critical points. For cybersecurity professionals, having an enhanced search tool can make all the difference in addressing cyber threats.<\/span><\/p>\nA simple “table of contents” isn\u2019t enough when specialists need detailed insights into the state of a protected corporate network. AI-powered systems can step in to improve traditional search capabilities, providing the critical context needed to make informed decisions and respond effectively to threats.<\/span><\/p>\nMinimizing the Human Error<\/b><\/p>\n
Managing thousands of hosts while adhering to security rules can be overwhelming. This is where AI can help by learning from correct configurations and past mistakes, identifying errors, and flagging them in real time. Additionally, AI could proactively generate host configurations based on descriptions of human-provided functionality.<\/span><\/p>\nEmbrace the Change<\/span><\/h2>\nWhile a leap toward fully autonomous AI systems seems relatively unlikely, AI has the potential to complement human expertise, empowering professionals to handle the most pressing issues in the field. The key to unlocking AI’s potential lies in having skilled specialists who understand how it works and apply creativity and critical thinking to make the technology even more effective.<\/span><\/p>\nThroughout history, every major technological breakthrough has sparked fear and uncertainty. Yet, over time, we have learned to adapt, embrace these tools, and use them effectively, balancing their capabilities with our limitations. It\u2019s time to do the same with AI: to integrate it into cybersecurity and delegate tasks where AI performs better than we do.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"AI is rapidly taking its place in the market, penetrating new application areas in ways we couldn\u2019t imagine, including AI cybersecurity solutions. The hype shows no signs of fading. In fact, it is gaining real momentum even among C-level executives. The reason is clear: AI\u2019s potential for improving efficiency is almost limitless.\u00a0 But so is […]<\/p>\n","protected":false},"author":10,"featured_media":62147,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":{"subtitle":"Can AI Solve the Cybersecurity Skills Gap?","format":"standard","override":[{"template":"5","layout":"right-sidebar","sidebar":"default-sidebar","second_sidebar":"default-sidebar","share_position":"float","share_float_style":"share-normal","show_share_counter":"1","show_view_counter":"1","show_featured":"1","show_post_meta":"1","show_post_author":"1","show_post_author_image":"1","show_post_date":"1","post_date_format":"default","post_date_format_custom":"Y\/m\/d","show_post_category":"1","show_post_reading_time":"0","post_reading_time_wpm":"300","post_calculate_word_method":"str_word_count","zoom_button_out_step":"2","zoom_button_in_step":"3","show_post_tag":"1","number_popup_post":"1","show_author_box":"0","show_post_related":"1","show_inline_post_related":"0"}],"image_override":[{"single_post_thumbnail_size":"no-crop","single_post_gallery_size":"crop-715"}],"trending_post_position":"meta","trending_post_label":"Trending","sponsored_post_label":"Sponsored by","disable_ad":"0"},"jnews_primary_category":[],"jnews_social_meta":[],"jnews_override_counter":{"view_counter_number":"0","share_counter_number":"0","like_counter_number":"0","dislike_counter_number":"0"},"footnotes":""},"categories":[408,3229],"tags":[963,3609],"coauthors":[17554],"class_list":["post-62142","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-contributors","category-artificial-intelligence","tag-ai","tag-cybersecurity"],"_links":{"self":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts\/62142","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/comments?post=62142"}],"version-history":[{"count":"6","href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts\/62142\/revisions"}],"predecessor-version":[{"id":62153,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/posts\/62142\/revisions\/62153"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/media\/62147"}],"wp:attachment":[{"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/media?parent=62142"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/categories?post=62142"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/tags?post=62142"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/dataconomy.ru\/wp-json\/wp\/v2\/coauthors?post=62142"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}