{"id":62142,"date":"2024-12-18T15:24:28","date_gmt":"2024-12-18T14:24:28","guid":{"rendered":"https:\/\/dataconomy.ru\/?p=62142"},"modified":"2024-12-18T16:03:42","modified_gmt":"2024-12-18T15:03:42","slug":"ai-cybersecurity-replacement-specialists","status":"publish","type":"post","link":"https:\/\/dataconomy.ru\/2024\/12\/18\/ai-cybersecurity-replacement-specialists\/","title":{"rendered":"AI Cybersecurity \u2014\u00a0 Replacement for Specialists or Efficiency Booster?"},"content":{"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<\/span><\/p>\n

But so is its potential for disruption. In the realm of cybersecurity, the stakes are as high as ever. The use of AI is evident on both sides of the barricades: by attackers and defenders alike.\u00a0<\/span><\/p>\n

In this article, I explore the impact of AI on the field of cybersecurity, describe potential use cases and their likely effectiveness, discuss challenges related to AI technologies themselves, and reflect on the threats AI poses to the jobs of cybersecurity professionals.<\/span><\/p>\n

AI Cybersecurity Challenges<\/span><\/h2>\n

Cybersecurity is a buzzworthy field, not so much for its efficiency but for its challenges. As the number of successful cyberattacks continues to rise, the U.S. Agency for International Development estimates the global cost of cybercrime at <\/span>$8 trillion in 2023<\/span>, projected to grow to $27 trillion by 2027. At the same time, the world faces a severe shortage of cybersecurity professionals.<\/span><\/p>\n

However, there is a growing concern that legitimate organizations and cybercriminals are adopting AI technologies. According to a survey by Sapio Research \u0438 Deep Instinct, <\/span>75% of cybersecurity professionals have observed an increase in cyberattacks, <\/span>and 85% believe that AI technologies are likely contributing to this surge.<\/span><\/p>\n

Indeed, attackers are increasingly leveraging AI to efficiently gather and process information about their targets, prepare phishing campaigns, and develop new versions of malware, enhancing the power and effectiveness of their malicious operations. Meanwhile, the digital world’s data growth outpaces human cognitive capacity, and cybersecurity talent cannot scale fast enough due to high expertise requirements. As external factors reshape the industry, existing challenges are intensifying under the surge of data and attacks.<\/span><\/p>\n

The Human Context<\/span><\/h2>\n

Introducing the most significant weakness in cybersecurity systems: human error. Time and again, we\u2019ve seen data breaches where systems designed to process and store valuable information within a protected network were left unsecured and exposed to public access due to configuration mistakes by personnel.<\/span><\/p>\n

Efficiency is yet another pain point in cybersecurity. Specialists cannot consistently and flawlessly handle hundreds of daily alerts, and managing manual processes becomes increasingly difficult as corporate networks grow more complex and diverse, as they do today.<\/span><\/p>\n

As in other industries, cybersecurity relies heavily on human intervention. Cybersecurity professionals validate database configurations before processing valuable data, scan the codebase of new applications before their release, investigate incidents, and identify root causes, among other tasks. But it is also time for us to embrace AI to improve efficiency and give cybersecurity defenders an edge.<\/span><\/p>\n

Use Cases of AI in Cybersecurity<\/span><\/h2>\n

Before we get into specific use cases, let\u2019s briefly define the technologies mentioned to establish a foundation for discussing their use cases.<\/span><\/p>\n

Artificial Intelligence (AI)<\/b> is a field of computer science focused on creating systems that perform tasks requiring human intelligence, such as language processing, data analysis, decision-making, and learning. It serves as the overarching discipline, with other areas falling under its umbrella.<\/span><\/p>\n

Machine Learning (ML)<\/b><\/a>, 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>\n

Deep 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