AI is rapidly taking its place in the market, penetrating new application areas in ways we couldn’t 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’s potential for improving efficiency is almost limitless.
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.
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.
AI Cybersecurity Challenges
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 $8 trillion in 2023, projected to grow to $27 trillion by 2027. At the same time, the world faces a severe shortage of cybersecurity professionals.
However, there is a growing concern that legitimate organizations and cybercriminals are adopting AI technologies. According to a survey by Sapio Research и Deep Instinct, 75% of cybersecurity professionals have observed an increase in cyberattacks, and 85% believe that AI technologies are likely contributing to this surge.
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.
The Human Context
Introducing the most significant weakness in cybersecurity systems: human error. Time and again, we’ve 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.
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.
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.
Use Cases of AI in Cybersecurity
Before we get into specific use cases, let’s briefly define the technologies mentioned to establish a foundation for discussing their use cases.
Artificial Intelligence (AI) 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.
Machine Learning (ML), 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.
Deep Learning (DL), 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.
- Lowering the Barrier to Entry
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.
AI 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.
- Asset Inventory and Attack Path Mapping
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.
AI 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.
- Vulnerability Management
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.
AI can play several key roles in this process to support professionals:
- Correlating vulnerability data with information about exploits and related attacks.
- Enriching system vulnerability data with business context.
- Prioritizing vulnerabilities for remediation and automating patch deployment.
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.
4. Malware Detection and Analysis
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.
AI 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’ choices, streamlining the analysis process for cybersecurity professionals.
5. Threat and Attack Monitoring
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.
However, 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.
This 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.
6. Phishing Protection
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.
To 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.
7. Behavior Monitoring and Insider Threat Detection
Protecting against insider threats is still one of the biggest challenges in cybersecurity. Insiders have legitimate access to corporate systems, making detection more difficult.
AI-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.
8. Enhanced Cybersecurity Event Search
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.
A simple “table of contents” isn’t 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.
9. Minimizing the Human Error
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.
Embrace the Change
While 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.
Throughout 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’s time to do the same with AI: to integrate it into cybersecurity and delegate tasks where AI performs better than we do.
