AI in cybersecurity – a chance for survival?

In recent years, artificial intelligence (AI) has become a key driver of digital transformation, and its impact on cybersecurity is groundbreaking. Modern IT systems operate in an environment of increasing complexity and constantly evolving threats, rendering traditional protection methods often insufficient. In this context, AI brings a new level of capability by enabling faster anomaly detection, automated incident response, and real-time analysis of vast datasets. On one hand, artificial intelligence presents a tremendous opportunity for security professionals. Machine learning-based systems can identify attack patterns, predict potential threats, and support defensive measures on an unprecedented scale. Consequently, organizations can more effectively protect their data, mitigate the risk of breaches, and respond more rapidly to incidents.

The role of artificial intelligence in network protection

Artificial intelligence plays an increasingly important role in network protection, primarily due to its ability to process vast amounts of data and detect patterns that would be difficult for humans to spot. In modern IT security systems, AI not only supports analysts but often acts as the first line of defense by monitoring infrastructure in real time. Identifying threats through the analysis of network traffic and user behavior is a fundamental application of AI in network security. Machine learning algorithms learn what “normal” system operation looks like and then detect any deviations from that norm. This enables the detection of unusual activities-such as unauthorized access attempts, suspicious data transfers, or signs of malware-at an early stage of an attack.
AI also significantly accelerates the response to security incidents. Traditional approaches relied on manual analysis and decision-making by specialists, a process that was often time-consuming. AI-driven systems can automatically classify and prioritize threats, and even initiate defensive actions-such as blocking suspicious IP addresses, isolating infected devices, or launching remediation procedures. Such automation allows response times to be reduced from hours to seconds.

Opportunities and benefits of using AI in cybersecurity

AI supports threat analysis and prediction. By learning from accumulated data, systems recognize attack patterns and forecast the techniques likely to be used in the future. This enables companies to take a proactive approach, strengthening defenses before an incident occurs. Another key area is the reduction of false alarms. Traditional security systems often generate a high volume of notifications, many of which do not represent genuine threats. AI can better assess the context of events and filter alerts, allowing IT teams to focus on actual issues. For companies, the benefits are tangible: enhanced protection effectiveness, lower operating costs through automation, and reduced risk of downtime and data loss. For users, this primarily means greater information security, improved privacy protection, and more reliable digital services.

AI-related threats in the context of cybersecurity

While the development of artificial intelligence in cybersecurity offers numerous benefits, it also presents serious risks. It is particularly concerning that the very technologies used to strengthen defense systems can be just as effectively exploited by cybercriminals. One major threat is the automation and scalability of attacks. AI enables criminals to create malware capable of real-time adaptation—learning to bypass security measures and adjusting its behavior to the victim’s environment. Intelligent malware variants, for instance, can alter their signatures, making detection by traditional security systems difficult. AI-driven threats falling under the category of social engineering attacks are especially dangerous. AI allows for the generation of highly convincing phishing messages tailored to specific recipients based on data analysis. There is also increasing use of “deepfake” technology to create realistic audio or video recordings that can impersonate trusted individuals, such as superiors or business partners. Another issue is the use of AI to automatically scan for security vulnerabilities. Algorithms can analyze systems and applications far faster than humans, identifying potential weaknesses that can subsequently be exploited in an attack. Consequently, the time gap between the discovery of a vulnerability and its exploitation is significantly reduced.

The Future of Artificial Intelligence in IT Security

The future of artificial intelligence in cybersecurity promises dynamic technological development, with AI becoming increasingly integrated into IT defense systems. Amidst the rising number and complexity of threats, the central debate will focus on determining which tasks should be handled by AI versus the security implications of AI models processing information. A key area of ​​development will be the continued automation and autonomization of security systems. AI will increasingly move beyond mere threat detection to independently making decisions and responding to incidents without human intervention. In the future, so-called autonomous defense systems could operate in real-time, analyzing the context of an attack and selecting the optimal response strategy. While this offers the potential for significantly faster and more effective protection, ensuring oversight of algorithmic decisions remains a challenge. Another significant trend will be the development of “explainable AI.” As AI plays an increasingly vital role in protecting critical infrastructure, there will be a growing need for greater transparency regarding how algorithms operate. Organizations will demand systems that not only make accurate decisions but can also justify them a capability particularly important for audits, regulatory compliance, and building trust in the technology. However, unlocking this potential will require striking a balance between innovation and control, effectiveness and security, and automation and accountability. Legal and ethical considerations will also play a crucial role; as AI systems become more autonomous, questions will arise regarding liability for their decisions, user privacy protection, and the boundaries of using such technology to monitor activity. Legal regulations will have to keep pace with the rate of technological change, which may pose a challenge for both companies and state institutions.