Cybersecurity practices are a consequence of already discovered threats, prescriptions for mitigating the consequences of attacks, and restoring systems after breaches. This hitherto non-alternative approach puts organizations in a permanent defensive position and leaves the initiative to malicious actors. Widely used tools at the moment, such as firewalls, antivirus programs, intrusion detection and intrusion prevention systems, work against already known malware and indicators of compromise. In such a case, an adequate response to protect systems often comes after hackers have already established unauthorized access to networks.
Generative AI shows potential to shift this paradigm and place cybersecurity practices in a proactive context, establishing itself not only as an important tool but also as an active autonomous participant in the processes of analysis, prevention, and forecasting of risks and threats. This new role of generative AI is implemented in three main steps, and its integration successfully complements current cybersecurity practices without replacing them:
- From detection to prediction: through the rapid processing of large data sets using large language models (LLMs), cyber systems are capable of running scenario simulations and predicting attack probabilities, thus enabling rapid correlation between hidden indicators of compromise.
- From asset protection to knowledge and asset management: security is no longer just a collection of technical controls and sequential steps to be followed under predetermined circumstances. It is now sought through a flexible and intelligent system for high-speed data processing and context-aware analysis with automated tools for rapid decision-making.
- From human reaction to machine collaboration: AI does not replace human expertise, but rather multiplies its interpretation capabilities and frees it from repetitive routine tasks.
The aforementioned transition does not have only technical dimensions. It is accompanied by organizational and ethical dilemmas and requires expert preparation for building a predictable and secure cybersecurity system, assisted by AI. The change requires a fusion of already established system elements, such as cyber intelligence, machine learning, and strategic risk management. Thus, this activity can evolve into an offensive one and be carried out simultaneously by a human factor and AI with clearly distinguishable roles.
The ability of AI to process vast amounts of data transforms cybersecurity into a dynamic self-learning system, the essence of which is to continuously change its approaches according to the incoming flow of information. With the help of AI, cybersecurity activities shift from the position of a last line of defense to the vanguard of the digital battlefield. All this, however, does not come without risks, some of which will be discussed in the following pages.