There are too few aspects of modern progress that have remained unaffected by the spread of AI in the last few years. The technology is no longer experimental, but sufficiently advanced to take center stage in the ongoing digital transformation. Generative AI is sparking a new phase in the development of intelligent systems because, unlike other types of AI, generative AI is actually specialized in creating new content, helping to generate ideas and strategies.
Naturally, the models are limited by the volume and type of information they are trained on, but the coined term “creative modeling” actually describes the capabilities of generative AI extremely accurately[1]. The consequences are diverse, which is true for a number of technologies. In the context of cybersecurity, generative AI is already widely used in malicious activities on the internet, while for the needs of cyber defense, ways for secure mass deployment are still being sought.
In this sense, this subchapter aims to reveal the specifics of generative AI and to outline the ethical dilemmas, risks, and functionality of the technology in the context of the concept of cybersecurity. The historical path of AI, as an avant-garde technology, is clearly traceable, and the periods of its evolution are distinguishable.
Even in the earliest stages of AI development, the technology found applications in terms of security, as its role in the analysis of encrypted texts during the Second World War and the cracking of the “Enigma” code can be pointed out. Nowadays, AI is gaining widespread use both in defensive and malicious activities without the need for significant resources and technological skills. It is necessary to examine the existing definitions of AI to clarify the essence of the technology.
History recognizes Alan Turing as one of the most important pioneers in the creation of AI and the foundation of computer science. In 1936, Turing defined the abstract “Turing machine,” which performs computational operations based on set rules, and through which the same calculations performed by modern computers can be carried out. During the Second World War, Turing applied his discoveries to the analysis of encrypted text and automated the decryption of messages encoded with “Enigma.” Thus, he was able to take advantage of the superiority of computers over human speed and accuracy in complex intellectual tasks.
In 1950, the mathematician raised the question “can machines think?” and proposed an experimental framework through which to conduct a check, known as the “Turing test”[2]. The essence of the test consists of conducting a conversation using natural speech between a human evaluator and an unknown participant, who may be a machine. If, during the test, the evaluator cannot definitively distinguish whether they are contacting a human or a machine, it is considered that the machine exhibits intelligence. This criterion for “intelligence” is set out in Turing’s article “Computing Machinery and Intelligence,” in which the author additionally hints at fundamental ideas regarding AI that find manifestation at a later stage.
One of the ideas is that machines could learn from past experience, mentioning the concept of a “child machine” that would be educated and trained over time. Such an insight at that time, that the ability to learn is as necessary as logical reasoning for the presence of intelligence, was an early signal that machine learning is possible at all[3]. The aforementioned two Turing discoveries—the “Turing machine” and the “Turing test”—reveal the enormous potential of the new technology and the new horizons that a number of scientists at the time were embarking upon. The practical proof of the idea that machines are capable of reproducing cognitive functions typical of humans was the catalyst for the creation of the multidisciplinary and multi-vector science of AI, and Alan Turing earned the recognition as its foundational figure.
The speed at which AI is developing requires the finding of a coherent and meaningful definition that will not cause misunderstanding and ambiguity over time. A precise definition of AI, however, continues to be sought due to the growing spectrum of new capabilities, the continuous discovery of additional applications, and different philosophical interpretations.
For the purposes of this monograph, it is extremely important to derive a clear working definition. In this regard, it could be noted that AI is the ability of machines to perform activities typical of human intelligence. Such a formulation does not contradict already made attempts to point to activities such as reasoning, learning, problem-solving, perception, and speech understanding[4], as typical of the presence of AI.
In 2021, the European Commission derived its own definition of AI, defining it as a program that can generate content, make predictions, provide recommendations, etc., after certain goals set by a human factor have been provided[5]. The common thread among the diverse definitions of AI is that they concern an attempt to replicate cognitive activity typical of humans through machines, but one must keep in mind the broad-spectrum purpose of existing models at this moment. Their activity can involve the execution of simple instructions or the generation of complex end results requiring the existence of autonomous systems.
An observed trend in the development of the technology is increased complexity with a reduced need for human intervention, for which machine learning and deep learning can be cited as examples. In these cases, the models adapt their behavior according to available datasets without human configuration.
Of course, the question may arise here as to what distinguishes conventional computer calculations from AI? The differences are significant and include autonomy, adaptability, ability to generate results, and others. The latter is particularly important for generative AI and makes possible the dual-use of the technology, which has already entered activities such as diagnosing in healthcare, improving transportation systems, and strengthening some activities in cybersecurity.
The reverse is also true, because the use of generative AI as a weapon in cyberattacks and disinformation campaigns is already widespread. It is precisely this duality that necessitates accepting AI not only as a tool but also as a strategic capability with possible profound consequences for digital security. Understanding these fundamental features of the technology is necessary for a better understanding of its emerging role as a catalyst for opportunities, but also as a bearer of risks and a vector for attacks in the contemporary digital environment.
[1] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.,
https://www.deeplearningbook.org/
[2] Turing, A.M. (1950) ‘I.—COMPUTING MACHINERY AND INTELLIGENCE,’ Mind, LIX(236), pp. 433–460. https://doi.org/10.1093/mind/lix.236.433
[3] Muggleton, S. (2014) ‘Alan Turing and the development of Artificial Intelligence,’ AI Communications, 27(1), pp. 3–10. https://doi.org/10.3233/aic-130579
[4] Russell, S.J., Norvig, P. and Davis, E. (2010) Artificial intelligence: A Modern Approach. Prentice Hall., https://people.engr.tamu.edu/guni/csce625/slides/AI.pdf
[5] European Commission, (2021), Laying Down Harmonised Rules On Artificial Intelligence (Artificial Intelligence Act) And Amending Certain Union Legislative Acts, Article 3, 1, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52021PC0206 (accessed 06.05.2025)