Provisional understanding of the future cybersecurity landscape in the new age of AI is a quite challenging task due to multiple dynamic socio-technological factors. However, with the progressive development of AI technologies, the task becomes even more complex, joining both human and machine intelligence, while having diferent evolutionary levels of maturing. The study outlines an almost 10-year efort (since 2015) for a successful cybersecurity landscape predictive outlining with progressive identification of future socio-technological transcendents (threats, challenges, opportunities, etc.), implementing Computer Assisted eXercises (CAX) in the loop. Additionally, some generative AI elements have been recently added, trying to achieve an extended holistic analytical framework with a flexible and futuristic scenario-based exploration context. Both machine and human multimodal feedbacks have been successfully implemented in the study, bringing out a comprehensive enough monitoring & quantitative assessment of the future cyberspace evolution with the new age of AI immersion in our new extended reality of living, joining both humans and machines in a new symbiotic human-machine ecosystem.
Getting provisional knowledge about the future has always been a dream of human civilization. With
the fast technological development of the new 21st digital century the AI technologies are already
entering our new socio-technological reality, transforming it in an unprecedented manner [
However, successful exploration of the future digital societies organizational concepts is becoming quite challenging due to multiple unknown issues. Apart of all these facts the future cybersecurity landscape is also evolving, whilst expected to be successfully handled with the AI assistance (though the idea could be somewhat arguable) either completely dominated with the autonomization of machine-tomachine & machine-to-human communications.
Successful identification of these cybersecurity perspectives (threats, challenges, opportunities, divides, etc., called in brief “transcendents”) could significantly benefit with the joint human-machine intellect analytical & validation mixing.
Further in the paper, an ad-hoc exploration framework successful implementations within the last 10-15 years will be given, providing also a further comprehensive viewpoint for the near future towards 2037, i.e. giving similar perspective ahead.
The presented framework hereafter is generalizing some of the methodological approaches, outlined in [
More details on the presented framework, regarding diferent outlooks for cybersecurity future landscape exploration will be given within the next section of the present work.
In this section the triplet of analytical, simulated & mixed outlooks (see Fig. 1) to the future cybersecurity landscape exploration will be given in detail with selected illustrative practical examples.
This exploration outlook combines both morphological & system analysis, while mostly relying on
expert support, thus having a somewhat subjective nature in general. In brief, the analytical part relies
on well-known techniques for unstructured data processing like "morphological analysis" that are
modified with the idea to tailor future uncertainties and establish a scenario pool of plausible and
implausible combinations in a discrete software environment, while handling uncertainty [
Both studies clearly outline aggregated future trends for AI autonomization and mixing with infrastructure, producing numerous smart services. Apart of this, future people are also transforming with AI-assisted capabilities, concerning future homes and cities. This however has a dual nature establishing at the same time a smart cyber security landscape with dual ofensive and defensive roles for both humans & machines. Though sounding quite sci-fi-oriented these findings are already getting visibility with AI progressive implementations. So, some further results’ dynamic assessments, adding machine algorithms intelligence together with the human ones will be presented next.
Being somehow static the outlined analytical findings in Section 3.1 are explored also dynamically
taking in to account the ideas from [13, 14]. In brief, the accomplished ideas could be aggregated on each
of the system model relations with an S-shaped probabilistic exploration. The last is approximated using
Quasi-Monte Carlo scenario-based integration within the four stages (assuming the Kondratiev’s society
dynamics assumptions) of evolution [
The organization of this stage is quite complex as it combines both (i) messages exchanged amongst the CAX participants (following an agent-based multirole architecture), together with their (ii) engagement & response assessment within the training process (measured by a battery of psycho & biometrics). Additionally, with the progressive development of AI, (iii) generative & indexing smart tools have been also recently implemented from a machine perspective. Social verification (because of the prognostic nature of the task) is also applicable via diferent events like workshops, summer schools, conferences, etc. (see e.g. [17]) Some recent illustrations of the above-mentioned mixed outlook, taking the CYREX 2024 organization [18], extended reality verification mini-lab (with heart rate, respiration, GSR set dynamics with HTC VIVE XR Elite & Bitalino/Plux wearable biomonitoring box, i.e. and IoT device) and joint human-machine multicriteria assessment, following the experience with [16], but adding also ChatGPT generative AI feedback are given below.
What is important to note here is the presence of AI within this mixed outlook stage. This actually should be taken with the assumption for human extensive control. In any case, it is important to note that both AI-generated scenarios (for CYREX 2024 & the Society 6.0 context, by Invideo AI) demonstrate constant striving algorithmic subjectiveness towards machine domination. It should be noted that the AI-observed data was taken completely from human recent discussions during “Digital Transformation in the Age of AI” training at SRS’2024 [19]. Apart of these, the event assessment amongst the participants and via AI was also somewhat challenging as the machine-generated responses have a diferent, autonomous viewpoint, even with exclusive human interests’ context requesting.
Properly understanding the future cybersecurity landscape certainly requires a complex outlook from both static and dynamic analytical perspectives. In this sense however, it is extremely important to note the role of AI that if it becomes out of control could find a dominant position, especially with the generation of completely unknown, or very uncommon new cyber threats, either inclined feedback results. Luckily, the process is still dominated by the human intellect and could be significantly supported with AI assistance that has no-dominative behavior, i.e. semiautonomous AI, and completely takes the human beings leading decision-making & responsibility position. Whether this position is right or wrong is a really philosophical moment that from a technical perspective has no particular meaning, at least for the present technological level of our new digital society aiming for joint symbiotic human-machine co-existence.
The study is granting special appreciation for the extended reality mini-lab establishment and further exploration funding support to the National Scientific Programme “Security & Defense”. Additional gratitude for the international expert support is given to EDIH Trakia & the initiative “Securing Digital Future 21” with more than sixty countries now, spread around the world, https://securedfuture21.org/.
The author has not employed any Generative AI tools. [13] K. R. Dark, The waves of time: long-term change and international relations, Bloomsbury Publishing, 2016. [14] D. Meadows, J. Randers, D. Meadows, A synopsis: Limits to growth: The 30-year update, Estados
Unidos: Chelsea Green Publishing Company 381 (2004). [15] Z. Minchev, Malicious future of ai: transcendents in the digital age, in: The 12th International
Conference on Business Information Security (BISEC-2021), 2021, pp. 18–22. [16] Z. M. Minchev, L. Boyanov, Future of smart cities security challenges – proactive modelling & identification, in: The 14th International Conference on Business Information Security (BISEC2023), 2024, pp. 12–17. [17] Web Forum Initiative, Secure digital future 21, 2024. URL: https://securedfuture21.org/. [18] CYREX 2024, Cyber research exercise 2024, 2024. URL: https://securedfuture21.org/cyrex_2024/ cyrex_2024.html. [19] Digital Transformation in the Age of AI, Video Report, SRS’2024, Varna, Bulgaria, 2024. URL: https://youtu.be/xnXkrPkgrDQ.