The Rise of Automated Cybercrime: How AI is Redefining Cyber Threats

Author

Harshita Gautam, Ritu Dagar, and Dr. Manju Rani

Abstract

 Artificial intelligence (AI) is changing the economics and operational possibilities of cybercrime, but it is important to distinguish automated activity from activity specifically enabled by AI. This paper examines AI-assisted and automated cyber threats, with attention to phishing and social engineering, credential theft, ransomware, reconnaissance, and vulnerability exploitation. It uses an exploratory mixed-source design: a questionnaire completed by 36 university students is interpreted alongside published evidence from IBM X-Force, the World Economic Forum (WEF), the European Union Agency for Cybersecurity (ENISA), Verizon, and peer-reviewed research. In the student sample, 32 respondents (88.9%) reported awareness of AI-based cyber threats; 19 (52.8%) reported receiving suspicious or potentially phishing-related messages; 22 (61.1%) believed AI makes cybercrime more dangerous; and the most common self-rated cybersecurity-awareness score was 3 out of 5 (47.2%). External reports describe an 84% year-on-year increase in emails delivering infostealer malware observed by IBM in 2024, while WEF reported that 66% of surveyed organisations expected AI to have a significant cybersecurity impact and 37% had processes for assessing AI-tool security before deployment. These figures come from different samples and measurement frameworks and are not directly comparable. The paper argues that AI is best understood as an amplifier of selected attack capabilities rather than a universal or sole cause of cybercrime. It recommends layered controls, identity protection, secure AI adoption, verification procedures, and practical cybersecurity education. Because the student sample is small and self-reported, the primary findings are descriptive and exploratory rather than generalisable or causal.

Keywords

artificial intelligence, automated cybercrime, generative AI, phishing, social engineering, cybersecurity awareness, credential theft

Full Text:

Download Paper PDF

References

Atuncar Flores, E. J., Chuan García, A. F., Ráez Martínez, H. T., & Pachas Quispe, G. H. (2024). Algorithms based on artificial intelligence for the detection and prevention of social engineering attacks: Systematic review. *Proceedings of the LACCEI International Multi-Conference for Engineering, Education, and Technology, 1*(10). https://doi.org/10.18687/LACCEI2024.1.1.1026

European Union Agency for Cybersecurity. (2024). *ENISA threat landscape 2024*. https://www.enisa.europa.eu/publications/enisa-threat-landscape-2024

IBM. (2025). *X-Force threat intelligence index 2025*. IBM Security. https://www.ibm.com/think/x-force/x-force-threat-intelligence-index-2025-attackers-steal-sell-user-identities

Kutscher, J. (2025, April 23). *M-Trends 2025: Data, insights, and recommendations from the frontlines*. Google Cloud. https://cloud.google.com/blog/topics/threat-intelligence/m-trends-2025/

Naqvi, B., Perova, K., Farooq, A., et al. (2023). Mitigation strategies against the phishing attacks: A systematic literature review. *Computers & Security, 132*, 103387. https://doi.org/10.1016/j.cose.2023.103387

Pascoe, C., Quinn, S., & Scarfone, K. (2024). *The NIST Cybersecurity Framework (CSF) 2.0* (NIST Cybersecurity White Paper CSWP 29). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.CSWP.29

Schmitt, M., & Flechais, I. (2024). Digital deception: Generative artificial intelligence in social engineering and phishing. *Artificial Intelligence Review, 57*, Article 324. https://doi.org/10.1007/s10462-024-10973-2

The weaponization of artificial intelligence in cybersecurity: A systematic review. (2024). *Procedia Computer Science, 239*, 547–555. https://doi.org/10.1016/j.procs.2024.06.206

Verizon. (2025). *2025 data breach investigations report*. https://www.verizon.com/business/resources/reports/dbir/

World Economic Forum. (2025). *Global cybersecurity outlook 2025*. https://www.weforum.org/publications/global-cybersecurity-outlook-2025/

Primary survey data. (2026). *Google Forms survey on AI-based cybercrime awareness* [Unpublished raw survey summary; n = 36].

The indicators in Table 6 are proposed evaluation measures, not outcomes observed in the present study. They should be selected in advance, measured consistently, and reviewed for unintended effects such as excessive false-positive alerts or privacy intrusion.

Share your valuable work from Social Media Buttons