| Title |
Navigating the CISO’s mind by integrating GenAI for strategic cyber resilience / |
| Authors |
Grigaliūnas, Šarūnas ; Brūzgienė, Rasa ; Driaunys, Kęstutis ; Danielienė, Renata ; Veitaitė, Ilona ; Astromskis, Paulius ; Nemickienė, Živilė ; Vengalienė, Dovilė ; Lopata, Audrius ; Andrijauskaitė, Ieva ; Gaubienė, Neringa |
| DOI |
10.3390/electronics14071342 |
| Full Text |
|
| Is Part of |
Electronics.. Basel : MDPI. 2025, vol. 14, iss. 7, art. no. 1342, p. 1-20.. ISSN 2079-9292 |
| Keywords [eng] |
GenAI ; cyberforensics ; CISO ; OSINT ; digital attributes ; 5W |
| Abstract [eng] |
Abstract: AI-driven cyber threats are evolving faster than current defense mechanisms, complicating forensic investigations. As attacks grow more sophisticated, forensic methods struggle to analyze vast wearable device data, highlighting the need for an advanced frame- work to improve threat detection and responses. This paper presents a generative artificial intelligence (GenAI)-assisted framework that enhances cyberforensics and strengthens strategic cyber resilience, particularly for chief information security officers (CISOs). It addresses three key challenges: inefficient incident reconstruction, open-source intelligence (OSINT) limitations, and real-time decision-making difficulties. The framework integrates GenAI to automate routine tasks, the cross-layering of digital attributes from wearable devices and open-source intelligence (OSINT) to provide a comprehensive understanding of malicious incidents. By synthesizing digital attributes and applying the 5W approach, the framework facilitates accurate incident reconstruction, enabling CISOs to respond to threats with improved precision. The proposed framework is validated through ex- perimental testing involving publicly available wearable device datasets (e.g., GPS data, pairing and activity logs). The results show that GenAI enhances incident detection and reconstruction, increasing the accuracy and speed of CISOs’ responses to threats. The ex- perimental evaluation demonstrates that our framework improves cyberforensics efficiency by streamlining the integration of digital attributes, reducing the incident reconstruction time and enhancing decision-making precision. The framework enhances cybersecurity resilience in critical infrastructures, although challenges remain regarding data privacy, accuracy and scalability. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2025 |
| CC license |
|
