Analysis of Perception and Readiness for Preemptive Cybersecurity  Implementation as a Cyber Threat Prevention Strategy

Trija Anjelia; Mambang Mambang

doi:10.30871/jaic.v10i5.13351

Authors

Trija Anjelia Universitas Sari Mulia
Mambang Mambang Universitas Sari Mulia

DOI:

https://doi.org/10.30871/jaic.v10i5.13351

Keywords:

Artificial Intelligence, Cyber Threat Intelligence, Preemptive Cybersecurity, Threat Hunting, Cybersecurity Readiness

Abstract

The increasing complexity of cyber threats requires organizations and individuals to adopt proactive cybersecurity strategies. This study aims to analyze perceptions and readiness toward the implementation of Preemptive Cybersecurity as a cyber threat prevention strategy. A quantitative approach was employed using a cross-sectional survey distributed through Google Forms. A total of 45 valid responses were collected and analyzed using descriptive statistical analysis. The study examined five dimensions, namely Cyber Threat Intelligence (CTI), Threat Hunting (TH), Artificial Intelligence (AI), Vulnerability Assessment (VA), and Readiness (RD). The findings indicate that respondents generally have positive perceptions toward proactive cybersecurity practices and demonstrate a high level of readiness to support preventive cybersecurity initiatives. The readiness dimension achieved the highest mean score, highlighting the importance of anomaly reporting, organizational transparency, and continuously updated security systems. Overall, the results suggest that Preemptive Cybersecurity is positively perceived as an effective approach for improving cyber resilience and preventing cyber threats.

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References

[1] B. Zhang, Y. Gao, B. Kuang, C. Yu, A. Fu, and W. Susilo, “A Survey on Advanced Persistent Threat Detection: A Unified Framework, Challenges, and Countermeasures,” ACM Comput. Surv., vol. 57, no. 3, pp. 62:2-62:36, 2024, doi: 10.1145/3700749.

[2] S. Berrios, D. Leiva, B. Olivares, H. Allende-Cid, and P. Hermosilla, “Systematic Review: Malware Detection and Classification in Cybersecurity,” Appl. Sci., vol. 15, no. 14, pp. 1–32, 2025, doi: 10.3390/app15147747.

[3] P. Radanliev, “Cyber diplomacy: defining the opportunities for cybersecurity and risks from Artificial Intelligence, IoT, Blockchains, and Quantum Computing,” J. Cyber Secur. Technol., vol. 9, no. 1, pp. 28–78, 2025, doi: 10.1080/23742917.2024.2312671.

[4] S. AboulEla, N. Ibrahim, S. Shehmir, A. Yadav, and R. Kashef, “Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems,” AI, vol. 5, no. 2, pp. 704–732, 2024, doi: 10.3390/ai5020037.

[5] S. Saeed, S. A. Altamimi, N. A. Alkayyal, E. Alshehri, and D. A. Alabbad, “Digital Transformation and Cybersecurity Challenges for Businesses Resilience: Issues and Recommendations,” Sensors, vol. 23, no. 15, pp. 1–20, 2023, doi: 10.3390/s23156666.

[6] A. I. Jony and S. A. Hamim, “Navigating the Cyber Threat Landscape: A Comprehensive Analysis of Attacks and Security in the Digital Age,” J. Inf. Technol. Cyber Secur., vol. 1, no. 2, pp. 53–67, 2023, doi: 10.30996/jitcs.9715.

[7] P. Nespoli, S. B. Molina, P. Beltran-Lopez, and F. G. Marmol, “Tackling Cyberattacks through AI-based Reactive Systems: A Holistic Review and Future Vision,” IEEE Commun. Surv. Tutorials, vol. 28, pp. 1557–1596, 2025, doi: 10.1109/COMST.2025.3583868.

[8] N. Kshetri, “Transforming cybersecurity with agentic AI to combat emerging cyber threats,” Telecomm. Policy, vol. 49, no. 6, p. 102976, 2025, doi: 10.1016/j.telpol.2025.102976.

[9] A. Tanikonda, B. K. Pandey, S. R. Peddinti, and S. R. Katragadda, “Advanced AI-Driven Cybersecurity Solutions for Proactive Threat Detection and Response in Complex Ecosystems,” SSRN Electron. J., vol. 3, no. 1, pp. 196–217, 2025, doi: 10.2139/ssrn.5102358.

[10] S. Ryu, J. Kim, N. Park, and Y. Seo, “Preemptive prediction-based automated cyberattack framework modeling,” Symmetry (Basel)., vol. 13, no. 5, 2021, doi: 10.3390/sym13050793.

[11] Rakibul Hasan Chowdhury, Nayem Uddin Prince, Salman Mohammad Abdullah, and Labonno Akter Mim, “The role of predictive analytics in cybersecurity: Detecting and preventing threats,” World J. Adv. Res. Rev., vol. 23, no. 2, pp. 1615–1623, 2024, doi: 10.30574/wjarr.2024.23.2.2494.

[12] J. Yu, A. V. Shvetsov, and S. Hamood Alsamhi, “Leveraging Machine Learning for Cybersecurity Resilience in Industry 4.0: Challenges and Future Directions,” IEEE Access, vol. 12, no. October, pp. 159579–159596, 2024, doi: 10.1109/ACCESS.2024.3482987.

[13] S. Ainslie, D. Thompson, S. Maynard, and A. Ahmad, “Cyber-threat intelligence for security decision-making: A review and research agenda for practice,” Comput. Secur., vol. 132, p. 103352, 2023, doi: 10.1016/j.cose.2023.103352.

[14] S. Saeed, S. A. Suayyid, M. S. Al-Ghamdi, H. Al-Muhaisen, and A. M. Almuhaideb, “A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience,” Sensors, vol. 23, no. 16, pp. 1–27, 2023, doi: 10.3390/s23167273.

[15] N. Sun et al., “Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives,” IEEE Commun. Surv. Tutorials, vol. 25, no. 3, pp. 1748–1774, 2023, doi: 10.1109/COMST.2023.3273282.

[16] B. Nour, M. Pourzandi, and M. Debbabi, “A Survey on Threat Hunting in Enterprise Networks,” IEEE Commun. Surv. Tutorials, vol. 25, no. 4, pp. 2299–2324, 2023, doi: 10.1109/COMST.2023.3299519.

[17] S. R. Sindiramutty, “Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence,” J. Artic., 2023, doi: httpsdoi.org/10.48550/arXiv.2401.00286.

[18] B. Kayode, N. T. Adebola, S. Akerele, O. Fagbohun, C. Agbo, and O. Bantale, “Journal of Artificial Intelligence , Machine Learning and Data Science The State of AI-Driven Cybersecurity : Trends , Challenges and Opportunities,” URF Publ., vol. 3, no. 2, pp. 1–9, 2025, doi: doi.org/10.51219/JAIMLD/Bisola-Kayode/577 Journal.

[19] A. Mishra, “Ai-Powered Cybersecurity Framework for Secure Data Transmission in Iot Network,” Int. J. Adv. Eng. Manag., vol. 7, no. 3, pp. 05–13, 2025, doi: 10.35629/5252-07030513.

[20] K. Bennouk, N. Ait Aali, Y. El Bouzekri El Idrissi, B. Sebai, A. Z. Faroukhi, and D. Mahouachi, “A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies,” J. Cybersecurity Priv., vol. 4, no. 4, pp. 853–908, 2024, doi: 10.3390/jcp4040040.

[21] P. Cheimonidis and K. Rantos, “Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review,” Futur. Internet, vol. 15, no. 10, 2023, doi: 10.3390/fi15100324.

[22] A. Amulya, K. S. Swarup, and R. Ramanathan, “Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework,” ACM Trans. Cyber-Physical Syst., vol. 8, no. 4, 2024, doi: 10.1145/3661827.

[23] M. Neri, F. Niccolini, and L. Martino, “Organizational cybersecurity readiness in the ICT sector: a quanti-qualitative assessment,” Inf. Comput. Secur., vol. 32, no. 1, pp. 38–52, 2024, doi: 10.1108/ICS-05-2023-0084.

[24] Berlilana, T. Noparumpa, A. Ruangkanjanases, T. Hariguna, and Sarmini, “Organization benefit as an outcome of organizational security adoption: The role of cyber security readiness and technology readiness,” Sustain., vol. 13, no. 24, 2021, doi: 10.3390/su132413761.

[25] R. Agyemang, S. Furnell, and T. Muller, “A Context-Aware Cybersecurity Readiness Assessment Framework for Organisations in Developing and Emerging Environments,” Futur. Internet, vol. 18, no. 4, pp. 1–30, 2026, doi: 10.3390/fi18040178.

[26] Dimas Febriawan and Hizra Marisa, “Understanding Indonesia’s Cyber Security Policies: Opportunities and Challenges In The Digitalization Transformation Era,” JOELS J. Elect. Leadersh., vol. 5, no. 1, pp. 13–21, 2024, doi: 10.31849/joels.v5i1.15908.

[27] A. I. Saleh and M. D. Winata, “Indonesia’s Cyber Security Strategy: Problems and Challenges,” IJCAH, vol. 2023, no. Ijcah 2023, pp. 1675–1696, 2023, doi: 10.2991/978-2-38476-152-4_169.

[28] M. Neri, F. Niccolini, and R. Pugliese, “Assessing SMEs’ cybersecurity organizational readiness: Findings from an Italian survey,” Online J. Appl. Knowl. Manag., vol. 10, no. 2, pp. 1–22, 2022, doi: 10.36965/ojakm.2022.10(2)1-22.

[29] K. Hasan, “Quantitative Methods in Social Science Research: Systematic Review of Content Analysis, Survey and Experiment Methodologies,” SSRN Electron. J., 2024, doi: 10.2139/ssrn.4698175.

[30] W. Febriani, S. E. Prastya, B. Sabella, and F. D. Marleny, “Analysis of the Impact of Violent Content on Social Media on Adolescent Cyberpsychology Using Support Vector Machine and Random Forest,” J. Appl. Informatics Comput., vol. 10, no. 1, pp. 707–711, 2026, doi: https://doi.org/10.30871/jaic.v10i1.11415.