Opportunities and Challenges in AI-Driven Cybersecurity: A Systematic Literature - Shahwali Shahidi; Farid Ahmad Darmel; Safiullah Jalalzai; Ghulam Ali Amiri (Yayasan Adra Karima Hubbi, 2024)
Background. The need for more sophisticated security strategies has become apparent as the number of cyber threats grows. AI is one framework that has been shown to boost security by providing advanced threat detection and response capabilities. Nonetheless, AI integration introduces inherently ethical and privacy-related concerns. Purpose. This research examines the AI implementation factors influencing the overall performance of the AI for cybersecurity and data privacy in both critical infrastructures and financial services. Method. This research derives its data from the extensive literature published from 2019 to 2024 in notable databases such as IEEE, Science Direct, MDPI, and Wiley Library, with more than 300 records. This analysis examined, with the help of artificial intelligence tools, the patterns and recurrent problems about the place of AI in cybersecurity, setting sights on the present challenges in the domains of intrusion detection and mitigation. Results. The results indicate that better threat detection in industry is enabled by AI. However, disadvantages of bias, the need for privacy, and suboptimal data management are evident, necessitating the need for stronger machine and human-readable regulations. Conclusion. Although AI strengthens security in an age of cyber-insecurity, its shortcomings point to the need for further development. Post-quantitative encryption palliatives and integration models will be effectively handled as cybersecurity-harming threats evolve.