Towards an AI-Powered Cyber Resilience Model: A Systematic Evaluation of Frameworks Against Emerging Threats

This study presents a Systematic Literature Review of cyber resilience frameworks against emerging threats, published between 2010 and 2025. While numerous frameworks exist, their ability to anticipate, withstand, and evolve in the face of sophisticated attacks remains uncertain. The study maps frameworks across nine resilience goals, namely Identify, Protect, Detect, Respond, Recover, Govern, Anticipate, Withstand, and Evolve, creating a goal-wise evidence matrix and quantification. Using the PRISMA methodology, 11,027 publications were identified, of which 55 studies met the inclusion criteria for critical analysis. The results indicate that most frameworks accentuate Protect and Detect functions at 87.72 per cent, whereas Govern at 17.54 per cent, Withstand at 28.07 per cent, and Evolve at 24.56 per cent remain under-represented. Only 45.61 per cent of frameworks explicitly address emerging threats such as Artificial Intelligence-driven or Internet of Things-based attacks. Strengths observed include situational awareness, Artificial Intelligence and Machine Learning integration, dynamic defence mechanism, Blockchain, and adoption of Zero Trust principles. The key weaknesses lie in the undervalued cyber resilience goals, namely Govern, Withstand, and Evolve, low empirical validation, and a narrow scope in addressing emerging threats, which highlight gaps that limit resilience against sophisticated attacks. Based on these findings, an evidence-informed Artificial Intelligence-powered cyber resilience model is proposed that privileges adaptability and future proofing. This review highlights the urgent need for cyber resilience frameworks to expand beyond reactive measures and to embed forward-looking resilience capabilities.

@article{Jahajeeah-Suntoo2026,
  title     = {Towards an AI-Powered Cyber Resilience Model: A Systematic Evaluation of Frameworks Against Emerging Threats},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Chhaya Jahajeeah-Suntoo and Sheeba Armoogum},
  doi       = {10.14569/IJACSA.2026.0170106},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170106}
}