AI-Enabled Resilience: Designing Secure and Adaptive Systems for Next-Generation Digital Enterprises
DOI:
https://doi.org/10.15662/IJEETR.2026.0801021Keywords:
Artificial Intelligence, Cybersecurity, Digital Resilience, Adaptive Systems, Machine Learning, Threat Detection, Self-Healing Systems, Enterprise Security, Risk Management, Intelligent AutomationAbstract
The rapid evolution of digital enterprises has intensified the need for systems that are not only secure but also resilient and adaptive to dynamic threats and disruptions. Artificial Intelligence (AI) plays a pivotal role in enabling such resilience by enhancing predictive capabilities, automating responses, and improving system robustness. This paper explores how AI-driven architectures contribute to resilience by integrating cybersecurity, real-time analytics, and adaptive learning mechanisms. It examines the intersection of AI with secure system design, focusing on threat detection, anomaly identification, and self-healing infrastructures. The study highlights how machine learning models can proactively mitigate risks, reduce downtime, and ensure business continuity in complex digital ecosystems. Furthermore, it addresses challenges such as data privacy, algorithmic bias, and system vulnerabilities. By synthesizing current research and proposing a structured methodology, this paper aims to provide a comprehensive framework for designing next-generation resilient digital systems. The findings emphasize the importance of combining AI technologies with robust governance and security strategies to build adaptive enterprises capable of thriving in uncertain and rapidly changing environments
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