Cyber Resilience Against AI-Augmented Advanced Persistent Threats: An Adaptive Detection, Containment, and Recovery Framework for Critical Systems

  • Authors

    • Santosh Kumar Jadala Cyber Security & Business Analysis Specialist, Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-V9I2P102

    Published 04-08-2026

  • Cyber Resilience, Advanced Persistent Threats, Artificial Intelligence, Critical Infrastructure, Adaptive Threat Detection, Threat Containment, Secure Recovery, Zero Trust Security

    Issue

    Vol. 9 No. 2 (2026)

    Section

    Articles

    How to Cite

    [1]
    S. K. Jadala, “Cyber Resilience Against AI-Augmented Advanced Persistent Threats: An Adaptive Detection, Containment, and Recovery Framework for Critical Systems”, IJADSMC, vol. 9, no. 2, pp. 15–61, Apr. 2026, doi: 10.67228/30713498/IJADSMC-V9I2P102.

  • Abstract

    The growing use of artificial intelligence in offensive cyber operations is changing the speed, precision, and adaptability of advanced persistent threats, creating serious security concerns for critical systems. AI-assisted reconnaissance, targeted social engineering, adaptive malware, automated lateral movement, and evasive command-and-control techniques can reduce the effectiveness of conventional security measures that depend heavily on static rules and predefined indicators. This study proposes an adaptive cyber resilience framework for detecting, containing, and recovering from AI-augmented advanced persistent threats in critical environments. The framework combines continuous asset and identity awareness, behavioral threat detection, dynamic risk assessment, risk-based containment, operational continuity, secure recovery, and post-incident learning. Zero Trust principles, threat intelligence, and human oversight are incorporated as supporting controls across the framework. The study also develops a threat model that maps AI-enhanced adversarial capabilities to major stages of the APT lifecycle and identifies security controls for limiting persistence, privilege escalation, lateral movement, command and control, and operational impact. Framework effectiveness is assessed through resilience-oriented measures, including detection latency, containment time, attack propagation, service availability, and recovery performance. The proposed approach shifts attention from preventing every intrusion to limiting attacker progress, preserving essential functions, and restoring trusted operations following compromise. The framework provides a structured basis for strengthening cyber resilience in critical infrastructure and other high-consequence systems exposed to increasingly adaptive threat actors.

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