AI–Driven Cybersecurity Frameworks: Strengthening Resilience in Nuclear–Powered Data Centers

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ABSTRACT

The rising deployment of nuclear-powered data centers offers a sustainable and high-capacity solution to meet the ever-growing demand for computing power. However, these facilities present unique cybersecurity challenges due to their complex infrastructure and critical role in national security and global digital infrastructure. This paper explores AI-enabled cybersecurity solutions to enhance the resilience of nuclear-powered data centers against sophisticated cyber threats. By leveraging machine learning models, such as anomaly detection and reinforcement learning, the proposed framework is capable of real-time threat detection, automated incident response, and system vulnerability analysis. The study also incorporates a hybrid approach combining AI with traditional security measures, ensuring comprehensive protection against both known and emerging threats. Experimental results demonstrate that the AI-driven solutions achieve high detection accuracy, faster response times, and improved system resilience, thereby safeguarding the integrity and availability of these critical infrastructures. The findings indicate that AI-enabled cybersecurity can significantly enhance the overall security posture of nuclear-powered data centers, paving the way for their secure and reliable integration into the global data ecosystem.

Significance of the Study:

As nuclear-powered data centers emerge as sustainable solutions for high computing demands, ensuring their security is crucial. This study highlights the transformative potential of AI-driven cybersecurity in mitigating threats, improving resilience, and safeguarding critical infrastructures. By addressing challenges like emerging cyberattacks and system vulnerabilities, the proposed framework supports the reliable operation of these data centers, enabling their secure adoption. This research establishes a foundation for advanced AI applications, promoting a sustainable and secure global digital infrastructure.

Summary of the Study:

This study introduces an AI-driven cybersecurity framework tailored for nuclear-powered data centers, addressing their unique security challenges. By leveraging machine learning techniques like anomaly detection and reinforcement learning, the framework enables real-time threat detection, automated incident response, and vulnerability analysis. Experimental results demonstrate improved detection accuracy, faster response times, and enhanced resilience against sophisticated cyber threats. The hybrid approach integrates AI with traditional security measures, offering robust, multi-layered protection for these critical infrastructures, ensuring their secure integration into the global data ecosystem.