AI-Driven Cryptographic Framework for Smart Industrial Cloud Security

Industrial systems, nowadays, are rapidly advancing with Industry 4.0 to encompass IoT, automation, and cloud computing in Cloud-based Industrial Control Systems. However, such increased interconnectivity and reliance on data opens up vulnerabilities whereby unauthorized access and cyberattacks can exploit these systems with data breaches. This study aims at the creation of a robust security solution in order to address these vulnerabilities with the integration of multi-layered attribute-based authentication, advanced cryptographic techniques, and AI-powered threat detection. The primary objectives of enhancing privacy protection, increasing efficiency, and ensuring safe data sharing in dynamic industrial environments. The proposed framework makes use of attribute-based encryption for adaptable access management, uses multi-tier encryption design to have robust data safeguarding, and trains AI models for the detection of threats in real-time. It combines symmetric, asymmetric, and homomorphic encryption with AI-driven anomaly identification to counter the risks emerging from such new vulnerabilities. Performance analysis shows that the suggested AI-driven multi-layered encryption framework outperforms prior cloud-based ICS techniques in terms of scalability and resource optimisation, achieving 95.4% privacy preservation, 10.1 ms authentication speed, 520.7 operations/sec encryption throughput, and 93.8% threat detection accuracy. The results illustrate that the AI-assisted multi-layered framework will provide an integrated solution securing industrial control systems by ensuring data integrity, confidentiality, and operational efficiency.