The emergence of quantum computing threatens widely deployed public-key cryptographic systems such as RSA and ECC, creating an urgent need for post-quantum cryptographic (PQC) migration. This research proposes a risk-aware cryptographic agility framework that dynamically selects appropriate classical, hybrid, or PQC algorithms based on security requirements, computational resources, network conditions, and performance constraints. The study will evaluate standardized and emerging PQC schemes, including ML-KEM, ML-DSA, SLH-DSA, and HQC, through comprehensive security and performance benchmarking. Experimental results will be used to develop an adaptive decision model for selecting optimal cryptographic configurations, aiming to minimize migration overhead while maintaining appropriate security levels across diverse deployment environments.
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Future research can extend this work by developing AI-driven cryptographic agility capable of continuously adapting algorithm selection to emerging threats and changing system conditions. The framework can also be evaluated in real-world environments, including IoT, cloud infrastructure, mobile networks, and TLS based applications. Further studies may investigate side-channel resistance, fault attacks, energy efficiency, and hardware acceleration of PQC algorithms. As the post-quantum standardization landscape evolves, newly standardized algorithms can be incorporated into the framework. Finally, formal security analysis and large-scale deployment studies can validate the proposed approach and establish practical guidelines for secure, cost-effective, and scalable post-quantum migration.
Skills: Post-Quantum Cryptography, Cybersecurity, Cryptographic Risk Assessment, Algorithm Optimization.
Technical: Python/C++, PQC Benchmarking, Network Security, Data Analysis & Research.
