From AI security to ethical AI security: a comparative risk-mitigation framework for classical and hybrid AI governance

Abstract

Abstract As Artificial Intelligence (AI) systems evolve from classical to hybrid classical-quantum architectures, traditional notions of security—mainly centered on technical robustness—are no longer sufficient. This study aims to provide an integrated security ethics compliance framework that bridges technical and ethical dimensions across the AI lifecycle. By adopting a security ethics-by-design approach, the framework introduces mitigation measures in relation to key ethical principles capable of addressing emerging risks and considering AI governance needs in the initial AI design and development phases. This study proposes a novel framework, currently absent from the literature, to address security ethics challenges in both classical and hybrid systems. Key contributions include the integration of post-quantum and quantum cryptography, particularly homomorphic encryption, to ensure long-term privacy and security in hybrid AI. The framework also includes bias testing and explainable AI techniques to promote fairness and explainability, and to prevent safety-related vulnerabilities—such as algorithmic bias—from serving as vectors for malicious, discriminatory attacks. Ultimately, it provides a preliminary roadmap for embedding ethical security considerations throughout the lifecycle of classical and hybrid AI systems.

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