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<p>The rapid evolution of artificial intelligence (AI)–driven surveillance systems has significantly enhanced real-time monitoring, automated threat detection, and intelligent decision-making across smart cities and critical infrastructure environments. However, the sensitive nature of surveillance data and the growing capabilities of quantum computing pose serious threats to the cryptographic foundations that currently secure these systems. Traditional encryption techniques, widely used for data protection, are increasingly vulnerable to quantum-enabled attacks, creating an urgent need for future-resilient security solutions. This paper presents a comprehensive post-quantum security architecture specifically designed for AI-based surveillance systems to safeguard data confidentiality, integrity, and secure collaboration in quantum adversarial scenarios. The proposed framework integrates quantum-resistant cryptographic algorithms, secure key management mechanisms, and AI-aware encryption workflows to protect surveillance data throughout acquisition, transmission, storage, and inter-agency sharing. Unlike conventional approaches, the architecture adopts a co-design strategy that aligns cryptographic protection with AI inference pipelines, ensuring minimal performance degradation and real-time operational feasibility. The proposed PQ AI-aware architecture delivers quantum-safe surveillance with 0.64&#xa0;ms/MB encryption, 0.48&#xa0;ms/frame crypto overhead, and 19.4&#xa0;ms end-to-end latency, while achieving 9.6&#xa0;ms session setup (vs. 16.9&#xa0;ms full-PQC and 41.6&#xa0;ms QKD) and sustaining 874 Mbps throughput with 60 edge streams, outperforming existing PQC/QKD baselines in real-time deployability. The analysis demonstrates that the proposed architecture significantly enhances resilience against emerging quantum cyber threats while maintaining the performance requirements of modern AI-driven surveillance systems.</p>

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A post-quantum security architecture for AI-driven surveillance systems: ensuring data protection in the quantum computing era

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Prashant Kumar Shukla,
  • Monalisa Hati

摘要

The rapid evolution of artificial intelligence (AI)–driven surveillance systems has significantly enhanced real-time monitoring, automated threat detection, and intelligent decision-making across smart cities and critical infrastructure environments. However, the sensitive nature of surveillance data and the growing capabilities of quantum computing pose serious threats to the cryptographic foundations that currently secure these systems. Traditional encryption techniques, widely used for data protection, are increasingly vulnerable to quantum-enabled attacks, creating an urgent need for future-resilient security solutions. This paper presents a comprehensive post-quantum security architecture specifically designed for AI-based surveillance systems to safeguard data confidentiality, integrity, and secure collaboration in quantum adversarial scenarios. The proposed framework integrates quantum-resistant cryptographic algorithms, secure key management mechanisms, and AI-aware encryption workflows to protect surveillance data throughout acquisition, transmission, storage, and inter-agency sharing. Unlike conventional approaches, the architecture adopts a co-design strategy that aligns cryptographic protection with AI inference pipelines, ensuring minimal performance degradation and real-time operational feasibility. The proposed PQ AI-aware architecture delivers quantum-safe surveillance with 0.64 ms/MB encryption, 0.48 ms/frame crypto overhead, and 19.4 ms end-to-end latency, while achieving 9.6 ms session setup (vs. 16.9 ms full-PQC and 41.6 ms QKD) and sustaining 874 Mbps throughput with 60 edge streams, outperforming existing PQC/QKD baselines in real-time deployability. The analysis demonstrates that the proposed architecture significantly enhances resilience against emerging quantum cyber threats while maintaining the performance requirements of modern AI-driven surveillance systems.