Quantum-AI cybersecurity threat detection framework: A hybrid architecture for enterprise security operations.

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Bibliographic Details
Title:	Quantum-AI cybersecurity threat detection framework: A hybrid architecture for enterprise security operations.
Authors:	Shaik, Muneer Basha reachme.muneer@gmail.com
Source:	International Journal of Electrical & Computer Engineering (2088-8708). Oct2026, Vol. 16 Issue 5, p2672-2679. 8p.
Subjects:	Quantum computing, Anomaly detection (Computer security), Hybrid computers (Computer architecture), Cryptography, Internet security
Abstract:	Enterprise security operations face computational bottlenecks that classical architectures cannot efficiently resolve at scale. This paper presents the quantum-AI cybersecurity threat detection framework (QACTDF), a hybrid architecture that combines quantum-enhanced optimization with AI-driven behavioral analytics for large-enterprise threat detection. The architecture employs a four-layer orchestration model in which an AI-governed routing policy selectively directs security workloads-including threat correlation, cryptographic verification, and anomaly classification-to quantum-assisted processors, while high-throughput tasks such as telemetry ingestion and alert triage are handled by classical infrastructure. Qualitatively evaluated against canonical security framework criteria-detection scope, integration feasibility, scalability, and sustainability-and supported by simulation-derived performance estimates, QACTDF addresses functional capability gaps in conventional security architectures. The framework additionally incorporates post-quantum cryptographic alignment in accordance with NIST FIPS 203 and provides a sequential adoption pathway for enterprise architects integrating quantum capabilities into existing security operations centers. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Enterprise security operations face computational bottlenecks that classical architectures cannot efficiently resolve at scale. This paper presents the quantum-AI cybersecurity threat detection framework (QACTDF), a hybrid architecture that combines quantum-enhanced optimization with AI-driven behavioral analytics for large-enterprise threat detection. The architecture employs a four-layer orchestration model in which an AI-governed routing policy selectively directs security workloads-including threat correlation, cryptographic verification, and anomaly classification-to quantum-assisted processors, while high-throughput tasks such as telemetry ingestion and alert triage are handled by classical infrastructure. Qualitatively evaluated against canonical security framework criteria-detection scope, integration feasibility, scalability, and sustainability-and supported by simulation-derived performance estimates, QACTDF addresses functional capability gaps in conventional security architectures. The framework additionally incorporates post-quantum cryptographic alignment in accordance with NIST FIPS 203 and provides a sequential adoption pathway for enterprise architects integrating quantum capabilities into existing security operations centers. [ABSTRACT FROM AUTHOR]
ISSN:	20888708
DOI:	10.11591/ijece.v16i5.pp2672-2679