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All Journal International Journal of Electrical and Computer Engineering International Journal of Engineering, Science and Information Technology
Muneer Basha Shaik
Independent Researcher
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Quantum-AI cybersecurity threat detection framework: A hybrid architecture for enterprise security operations Muneer Basha Shaik
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2672-2679

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.
Enterprise Copilot Architecture for Legacy Systems: A Framework for Extending AI Capabilities Across Mission-Critical Enterprise Platforms Muneer Basha Shaik
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.2063

Abstract

Enterprise AI adoption is increasingly constrained by the difficulty of integrating AI capabilities with legacy platforms that continue to support critical business operations. Mainframe environments, SAP deployments, and proprietary enterprise applications contain established business logic, transactional data, and operational knowledge, but often lack standardized interfaces for direct interaction with modern AI systems. This article develops an Enterprise Copilot Architecture for Legacy Systems that enables AI copilot services to interact with legacy applications without requiring their replacement. The proposed architecture introduces an Intelligence Integration Layer that combines layered integration, service-oriented and API-driven communication, event-driven processing, agent-based intelligence, human-in-the-loop governance, and security-by-design. The approach further incorporates hybrid processing separation, multi-layer caching, context-aware retrieval, containerized deployment, and adaptive compute scaling to reduce processing overhead and minimize disruption to legacy platforms. The architectural analysis indicates that the proposed mechanisms provide a structured pathway to connect AI services to heterogeneous legacy environments while maintaining controlled access to enterprise data and business functions. The architecture also supports separating AI processing from core transactional workloads, allowing computational demands to be managed without directly modifying legacy applications. Context-aware retrieval facilitates access to institutional knowledge embedded in legacy systems, while governance mechanisms maintain human oversight over AI-generated actions and recommendations. The resulting framework demonstrates how contemporary AI capabilities can be introduced incrementally into established enterprise environments while preserving existing business logic, operational continuity, security controls, and architectural sustainability. It therefore provides a non-invasive architectural approach for extending legacy enterprise systems with scalable and governable AI copilot capabilities
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