Explainable Artificial Intelligence has emerged as one of the most critical research directions in contemporary artificial intelligence, driven by the increasing deployment of complex machine learning systems in high-stakes social, economic, and institutional domains. As AI models become more powerful, opaque, and autonomous, the inability of stakeholders to understand, interpret, and contest algorithmic decisions raises profound technical, ethical, and governance-related concerns. This research article provides a comprehensive and theoretically grounded exploration of Explainable Artificial Intelligence, drawing strictly from established scholarly works that define, categorize, and critique explanation methods for black-box models, human-centered interpretability frameworks, ethical implications, and applied use cases such as credit scoring, cybersecurity, and risk management. Through extensive conceptual elaboration, the article examines how explanation techniques function not merely as technical artifacts but as socio-technical instruments that mediate trust, accountability, fairness, and moral responsibility. The methodological approach is qualitative and analytical, synthesizing taxonomies, philosophical perspectives, and applied frameworks to construct an integrative understanding of XAI as both a scientific and ethical endeavor. The results highlight recurring patterns across domains, demonstrating that explanation quality is deeply contingent on context, user expertise, and institutional purpose. The discussion critically evaluates limitations, including cognitive overload, explanation misuse, and the tension between model performance and interpretability, while outlining future research trajectories aimed at responsible AI governance. The study concludes that Explainable Artificial Intelligence is not an optional enhancement but a foundational requirement for aligning artificial intelligence systems with human values, democratic oversight, and sustainable technological progress.

This article presents an integrative theoretical framework for Adaptive DevSecOps that tightly couples threat intelligence, dynamic data management for continuous retraining, privacy-by-design, and containerized CI/CD security to enable automated, risk-aware security decisions before code reaches production. The work synthesizes multiple strands of recent scholarship and practitioner guidance—threat intelligence mapping, blockchain-enabled intelligence lifecycle support, continuous retraining pipelines, DevOps metrics and project alignment, container security techniques, regulatory privacy constraints, and event-consistency tradeoffs in distributed systems—into a single, coherent model for securing modern cloud-native software delivery. The article first identifies gaps in contemporary DevSecOps practice, then defines methodological constructs for integrating automated threat feeds, retraining controls, and privacy-preserving data governance within CI/CD workflows. A detailed, text-based methodology describes architecture patterns, data flows, decision points, and governance controls; results are presented as conceptual outcomes and expected operational benefits, grounded in the referenced literature. The discussion examines theoretical implications, counter-arguments, limitations (including data quality, false positives, and compliance complexity), and avenues for future empirical validation. This contribution intends to guide researchers and practitioners toward provable, auditable, and privacy-respecting automation of security actions in the software delivery lifecycle.

Background: Delay-Tolerant Networks (DTNs) are engineered to function in environments with intermittent connectivity, making them vital for applications ranging from wildlife tracking to disaster response. However, their defining store-carry-forward nature exposes them to unique security vulnerabilities, such as black hole and gray hole attacks, which are difficult to counter with traditional security protocols. Existing DTN routing mechanisms, while efficient in message delivery, often lack robust, integrated security layers, creating a critical need for novel defense strategies.

Methods: This paper introduces and evaluates a Geospatial Anomaly Detection (GAD) framework designed to enhance security in DTNs. Using The ONE (Opportunistic Network Environment) simulator, we modeled node mobility based on real-world map data from OpenStreetMap. We implemented the GAD framework on top of the MaxProp routing protocol [2]. The framework establishes normal behavior profiles by analyzing nodes' historical geospatial-temporal data. Anomalies are flagged when a node's movement significantly deviates from its established patterns, indicating a potential compromise or malicious intent. We evaluated the framework's effectiveness against a simulated black hole attack.

Results: The simulations demonstrate that the GAD framework is highly effective. It successfully identified over 94% of malicious nodes (True Positive Rate) while maintaining a False Positive Rate below 5%. Crucially, this security enhancement introduced minimal network overhead, with a negligible impact on key performance metrics such as packet delivery ratio and latency when compared to the baseline MaxProp protocol operating in a non-hostile environment.

Conclusion: The findings confirm that leveraging geospatial data for anomaly detection is a viable and potent strategy for securing DTNs. The proposed GAD framework offers a practical and resource-efficient security layer that can be integrated with existing routing protocols. This approach represents a significant step toward building more resilient and trustworthy communication systems for challenged network environments.

As cloud computing evolves to support highly heterogeneous workloads, the adoption of Field-Programmable Gate Arrays (FPGAs) in multi-tenant cloud platforms has surged. This shift promises unparalleled performance benefits, especially for domains such as machine learning inference acceleration. However, the unique hardware-sharing model inherent to multi-tenant FPGAs also exposes novel and severe security risks — ranging from covert communication via wiring-level leakage to cross-tenant side-channel attacks exploiting power, timing, and electromagnetic emanations. In this paper, we present a comprehensive, theory-driven analysis of these threats, survey existing empirical evidence, and propose a unified security framework for future cloud FPGA deployments. We synthesize prior findings regarding wiring-based information leakage (Giechaskiel, Rasmussen & Eguro, 2018), cross-VM leakage in FPGA‑accelerated cloud environments (Giechaskiel, Tian & Szefer, 2021), power-analysis and ring‑oscillator based side-channel attacks (Glamočanin et al., 2020; Gravellier et al., 2019), bitstream-level fault inducement via voltage drop (Gnad, Oboril & Tahoori, 2017; Gnad et al., 2018), and isolation methodologies to shield non‑trusted IPs (Hategekimana et al., 2016–2018). On top of these technical vulnerabilities, we discuss the need for higher-level tenant isolation, attribute‑based access control, and zero‑trust policies in multi‑tenant deployments (e.g., Almorsy, Grundy & Ibrahim, 2012; Kamara & Lauter, 2010; Hariharan, 2025). Our framework advocates a layered defense strategy combining hardware partitioning, rigorous bitstream verification, runtime side‑channel monitoring, and tenant‑aware policy enforcement. Through detailed theoretical analysis, we demonstrate that such a unified approach can — in principle — mitigate the majority of known FPGA-based threats. We conclude by outlining research directions and standardization efforts essential for realizing secure, scalable, and trustworthy FPGA-accelerated cloud services.

Background: As organizations migrate to cloud-centric workflows, the web browser has emerged as the primary interface for enterprise data access. Traditional perimeter-based security models are increasingly insufficient against sophisticated threats targeting the application layer. This paper explores the efficacy of Secure Enterprise Browsers (SEB) as a critical enforcement point within Zero Trust Architectures (ZTA), specifically within the high-stakes context of the financial services industry.

Methods: We employed a Fuzzy Multi-Criteria Decision Making (MCDM) approach to evaluate security technologies. Drawing upon methodologies typically used for personnel selection, we adapted fuzzy logic algorithms to assess three distinct remote access mechanisms: Virtual Private Networks (VPNs), Virtual Desktop Infrastructure (VDI), and Secure Enterprise Browsers. The evaluation criteria included data leak prevention (DLP) capabilities, user experience (UX), deployment cost, and maturity alignment with NIST 800-207 standards.

Results: The Fuzzy MCDM analysis reveals that while VDI provides high security, it suffers from significant cost and UX penalties. Secure Enterprise Browsers demonstrated the highest composite score for balance between security efficacy and operational agility. Specifically, SEBs reduced the theoretical attack surface by 40% compared to standard browsers patched with extensions.

Conclusion: The findings suggest that the Secure Enterprise Browser is not merely a tool but a strategic imperative. By decoupling security from the underlying device and embedding it into the browser, organizations can achieve a higher maturity level in Zero Trust, particularly in sectors where data privacy and financial stability are paramount.