Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
Machine learning models face sophisticated cybersecurity threats from adversarial attacks that exploit fundamental vulnerabilities in AI systems. These attacks include carefully crafted adversarial examples that cause misclassification while appearing normal to humans, model poisoning that introduces backdoors through contaminated training data, and extraction attacks that reverseengineer proprietary models. Effective defense requires a multi-layered approach combining robust model design techniques such as adversarial training, defensive distillation, and gradient masking with runtime protection strategies, including input sanitization, anomaly detection, and ensemble methods. Organizations must complement these technical measures with rigorous operational protocols, including strict access controls, regular security audits, and comprehensive monitoring. As attackers grow more sophisticated, defense strategies must continually evolve through ongoing collaboration between cybersecurity and AI communities, with promising advances in certifiable robustness and integration with broader security frameworks showing potential for improved resilience....
Introduction: in the context of an unprecedented intensification and structural complication of cyber threats, which increasingly manifest as full-scale attacks on organizational entities across diverse economic clusters, the exigency of formulating and implementing conceptually sound and technologically advanced paradigms of information security management has become irrefutable. Objective: the principal objective of this scholarly inquiry is the identification and systematic structuring of prevailing trends, as well as the analytical explication of the discursive features characterizing the implementation of innovative approaches to information security within the corporate domain. Method: the methodological framework is grounded in a descriptive-analytical model, incorporating elements of methodological pluralism—particularly the confluence of deductive theoretical analysis of security governance paradigms and empirical reflection on secondary data pertinent to the state and specificities of such implementation. Results: the findings substantiate the premise that the persistent escalation in the complexity of cyber threats precipitates substantial reputational, economic, and operational risks, thereby compelling organizations to recalibrate their strategic posture towards integrative models of information security governance. The most adaptive to the volatile threat landscape are risk-based and holistic approaches. Moreover, regulatory transformations within the European legal framework concerning personal data protection function as a significant catalyst in the strategic reconfiguration of information security imperatives. Conclusions: the practical significance of this study lies in the critical generalization and systematization of the tendencies that shape the emerging epistemology of information security management in contemporary organizational structures....
Increasing technological complexity of cyber threats creates a major challenge between securing data privacy and maintaining potent cybersecurity practices. The paper examines privacy-protecting security methods in cybersecurity by detailing organizational approaches to defend private information throughout the cyber threat detection and mitigation process. Organizations need to establish the appropriate levels of data security because implementations that limit privacy too much threaten their security capabilities but weak protection measures create vulnerabilities to data breaches. The research implements Cybersecurity: Suspicious Web Threat Interactions data to examine actual cyber threats which comprise phishing attacks and malware and unauthorized access attempts. The effectiveness of data protection approaches including encryption and differential privacy together with homomorphic encryption and federated learning and anonymization solutions gets tested for their ability to secure confidential information throughout cybersecurity operations. The research investigates threat detection accuracy together with computational efficiency and GDPR and CCPA compliance effects when using these techniques. Results demonstrate that security frameworks gain significant improvements from privacy-preserving systems because these systems decrease breach threats and meet all regulatory compliance requirements. The main limiting factors for these privacy-preserving methods consist of excessive computational requirements as well as adversarial threat vectors and the detection versus protection trade-offs that need improvement. This paper presents strategic guidance about privacy-aware cybersecurity models which optimize security capabilities together with data protected information. This research investigates cybersecurity and privacypreserving methods to assist the development of ethical systems meeting regulatory standards which protect users from advancing cyber threats through privacy-protected mechanisms....
This article examines the evolving security landscape for Generative Artificial Intelligence (GenAI) systems as they become increasingly integrated across critical sectors including healthcare, finance, and transportation. The proliferation of these technologies creates both transformative opportunities and significant security challenges that require specialized approaches. It explores key security vulnerabilities unique to GenAI implementations, including data protection vulnerabilities, access control complexities, data anonymization gaps, model integrity risks, monitoring challenges, intellectual property concerns, and regulatory compliance issues. Building upon current research, the article presents a comprehensive security framework encompassing data protection strategies, access control mechanisms, model security approaches, network security architectures, monitoring frameworks, compliance guidelines, incident response methodologies, and zero trust principles. Organizations implementing these strategies demonstrate substantially improved security outcomes, including faster threat detection, reduced breach incidents, and enhanced resilience against emerging attack vectors. It underscores the necessity for purpose-built security approaches that address the unique characteristics of GenAI systems, requiring close collaboration between industry stakeholders, policymakers, and security practitioners to establish robust defensive frameworks while enabling continued innovation....
Insider threats pose significant challenges to organizations, seriously endangering information security and privacy protection. These threats arise when employees with legitimate access to systems and databases misuse their privileges. Such individuals may alter, delete, or insert data into datasets, sell customer or client email addresses, leak strategic company plans, or transfer industrial and intellectual property information. These actions can severely damage a company’s reputation, result in revenue losses and loss of competitive advantage, and, in extreme cases, lead to bankruptcy. This study presents a novel solution that examines how organizational factors such as job satisfaction and security, organizational support, attachment, commitment, involvement in information security, and organizational norms influence employees’ attitudes and intentions, thereby mitigating insider threats. A key strength of this research is its integration of two foundational theories: the Social Bond Theory (SBT) and the Theory of Planned Behavior (TPB). The results reveal that job satisfaction and security, affective and normative commitment, information security training, and personal norms all contribute to reducing insider threats. Furthermore, the findings indicate that employees’ attitudes, perceived behavioral control, and subjective norms significantly influence their intentions to mitigate insider threats. However, organizational support and continuance commitment were not found to have a significant impact....
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