Advanced Journal of Management, Humanity and Social Science

Abstract Introduction / Background: In the current digital era, organizations face increasingly sophisticated cyber threats that challenge conventional security mechanisms. The growing complexity and volume of cyber incidents demand innovative solutions capable of supporting rapid, accurate, and proactive decision-making.

Purpose / Objective: This study investigates the impact of Artificial Intelligence (AI)-based Decision Support Systems (DSS) on organizational cybersecurity and risk management. Specifically, it aims to examine how AI-driven DSS enhances threat detection, improves incident response, strengthens risk management, and contributes to organizational resilience. Additionally, the study explores challenges, limitations, and governance requirements associated with integrating AI-based DSS into organizational practices.

Methodology: A comprehensive literature review and conceptual analysis were conducted, drawing insights from recent empirical studies, frameworks, and case studies on AI applications in cybersecurity and risk management. The study identifies key constructs, including AI-DSS capabilities, cybersecurity effectiveness, risk management efficiency, governance mechanisms, and organizational readiness, and proposes a conceptual framework linking these constructs.

Results: Findings indicate that AI-based DSS significantly improves cybersecurity performance, enabling faster detection of threats, proactive mitigation, and reduced operational and strategic risk exposure. Furthermore, AI integration supports more informed and timely decision-making, enhancing overall organizational resilience. However, the effectiveness of AI-based DSS is influenced by data quality, human oversight, governance frameworks, and organizational readiness. Potential risks include adversarial attacks, model biases, and over-reliance on automated decision-making.

Conclusion: AI-based Decision Support Systems represent a strategic tool for strengthening cybersecurity and risk management in modern organizations. Successful implementation requires a holistic approach encompassing governance, human-in-the-loop oversight, infrastructure readiness, and continuous evaluation. By integrating AI responsibly, organizations can achieve enhanced security, better risk mitigation, and improved operational resilience.

Abstract Small and Medium-sized Enterprises (SMEs) represent the backbone of the global economy, yet they are disproportionately vulnerable to global economic fluctuations, supply chain disruptions, and sustainability pressures. In today’s uncertain environment, resilient supply chain management (RSCM) and the integration of sustainable practices have become strategic imperatives for SMEs striving to maintain competitiveness and continuity. This paper explores how SMEs can enhance resilience by diversifying suppliers, leveraging digital technologies, and strengthening collaborative networks while simultaneously adopting sustainability measures such as circular economy models, renewable energy integration, and ethical sourcing. The study emphasizes the dual role of resilience and sustainability as mutually reinforcing concepts: resilient practices ensure operational continuity during crises, while sustainable initiatives reduce long-term vulnerabilities and improve stakeholder trust. Case studies from SMEs across diverse industries demonstrate that adopting these strategies not only mitigates risks but also provides access to global markets, enhances brand reputation, and improves resource efficiency. However, SMEs face significant barriers, including limited financial resources, technological gaps, regulatory complexities, and knowledge deficits. Overcoming these challenges requires supportive policies, financial incentives, and capacity-building programs, alongside SME-driven innovation.