The rapid evolution of the digital age has transformed data into a critical asset, creating opportunities and challenges for modern organizations as sensitive information becomes a high-value target for online threats with significant financial and societal implications. This paper explores the vital role of Big Data Analytics (BDA) in enhancing digital resilience, proposing a conceptual framework rooted in a systemic literature review and thematic analysis. Despite advances in AI-driven systems utilizing big data technologies like hybrid intelligence systems and User and Entity Behavior Analytics (UEBA), technical and organizational barriers impede seamless integration. Our research examines how BDA can improve predictive, detective, preventative, and responsive security measures, particularly among SMEs facing unique challenges and being understudied. The study aims to propose strategies to overcome these challenges and efficiently harness AI-driven big data technologies. Grounded in literature synthesis (e.g., Samtani et al., 2023) and inspired by PRISMA (Moher et al., 2009) review methodology and narratives of Cram et al. (2019), our proposed framework integrates big data technologies into organizational strategies. It emphasizes risk assessment and adaptive mechanisms to enhance resilience. The analysis captures themes such as the role of machine learning and deep learning in security, the challenges of AI adoption, and the impacts of digital threats. The integration of AI and big data-driven methods with traditional tools like Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) is emphasized to enhance real-time detection within security frameworks. The proposed seven-step BDA integration framework monitors the data lifecycle, covering every aspect of data management from collection to threat detection and coping response. This proactive security posture incorporates continuous learning and adaptation through a feedback loop, transforming organizational measures from reactive to anticipatory strategies. However, the real-world integration process faces challenges, including organizational barriers like lack of expertise, resistance to change, insufficient financial incentives, limited security awareness, and ethical regulatory constraints. Technical barriers involve training data quality issues, computational limits, suboptimal model accuracy, and difficulties in developing adaptable algorithms. To tackle some of these challenges, security frameworks could evolve into resilient models guided by Zero Trust Architecture and human-in-the-loop systems. However, further research is needed to uncover the nuances of this approach and develop comprehensive coping responses. Our findings benefit academics and practitioners by enhancing proactive, data-driven protocols, synthesizing key big data algorithms for improved security surveillance, and offering a forward-looking perspective. This research expands theoretical frontiers, providing a holistic understanding of challenges in adopting big data-driven security measures within organizations. The proposed framework can be further developed for actionable solutions and empirical testing.