1 LL.M, University of the Pacific, McGeorge School of Law, California, USA.
2 Department of Computer Science, University of North Texas, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 310-327
Article DOI: 10.30574/wjarr.2024.24.3.3695
Received on 26 October 2024; revised on 02 December 2024; accepted on 04 December 2024
The proliferation of sophisticated cyber threats has compelled organizations to adopt advanced solutions for safeguarding sensitive data and mitigating enterprise risks. Artificial intelligence (AI)-driven cybersecurity systems have emerged as transformative tools in this endeavor, leveraging machine learning and predictive analytics to detect, respond to, and prevent cyberattacks. However, implementing these systems requires organizations to balance innovation with compliance, particularly in light of stringent global privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This paper examines strategic approaches for integrating AI-based cybersecurity frameworks into enterprise risk management. Key areas of focus include real-time threat detection, anomaly identification, and automated incident response. AI’s ability to analyse vast datasets and identify patterns enables organizations to proactively address vulnerabilities, minimize downtime, and protect critical assets. Furthermore, the paper explores how organizations can align these frameworks with privacy-by-design principles to ensure compliance with data protection laws while fostering consumer trust. The challenges of adopting AI-driven cybersecurity systems are also addressed, including ethical concerns related to data use, algorithmic transparency, and the risks of over-reliance on automated systems. Case studies from leading industries demonstrate how organizations have successfully implemented these systems to enhance resilience and maintain competitive advantage. By adopting strategic management practices, including robust governance models and continuous monitoring, organizations can optimize the effectiveness of AI-driven cybersecurity systems. This paper concludes that such systems, when integrated thoughtfully, not only strengthen enterprise risk mitigation but also support compliance, innovation, and long-term organizational growth.
AI-Driven Cybersecurity; Risk Mitigation; Data Privacy; GDPR Compliance; Threat Detection; Enterprise Strategy
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Geraldine O Mbah and Achudume Nkechi Evelyn. AI-powered cybersecurity: Strategic approaches to mitigate risk and safeguard data privacy. World Journal of Advanced Research and Reviews, 2024, 24(3), 310-327. Article DOI: https://doi.org/10.30574/wjarr.2024.24.3.3695