This research examines the role of artificial intelligence (AI) in enhancing cybersecurity, with a specific focus on its integration into encryption, cloud security, digital identity management, and financial asset protection. The primary objective is to evaluate how AI techniques, including machine learning, deep learning, natural language processing, and blockchain-assisted models, enhance real-time threat detection and secure data processing, while also addressing governance and ethical challenges. A systematic literature review methodology was employed, screening 1,248 records from five major databases, of which 64 studies met the inclusion criteria. Results indicate that deep learning models achieved detection accuracies exceeding 90%, while anomaly detection in cloud environments reduced false positives by nearly 25% compared with rule-based methods. Nonetheless, adversarial AI models exposed vulnerabilities, and homomorphic encryption integration faced scalability issues, with error rates in computational performance ranging from 8% to 12% across test environments. The study concludes that although AI offers transformative benefits for digital safeguarding, significant challenges remain, including those related to ethics, bias, resource intensity, and regulatory harmonisation, underscoring the need for scalable and inclusive frameworks.
Adeola et al. (Fri,) studied this question.
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