Predictive Analytics for Proactive Email Security Risk Management: A Systematic Review

In recent years, email has become an important communication tool for sharing private messages to crucial business message exchanges. However, its widespread use makes it a major target for cyber-attacks, including phishing, spam, and malware. These growing threats highlight the urgent need to investigate email security risk management to protect against attacks and maintain the integrity of communication systems. The study reviews the literature on the challenges of email security, risk management, and the role of predictive analysis in combating these threats. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), thirty-five (35) relevant peer-reviewed research articles were identified in various open research databases. This systematic literature review (SLR) also includes relevant case studies. The findings reveal that the integration of machine learning (ML), natural language processing (NLP), and real-time data analytics into email security frameworks improves threat detection and mitigation. Furthermore, these models often lack adaptability across languages and cultures. Additionally, they do not integrate well with human-centric security measures. Therefore, it is important to develop culturally adaptive predictive models, sector-specific solutions for industries such as finance and healthcare and incorporate behavioural analytics to enhance email threat detection and prevention. In other words, a comprehensive approach that combines technical advances with behavioural insights is crucial to strengthening email security and maintaining the integrity of global digital communications amid evolving cyber threats.