The growing dependence on engineering databases to store and manage critical industrial and operational data has significantly increased their exposure to evolving cybersecurity threats. To safeguard data confidentiality, integrity, and availability, structured cybersecurity risk assessment frameworks are essential. This study conducts a systematic review of 125 peer-reviewed articles using the PRISMA methodology to examine current risk assessment models, regulatory standards, and cybersecurity governance practices within engineering database environments. The review reveals that frameworks such as CVSS, FAIR, and CRAMM are widely adopted, with CVSS being the most prevalent due to its standardized vulnerability scoring system. Key threats identified include supply chain attacks, insider threats, and ransomware, emphasizing the need for multilayered defenses, zero-trust architectures, and real-time monitoring. Regulatory frameworks like GDPR, NIST SP 800-53, and CMMC play a crucial role in promoting compliance and resilience. Furthermore, the study highlights the rising integration of AI-based risk assessment tools, predictive analytics, and security automation in contemporary cybersecurity strategies. The findings underscore the need for engineering database security models to evolve by incorporating advanced analytics and compliance-driven approaches to address increasingly sophisticated cyber risks. This work offers valuable insights for database administrators, cybersecurity practitioners, and policymakers committed to enhancing the security posture of engineering databases.