Journal of Engineering Research and Reports

This study examines the ethical and legal implications of Shadow AI in healthcare, finance, and education by analyzing unauthorized AI deployments and their impact on data privacy, cybersecurity, and regulatory compliance. Using a quantitative research approach, descriptive statistics, ordinal regression modeling, and network analysis were employed to assess AI violations using the MITRE ATLAS AI Incident Database, EU AI Act Public Database, and IBM X-Force Threat Intelligence Report. Findings reveal that privacy breaches are most prevalent in education (22 cases), bias-related issues dominate finance (20 cases), and cybersecurity risks are highest in healthcare (19 cases). Legal risk analysis shows a 20% probability of regulatory intervention, with breach type as the strongest determinant. Anomaly detection identified healthcare as the most vulnerable to AI-driven cyber threats (8 anomalies). This study contributes to AI governance literature by quantifying the impact of regulatory interventions on Shadow AI risks, demonstrating how enforcement actions influence unauthorized AI adoption trends. It also underscores the limitations of current frameworks (e.g., GDPR, HIPAA, SEC regulations) in mitigating AI-related violations. The findings emphasize the urgent need for sector-specific AI compliance frameworks, AI ethics committees, and real-time cybersecurity monitoring systems to mitigate risks. Strengthening legal accountability and regulatory enforcement is critical to preventing the unchecked proliferation of Shadow AI in sensitive industries. Recommendations include sector-specific AI compliance frameworks, AI ethics committees, cybersecurity policies, and stricter regulatory enforcement.