Time Series Forecasting of Data Breach Incidents: A Risk Assessment Approach Across Industry Sectors

Data breaches pose escalating risks to organizations worldwide, with far-reaching consequences for financial integrity, regulatory compliance, and public trust.As cyber threats grow in complexity and volume, there is an increasing need for predictive tools that can forecast the frequency and severity of data breaches across industry sectors.This study proposes a time series forecasting framework to predict future breach incidents using autoregressive integrated moving average (ARIMA), Facebook Prophet, and long short-term memory (LSTM) neural networks.The research explores sector-specific breach dynamics in finance, healthcare, and education-three domains with high data sensitivity and regulatory oversight.From a broad perspective, the study assesses historical breach data, identifying sectoral vulnerability trends, escalation risk indicators, and temporal clustering patterns.These analyses help characterize how breach events evolve over time and vary by industry.Each model's predictive performance is evaluated using metrics such as RMSE and MAE, providing comparative insights into their suitability for different forecasting horizons and volatility levels.The results demonstrate that LSTM models outperform ARIMA and Prophet in capturing non-linear patterns and sudden shifts in breach frequency, particularly in sectors prone to targeted attacks like finance.Meanwhile, Prophet offers interpretable trend-seasonality decomposition, making it suitable for sectors with cyclical breach activity, such as education.Forecast outputs are translated into risk indices, enabling organizations and regulators to anticipate breach surges and tailor cybersecurity investments accordingly.This approach underscores the value of time series models in proactive cyber risk assessment and resource allocation, contributing to sector-specific threat anticipation and resilience planning.