The Prediction of Global Spending Trends on Security Using Holt-Winters Model

Over the years, threat actors have grown in their capabilities and diversified in their attack methods, increasing the ingenuity and pervasiveness of their threats. The massive data generation in modern-day digital interactions has enabled cyber security teams to amass large data to hand over to machine learning models for discerning patterns. As artificial intelligence (AI) becomes increasingly integrated into security frameworks, organizations face the dual challenge of mitigating AI-related threats while optimizing their security expenditure. This research delves into the complexities involved in managing these risks effectively. It explores the impact of emerging AI threats within cybersecurity landscapes and other relevant fields. It discusses the critical importance of efficiently forecasting the growth of allocated resources to bolster security measures. By emphasizing a balanced approach, this paper aims to provide insights into the intricate interplay between AI threats and security spending, offering a forecasting mathematical model to navigate this dynamic landscape in the future.