AI Sentinel: Predictive Anomaly Detection

Inspired by the powerful, often unseen AI entities in Simmons' 'Hyperion' and Kubrick's '2001', this project, 'AI Sentinel', aims to create a proactive cybersecurity solution. The core concept is to build a personalized anomaly detection system that moves beyond reactive threat responses to predictive security. The system works by first leveraging a simplified version of an 'AI Workflow for Companies' scraper to analyze publicly available workflow patterns for various industries or roles. This data serves as the initial training dataset. Then, the user, or a small business, would input their specific workflow data – application usage patterns, typical network traffic, login times, etc. AI Sentinel then trains a machine learning model (likely a recurrent neural network or an anomaly detection algorithm like Isolation Forest) to understand the baseline 'normal' behavior. This model constantly monitors system activity and flags any deviations from the established baseline as potential anomalies. For example, if an employee suddenly starts accessing files they never have before, or network traffic spikes at an unusual time, the system would trigger an alert. The 'Hyperion' inspiration comes from the idea that AI is constantly observing and learning, anticipating threats. The '2001' inspiration comes from the calm, vigilant nature of HAL 9000, constantly monitoring systems for deviations. AI Sentinel focuses on a niche area – personalized, predictive anomaly detection for small businesses or individuals who lack dedicated cybersecurity teams. It's low-cost because it utilizes readily available open-source libraries and doesn't require extensive computational resources to run once trained. High earning potential comes from offering it as a subscription service (e.g., a monthly fee for anomaly detection reports and alerts) or selling pre-trained models tailored to specific industries. Further development could include integration with existing security tools and automated response mechanisms. This leverages the scraper component by dynamically adjusting baseline models based on detected shifts in workflow trends across similar businesses.