You: Utilizing AI to monitor user behavior and identify anomalies indicating potential security breaches in financial applications involves a systematic approach that combines data collection, behavioral modeling, anomaly detection, and real-time alerting. This approach leverages machine learning techniques to establish a baseline of normal user activity and flag deviations that may signal malicious activity or compromised accounts. The process is designed to continuously monitor user actions, adapt to evolving behavior, and provide timely alerts to security personnel.
The first step is the collection of diverse user activity data from various sources within the financial system. This data includes login attempts, access patterns, transaction history, session duration, IP addresses, device information, and any interactions with the financial platform, all time-stamped for each specific user. For example, the system logs every time a user logs in, what time they log in, the location they log in from, and from what device. Transaction logs will include all the transactions a user has made, including the amount, recipient, time and location. This data needs to be collected continuously and accurately, while ensuring data privacy and security measures are implemented. Each user’s data is associated with a unique identifier, allowing the AI model to track that user's activity. The gathered data is then stored in a data lake or similar system where it can be processed and analyzed for potential threats.
The next step is to establish a baseline of normal behavior for each user. This is done by using machine learning algorithms to analyze historical user activity data. The algorithms use this data to create a model that represents the user’s typical actions and behaviors over time. This involves training different models for each user, rather than a general model, since different users will have different roles, different respo....
Log in to view the answer
