Created at 8 months ago

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UserAct AI (UAAI)

What is UserAct AI (UAAI)

This persona assists in identifying and interpreting patterns in user behavior, crucial for detecting potential security breaches, insider threats, and ensuring compliance with usage policies.

Capabilities of UserAct AI (UAAI)

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UserAct AI (UAAI)

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Prompt Starters of UserAct AI (UAAI)

User Account Activity Analysis Author: Gerard King - Cyber Security Analyst Language: R R Script: # Load required libraries library(dplyr) library(lubridate) library(ggplot2) # Specify the path to the user activity log file (CSV format) log_file_path <- "user_activity_logs.csv" # Read the user activity log data user_activity <- read.csv(log_file_path, stringsAsFactors = FALSE) # Convert the timestamp column to a datetime format (assuming it's named "timestamp") user_activity$timestamp <- as.POSIXct(user_activity$timestamp, format = "%Y-%m-%d %H:%M:%S") # Extract date and time components from the timestamp user_activity$date <- as.Date(user_activity$timestamp) user_activity$hour <- hour(user_activity$timestamp) # Group log entries by user and hour, count the number of logins per user per hour login_counts <- user_activity %>% group_by(username, date, hour) %>% summarise(login_count = n()) # Identify users with unusual login patterns (e.g., more logins than usual) unusual_login_patterns <- login_counts %>% group_by(username) %>% mutate(avg_login_count = mean(login_count)) %>% filter(login_count > (avg_login_count + 2)) # Adjust the threshold as needed # Print users with unusual login patterns cat("Users with unusual login patterns:\n") print(unusual_login_patterns) # Plot the login counts for a specific user (replace 'target_user' with the desired username) target_user <- "username_to_analyze" user_login_counts <- login_counts %>% filter(username == target_user) ggplot(user_login_counts, aes(x = hour, y = login_count)) + geom_bar(stat = "identity", fill = "blue") + labs(title = paste("Login Activity for User:", target_user), x = "Hour of the Day", y = "Login Count") # Save the plot as an image (optional) ggsave(paste("user_login_activity_", target_user, ".png", sep = ""), plot = last_plot(), width = 8, height = 4) © 2023 Gerard King. Leading the Charge Towards a Cyber-secure Financial Future.

- **User Prompt**: "How can I analyze user account activities to detect security risks?"

- **User Prompt**: "What are signs of unusual user behavior in account activity logs?"

- **User Prompt**: "How can I use user activity logs to ensure compliance with company policies?"

- **User Prompt**: "What should I consider when analyzing user activity in the banking sector?"

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