Cybersecurity Data Collection Research Assistant

The main objective of the project, focusing on cybersecurity data collection and labeling for vulnerability management, is to enhance the understanding and management of cybersecurity vulnerabilities. This project aims to engage students in the crucial task of gathering, analyzing, and labeling data related to cybersecurity threats and vulnerabilities. Problem to Solve: Students will be tasked with addressing the challenge of identifying and categorizing various types of cybersecurity vulnerabilities. This involves the collection of vast amounts of data from different sources, such as security logs, network traffic, and public vulnerability databases. The key challenge lies in accurately analyzing and interpreting this data to identify potential vulnerabilities. Expected Outcome: By the end of the project, students are expected to achieve the following outcomes: 1. Comprehensive Data Collection: Students should be able to gather relevant cybersecurity data from multiple sources systematically. This includes understanding where to find data and how to extract it efficiently. 2. Effective Data Labeling and Categorization: Students should develop skills to accurately label and categorize the collected data based on the type and severity of the vulnerabilities. This involves understanding different types of cybersecurity threats and their characteristics. 3. Vulnerability Analysis Skills: Students should be able to analyze the labeled data to identify patterns or trends that could indicate potential security vulnerabilities or breaches. 4. Reporting and Documentation: Students should be able to document their findings in a clear and concise manner, providing insights and recommendations for vulnerability management and mitigation strategies. 5. Awareness of Ethical and Legal Considerations: Students should understand and adhere to ethical and legal standards in data handling, particularly regarding sensitive or personal information. 6. Collaborative Skills: Given the complexity of cybersecurity, students should also learn to collaborate effectively, sharing insights and combining expertise to tackle multifaceted problems. The success of this project lies in its ability to equip students with practical skills in handling real-world cybersecurity challenges, enhancing their ability to identify, analyze, and manage cybersecurity vulnerabilities in various environments.

Prompt Engineering and Agent Refinement for CVE Analysis

PatchIT aims to enhance the performance of its AI agents in handling cybersecurity queries, particularly around CVE data. The project involves prompt engineering, testing, and refining the interaction logic of these AI agents to improve their accuracy and efficiency. Students will design effective instructions, test the agents' responses, and refine the logic to ensure better handling of CVE-related queries. The project offers a hands-on opportunity to apply classroom knowledge in AI and cybersecurity, focusing on real-world scenarios and data. The ultimate goal is to streamline the agents' responses and automate repetitive queries, thereby improving overall system performance. - Understand CVE analysis and their current capabilities. - Design and test various prompts for CVE queries. - Refine the agents' response logic for better accuracy. - Automate common analysis patterns for repetitive tasks. - Document the process and provide recommendations for future improvements.

AI Incident Dashboard Visualization

Project Objective: The main objective of this project is to develop a comprehensive visualization dashboard for the AI incident database, aiming to provide stakeholders with actionable insights into AI-related incidents, trends, and patterns. Learners will create an interactive platform that enhances their understanding of the AI threat landscape, enabling better decision-making and proactive measures for AI system security. Problem Description and Expected Outcome: Learners will tackle the challenge of transforming raw AI incident data into meaningful visual representations. By the end of the project, they are expected to achieve: A fully functional dashboard that displays trends, patterns, and insights derived from the AI incident database. Enhanced understanding of AI-related incidents, including attack vectors, threat types, and affected sectors. Ability to navigate and interact with the dashboard to filter, drill-down, and explore detailed incident information. Practical experience in data visualization techniques and tools, enhancing their skills in conveying complex data effectively. Deliverables include documentation that outlines methodologies, design decisions, and recommendations for further enhancements. Overall, the project aims to empower learners with the skills to analyze and present AI incident data visually, contributing to informed decision-making and proactive management of AI-related risks.