The course merges artificial intelligence (AI) with traditional methods, equipping professionals for the digital challenges ahead. It starts with anonymity and passive persona setup, integrating AI to detect potential risks effectively. We introduce an AI-powered search function to streamline terminology definitions and relationships, enhancing research efficiency. Incorporating AI tools into operational security, we identify weak links in security chains and provide real-time simulations to demonstrate modern intelligence capabilities and limitations. A significant focus is leveraging ChatGPT for various cyber intelligence tasks, including enhancing operational security, conducting open-source intelligence (OSINT), and automating stakeholder and threat actor analysis. The program covers various skills, such as adapting ChatGPT for cyber intelligence, critically assessing AI-generated insights, and integrating AI tools for enhanced analysis. We explore AI for scraping, sentiment analysis, and data categorization within OSINT, automating stakeholder sentiment analysis, and prioritizing intelligence requirements using AI. We examine pattern, trend, and tendency analysis with AI, assisting in data aggregation and correlation for adversary targeting. AI also plays a crucial role in sorting and categorizing collected data, optimizing collection planning, and automating parts of the collection process to streamline data gathering. We utilize AI algorithms for semantic analysis, uncover hidden threats in darknet sites, and analyze less common social media platforms. The course emphasizes AI's role in enhancing intelligence lifecycle production methods, validating alternative hypotheses, modeling adversary denial and deception techniques, and refining analytical writing through AI-based grammar and style checkers. By integrating AI across various stages, participants gain practical experience, becoming more effective and efficient professionals in navigating cybersecurity and intelligence challenges.
Learning Objectives
Use of recognized language across the intelligence community. Understanding what intelligence is and isn't. Skill in using multiple analytic tools, methods, and techniques. Develop expertise in AI-assisted analysis techniques, such as automated data scraping, sentiment analysis, and predictive modeling. Knowledge of how to evaluate and synthesize data. Learn to apply machine learning algorithms for data evaluation and pattern recognition. Ability to recognize and mitigate cognitive biases.
Use AI-powered tools to identify.