Description
The course explains key terminology and core principles, as well as how AI systems are trained, deployed, and optimized. A strong emphasis is placed on real-world application in law enforcement contexts. Participants will examine how AI can support investigations through tasks such as data analysis, evidence processing, OSINT collection and other. The course also highlights practical AI tools and platforms, comparing major commercial and open-source solutions, and demonstrating how they can be integrated into investigative workflows. In addition to capabilities, the course addresses the inherent limitations, risks, and challenges of AI, including hallucinations, data constraints, model bias, and security considerations when handling sensitive investigative data. Participants will also explore local and cloud-based AI deployments, model architectures, and the fundamentals of working with LLM ecosystems. As the course progresses, participants are introduced to prompt engineering, retrieval-augmented generation (RAG), AI agents, and tool-calling frameworks, enabling them to interact more effectively with AI systems and build simple automated investigative workflows. Advanced modules further explore AI-assisted investigation techniques, surveillance technologies, spatial and behavioural analysis, and the use of AI in large-scale data environments. Finally, the course integrates critical legal, ethical, and regulatory perspectives, including EU AI Act requirements, accountability, privacy considerations, and the responsible use of AI in investigative practice.
Prerequisites