This paper explores the design and implementation of advanced information systems in Law Enforcement Agencies, focusing on how these systems harness artificial intelligence for actionable insights and enhanced operational efficiency, addressing the critical challenges of managing and interpreting vast data sets within Law Enforcement Agencies, where the role of sophisticated information systems has become increasingly central. This paper presents the design, development, and deployment of an advanced Network Intrusion Detection System powered by Machine Learning, integrated with Cyber Threat Intelligence and explainable AI capabilities. The presented system exemplifies a real-world application of Data Science and Machine Learning methodologies, crafted to enhance the operational effectiveness of Law Enforcement. It utilizes a model-driven architecture to process and analyze data from network traffic, effectively identifying and responding to cyberthreats in both real-time and in forensic mode. The development of this system embodies the commitment to pushing the envelope in information system innovation, focusing on practical deployment in high-stakes environments. Through the integration of xAI, transparency and interoperability are provided, supporting the trustworthiness and accountability of Law Enforcement operations. This paper not only explores the technological aspects of the presented system but also highlights its implications for security, operational efficiency, and ethical AI use within Law Enforcement contexts.