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In a context where internal security is of paramount importance, the integration ofartificial intelligence (AI) presents as many opportunities as it does challenges. In 2025, despite significant technological advances, homeland security forces must navigate complex issues such as digital sovereignty, population protection and operational improvement. While AI promises to transform operations, particularly through applications in human resources management and in video surveillance, the effective implementation of these technologies frequently encounters organizational barriers and problems ofintegration.
The use of artificial intelligence (AI) in homeland security remains an attractive but challenging prospect. Although AI offers considerable potential to help police and gendarmerie services manage resources and reduce administrative tasks, it is far from being a miracle solution for resolving complex investigations. Many projects have ended in failure due to inaccurate information provided by immature AI models. Current technological advances are not yet enough to completely overcome these obstacles, but they represent a new frontier full of opportunities. This article explores the various technological challenges of AI in homeland security, examining recent examples of failures and successes, while highlighting long-term potentials and regulatory issues.
Use of AI in Security Forces
In internal security forces, artificial intelligence is mainly used to manage human resources and alleviate the daily tasks of agents. The idea of summarizing a legal procedure to make it easily understandable by a police officer seemed revolutionary, but was stopped following the « hallucinations » of the AI model, illustrating the challenges of technological maturity. Colonel Sarah Platteau acknowledges that these plans, while ambitious, have not yet achieved the level of precision required for widespread adoption.
AI Model Integration and Accuracy Challenges
The implementation of AI in homeland security raises several organizational integration challenges. AI error can result in decisions being made based on incorrect information, which is unacceptable in such critical contexts. The problem of “hallucinations” or errors in AI models highlights the need to develop more reliable and robust algorithms. Other concerns include the management of big data and the need to ensure the protection and confidentiality of sensitive information processed by these intelligent systems.
Technological Progress and New Potentials
Although current techniques are not yet fully developed, they open the way to promising new applications. Innovations in video surveillance, text analytics, and data processing illustrate how AI can transform homeland security. These potential applications chart a new frontier for innovation, providing advanced means to analyze, predict and respond to security threats. However, their success will depend on continued investment in technological research and improvement of machine learning methods.
Regulation and Sovereignty Issues
As AI adoption intensifies, critical regulatory questions emerge. The need to establish a robust legislative framework for the use of AI in homeland security is imperative to prevent potential abuses and protect citizens’ rights. Additionally, the issue of digital sovereignty is becoming a priority, ensuring that the technologies employed are under the full control of the state and do not pose risks of espionage or cyberattacks. Governments must find a balance between technological innovation and the protection of individual rights.
