The role of the police has historically been built around a delicate balance between state authority, community engagement, and the ability to respond to emergencies. Field observation, intuition honed by experience, human-centered investigations, and the rigorous application of the law have long formed the foundation of police work. However, over the past fifteen years or so, this balance has been profoundly disrupted. Crime is evolving faster than the structures tasked with containing it. It is becoming more mobile, more fragmented, more digital, and often transnational. Cyberfraud, organized criminal networks, money laundering via cryptocurrencies, exploitation of digital vulnerabilities, and the use of deepfakes are now everyday operational realities.
At the same time, the police’s information landscape is exploding. Urban video surveillance, in-car cameras, phone data, digital traces, social media, and urban sensors are generating an unprecedented volume of data. Europol estimates that more than 90% of major criminal investigations now involve a significant digital component1. This mass of information far exceeds human processing capabilities. It is in this gap that artificial intelligence is gradually establishing itself as a strategic tool, capable of analyzing, correlating, and prioritizing what humans alone can no longer comprehend.
The use of AI is far from marginal. The global market for security technologies incorporating AI is now worth over $45 billion, with double-digit annual growth, particularly in video analytics and cybersecurity2. But this adoption raises a fundamental question at the heart of the European democratic debate: how can we equip the police with powerful technologies without undermining civil liberties, the presumption of innocence, and public trust, in a context where the AI Act classifies certain police uses among the most sensitive3 ?
How AI Is Actually Being Integrated Into Police Work
Artificial intelligence is not making a dramatic breakthrough in law enforcement, but rather is being integrated step by step into various stages of police work. These applications, which are already very much a reality, are profoundly transforming the chain of prevention, investigation, and response.
- Predictive policing and territorial forecasting: statistical models analyze historical crime data, cross-referenced with temporal, socioeconomic, or environmental variables, to identify high-risk areas or periods. Used with caution, these tools can improve the allocation of patrols. If poorly managed, they risk creating feedback loops where certain neighborhoods are subject to sustained surveillance, regardless of actual crime trends4.
- Intelligent video analytics: Computer vision enables the automatic detection of unusual behavior, intrusions, abandoned objects, or unusual gatherings. In a world with over a billion surveillance cameras, these systems are primarily used to filter information and direct human attention, not to make decisions on their own2.
- Facial recognition and biometrics: likely the most controversial application. In Europe, real-time remote biometric identification in public spaces is generally prohibited, with exceptions strictly regulated by the AI Act, which illustrates the political will to establish clear red lines3. Retrospective use, within a specific judicial framework, remains authorized under certain conditions.
- Support for criminal investigations: AI speeds up the analysis of telephone, financial, and digital data. It helps identify networks, communication patterns, or suspicious flows that are invisible to the human eye. Europol considers these capabilities essential in the face of the growing sophistication of organized crime1.
- Cyberpolice and fraud prevention: Automated systems detect phishing campaigns, scam networks, and large-scale financial fraud. The UN estimates that digital scams now result in tens of billions of dollars in annual losses worldwide5.
- Incident management and prioritization: Some platforms use AI to optimize unit dispatch, emergency call management, and response times, while ensuring that the final decision remains strictly human.
A New Role for Police Officers: Balancing Data and Judgment
In this augmented environment, police officers are not being replaced; they are being repositioned. Their role is evolving into that of a human referee at the heart of an algorithmic ecosystem. Alerts, risk scores, automated matches, and data visualizations enhance their understanding of the situation, but require constant vigilance.
This transformation reinforces the individual responsibility of law enforcement officers on several levels. Operational responsibility, because misinterpreting an algorithmic signal can lead to inappropriate intervention. Legal responsibility, because the use of intrusive tools raises questions about the legality of police action. Finally, democratic accountability, as the legitimacy of these technologies depends on the institution’s ability to explain their use and ensure effective human oversight, as emphasized by several police ethics bodies6.
Thus, far from relieving the agent of responsibility, AI underscores the need for human judgment. It does not eliminate uncertainty; rather, it makes it more complex.
What skills do police officers need in the age of AI?
The core competencies of a police officer—legal knowledge, stress management, and a commitment to public service—remain essential. However, the integration of AI requires a fundamental upgrade in skills.
Technical skills
- Understand the general principles of the algorithms used.
- Know how to interpret a probability score without confusing it with proof.
- Work with specialized analysts and cyber units.
Analytical skills
- Develop a critical mindset when dealing with automated alerts.
- Combining algorithmic data with field observations.
- Identify potential biases in the systems.
Legal and Ethical Competencies
- Understand the GDPR and AI Act frameworks applicable to police operations.
- Understand the concepts of proportionality, purpose, and traceability.
- Incorporate the protection of civil liberties as an operational responsibility.
This shift requires a fundamental overhaul of police training to foster a culture of critical thinking regarding AI, rather than merely technical training.
Can artificial intelligence make police work more effective and fairer?
The efficiency gains are undeniable. AI speeds up the analysis of digital evidence, improves the detection of criminal networks, and strengthens the fight against large-scale fraud5. It also enables better resource allocation in a context of budgetary constraints.
But fairness is never a given. Research by NIST shows that some facial recognition systems have different error rates across demographic groups, which poses a major risk in a law enforcement context7. Predictive systems can also reinforce regional inequalities if they rely on historically biased data4.
Thus, AI does not inherently make the police more just. It can only be so if it is regulated, audited, and supervised by humans, in accordance with the spirit of the European framework3.
What will the police profession look like in the future?
The police officer of tomorrow will operate in a profoundly hybrid environment, where the physical and digital realms will be inseparable and will constantly feed into one another. Investigations will no longer be limited to gathering witness statements or physical evidence; they will systematically incorporate data-driven components, combining the analysis of digital data streams, the examination of traces left on platforms, and the correlation of images, communications, and transactions. This evolution will lead to increasing specialization within units, with a strengthening of cyber units, criminal analysis teams, and operational intelligence services capable of transforming raw data into actionable intelligence. New roles will emerge on a permanent basis within law enforcement, such as criminal analysts specializing in artificial intelligence, ethics officers responsible for overseeing the use of technology, and compliance officers responsible for traceability and ensuring that deployed systems comply with the legal framework. The role of the police officer will thus expand to include functions of interpretation, supervision, and coordination, where understanding the tools will become as strategic as their use.
At the same time, criminals are also embracing artificial intelligence—not merely as a gimmick, but as a tool to industrialize their operations. The automation of scams, the generation of credible fake content, voice deepfakes for identity theft, the use of autonomous systems to circumvent controls, and the exploitation of algorithmic vulnerabilities are already emerging realities. Europol anticipates a rise in forms of crime relying on advanced technologies and semi-autonomous systems by 20358. This growing technological asymmetry requires law enforcement to continuously adapt, not only technically, but also organizationally, strategically, and ethically. The more technology advances, the more the ability of police officers to understand, anticipate, and regulate these uses will become a decisive factor in the credibility and effectiveness of public action.
Toward greater security, but always democratic
Artificial intelligence is fundamentally redefining the fight against crime, not by turning the police into an automated institution, but by changing the way information is generated, interpreted, and utilized in law enforcement. It offers law enforcement unprecedented capabilities for analysis, correlation, and anticipation, enabling them to tackle more complex, faster-moving, and technology-driven forms of crime. But this new power comes with increased responsibility: the responsibility to never confuse decision support with the decision itself.
The police officer of tomorrow will therefore be neither a mere executor of algorithmic recommendations nor a technician disconnected from the field. They will be professionals with sound judgment, capable of interacting with complex systems while maintaining a human, contextual, and ethical understanding of situations. Their value will not lie in their ability to follow an alert, but in their ability to question it, qualify it, and, if necessary, challenge it. The more technology advances, the more central this skill will become.
Ultimately, the success of augmented policing will not be measured by the raw performance of the models or the number of sensors deployed, but by the ability of institutions to demonstrate that technology strengthens the legitimacy of police action rather than undermining it. The issue is not merely one of security; it is profoundly democratic. And if artificial intelligence is to transform policing, it will not be by replacing humans, but by compelling them to reaffirm, with even greater rigor, the foundations of trust between the state and its citizens.
Learn more
To broaden your perspective and understand how AI is reshaping other professions—from human resources to finance, and from healthcare to communications—we invite you to explore our dedicated section “AI & Professions”, which analyzes the concrete impact of intelligent technologies on skills, practices, and the organization of work.
References
1. Europol. (2024). AI and Policing: The Benefits and Challenges of Artificial Intelligence for Law Enforcement.
https://www.europol.europa.eu
2. MarketsandMarkets. (2024). AI in Video Surveillance Market. https://www.marketsandmarkets.com
3. European Union. (2024). Artificial Intelligence Act (Regulation EU 2024/1689).
https://eur-lex.europa.eu /a>
4. Hung, T.-W., Yen, C.-P. (2023). Predictive policing and algorithmic fairness.
https://link.springer.com
5. UNODC. (2024). Global Cybercrime Report.
https://www.unodc.org
6. London Policing Ethics Panel. (2025). Live Facial Recognition Report.
https://www.policingethicspanel.london /a>
7. NIST. (2025). FRVT Demographic Effects.
https://pages.nist.gov
8. Europol. (2025). Facing Reality: Law Enforcement and Emerging Technologies.
https://www.europol.europa.eu
