Technical architecture, regulatory classification and potential applications in the security context

The growing debate surrounding preventive security technologies in public and semi-public spaces is also bringing AI-supported video analysis into focus in Europe. While the social context differs significantly from that of the United States, hybrid threats, lone wolf attacks and the protection of critical infrastructure (KRITIS) also present new challenges for European actors. Against this backdrop, systems for the automated detection of potentially dangerous situations are gaining in importance – provided they meet high technical, legal and ethical requirements.

From classic video surveillance to event-based analysis

In Europe, video surveillance is primarily reactive in nature. Cameras are used to secure evidence and investigate incidents after the fact. Modern AI-based video analysis shifts this focus to an event-based security architecture. The aim is not to identify individuals or evaluate behaviour, but to detect clearly defined objects or dangerous situations – such as openly carried firearms – at an early stage.

This object-centred approach is relevant from a regulatory perspective, as it is clearly distinct from biometric identification or behavioural profiling and is therefore compatible with European data protection and AI requirements under certain conditions.

System architecture: multi-level AI verification

Technically, modern visual weapon detection is based on a multi-layered architecture that combines response speed, accuracy and robustness:

1. Edge AI (on-premise analysis)

Video streams are analysed locally by compact AI models that are optimised for use on edge hardware. This first stage enables low latency, reduces data transmission and complies with data minimisation principles in accordance with the GDPR. The models are trained to recognise clearly defined visual characteristics (e.g. shape, posture, context of an object).

2. Cloud-based high-performance AI

If a potential event is detected, a secondary analysis is performed in the cloud using significantly larger and more powerful models. This second stage increases detection accuracy and reduces false alarms by enabling more complex scene interpretation. For European use, it is crucial that data processing takes place in certified data centres within the EU and that clear deletion and access concepts are implemented.

3. Human verification (human-in-the-loop)

The final decision is made by trained personnel. This third level is central from a European perspective: it ensures that no fully automated security-related decisions are made. The system thus complies with the principle of human oversight as enshrined in both the GDPR and the EU AI Act.

Integration into European security infrastructures

A key technical advantage lies in the integration into open video management systems (VMS). Camera manufacturer independence and standardised interfaces enable integration into existing security architectures of local authorities, educational institutions, transport companies or company locations.

Particularly relevant for European application scenarios is the connection to control centres and emergency services. Controlled access to live and recorded footage can improve situation assessment in emergencies – for example, in the context of critical infrastructure, major events or transport hubs. This requires clear role and authorisation concepts as well as audit-proof logging.

Regulatory classification: GDPR and EU AI Regulation

From a European perspective, the use of AI-supported video analysis is only permitted under clear conditions:

• Purpose limitation and proportionality: Use only in cases of specific security interest
• Data minimisation: Analysis as local as possible, transmission only event-related
• No biometric identification: Focus on objects, not people
• Human control: No fully automated interventions or alerts
• Transparency and documentation: Traceability of decision-making processes

According to the current interpretation of the EU AI Act, object-based hazard detection systems would probably be classified as ‘high-risk AI’, but would not be prohibited in principle – in contrast to real-time facial recognition in public spaces. The requirements for risk management, testing procedures, logging and governance are correspondingly high.

Technical performance and limitations

AI-supported visual weapon detection is not a panacea. Its performance depends heavily on image quality, camera positioning, lighting and context. Furthermore, false alarms can never be completely ruled out. However, the combination of edge AI, cloud analysis and human verification significantly reduces these risks.

It is crucial that such systems are not used in isolation. Only in combination with access controls, structural measures, organisational processes, training and clear deployment plans can an effective security concept be created.

Conclusion: Preventive security in the European context

AI-supported visual weapon detection also marks a technological paradigm shift for Europe: from reactive surveillance to preventive, event-based security. Technically mature multi-level systems show that high detection accuracy, data protection and human control are compatible.

For European users, it will be crucial to understand such technologies not as a replacement for existing security strategies, but as a complement to them. Properly implemented, they can save valuable time, improve situational awareness and reduce risks – without undermining fundamental European values such as data protection, the rule of law and proportionality.