Outdated security systems rely on simple rules like motion detection or line crossing. This generates many alarms but provides little meaning. They react to every pixel change without understanding what is actually happening. The result is a flood of notifications that are difficult to filter, causing operators to lose overview. Cameras produce millions of images and notifications, but only a small fraction offers true insights. As a result, physical security remains primarily reactive: we record a lot, but understand little.

Previous generations of video analytics brought progress but still lacked context. Deep-learning models recognize objects, but they do so frame by frame, without temporal connections. Even systems that search based on text, such as “person with a backpack,” remain fragmented. They see what is happening but do not understand why it is happening. Intent is missing. The difference between someone entering a building normally and someone doing so with malicious intent is not recognized.

This explains why traditional solutions are limited in prevention. We record everything but cannot interpret or predict. For true proactivity, a system is needed that continuously reasons about what it observes. A system that understands behavior and context, not just objects. This is where the shift that modern AI technology now makes possible emerges.

AI that Understands What is Happening

One of the most significant developments is the use of Vision-Language Models (VLM) in security. These models combine visual information with language, allowing them to reason as humans do. They track events across multiple frames and analyze cause and effect. The AI not only sees someone moving but understands whether someone is entering a building normally or if it poses a risk.

Because this AI runs continuously on local systems with GPUs, it operates without interruptions and without relying on cloud processing. This saves bandwidth and keeps data local, which is better for speed and privacy.

These models are trained on large amounts of real security videos, carefully and ethically collected to prevent bias. With millions of hours of footage, the AI develops an almost intuitive understanding of situations. It recognizes patterns that were not explicitly present in the training data. This ability to recognize new situations is called open-set detection. As a result, the AI can interpret a wide variety of behaviors, including situations that are new or unexpected.

By combining image and language, AI can also assess intent. It distinguishes innocent actions from risky behavior and includes the environment in its assessment. And this happens at machine speed, day and night, for a large number of cameras simultaneously. This far exceeds what human operators could ever keep up with.

From Rules to Situations: A Different Approach to Detection

These developments are changing the way we look at triggers and events. Where systems once relied on fixed zones and simple rules, it is now about recognizing situations. An operator can simply state in their own words what is relevant, such as someone unauthorized trailing another person or groups lingering near an entrance after closing hours. The AI understands the description and actively monitors these scenarios. When an alert comes in, it immediately includes an explanation of what is happening and why it is important. This leads to less noise and a sharper focus on real risks.

Another big step is semantic search. Instead of reviewing hours of footage, you can ask questions like “show all instances where someone entered through the emergency exit in the last 24 hours.” The AI immediately filters the relevant moments. Forensic investigation becomes much more efficient as a result.

It goes even further. Modern AI can reconstruct a complete incident. A question like “what led to the fire alarm in Hall 3” results in an automatically generated timeline with video snippets and relevant events. This allows teams to get to the core of the issue faster without losing time.

Early adopters report impressive results: a reduction in false alarms of up to 95 percent and alert handling within a minute. Routine work shifts to the background, allowing operators to focus on real decision-making.

Market Readiness in Europe

Despite the potential, the market remains cautious. The security sector traditionally focuses on reliability, long replacement cycles, and predictability. Many professionals wonder if the technology is mature enough and if customers are ready for it. This is understandable, especially given previous disappointments regarding “smart” cameras that proved to be underwhelming in practice.

But AI is no longer a hype. Large organizations have already achieved concrete results, and end-customers are seeing the benefits of less noise and more efficient staff deployment. At the same time, regulatory pressure is increasing, making a wait-and-see approach a risk. Adoption is therefore growing step by step. Large enterprises in technology, financial services, and infrastructure are leading the way, compelling integrators and installers to acquire new knowledge and adapt processes.

Legislation plays a central role in this adoption in Europe.

Legislation: GDPR, NIS2, and the AI Act

In Europe, AI in security cannot be separated from three important frameworks: GDPR, NIS2, and the AI Act.

GDPR considers video footage as personal data. This means that every analysis must be justified, with a clear purpose and transparency towards those involved. Retention periods must be limited, and footage must be automatically deleted once no longer needed. Access to data must be logged, and where appropriate, measures such as privacy masking must be applied. AI must not process unnecessary data, and organizations must be able to explain whether and how an individual has been analyzed by AI. Privacy by design is essential here.

NIS2 directly links physical security to cybersecurity. Especially in essential sectors, organizations must demonstrate that cameras and access controls are securely integrated into the network. This means hardened firmware, encrypted connections, protection against attacks, and clear logging and incident response. The Cyber Resilience Act aligns with this by imposing requirements for secure standards, updates, and transparency about vulnerabilities.

The AI Act has the greatest impact on AI applications themselves. Behavioral analysis and large-scale surveillance fall into the high-risk category. This requires extensive documentation, explainability of decisions, and demonstrable human oversight. Organizations must clearly state what data is used and how conclusions are reached. From 2026, full obligations for high-risk systems will apply. Fines are comparable to those of the GDPR, making compliance absolutely necessary.

For users, this means that every investment in new security technology must be accompanied by questions about proportionality, transparency, security, and bias reduction. Only with a good balance between technology and policy can organizations leverage the benefits without risks.

Looking Ahead: Innovation with Responsibility

AI is profoundly changing physical security. We are moving from recording to understanding, and from reacting to anticipating. By eliminating noise and centering context, security teams can work more effectively and purposefully.

But this power demands responsibility. Transparency, explainability, and legal compliance are not secondary concerns but prerequisites. Manufacturers and integrators must therefore collaborate on solutions that are innovative while being legally and ethically sound.

At IDIS, we consciously choose this balance. We build systems that are securely designed and, where possible, promote privacy, so that customers can deploy AI without worrying about compliance. The goal is for users and policymakers to trust the technology and the way it is applied.

The security sector stands at the beginning of a new chapter. By remaining realistic, communicating openly, and learning from practical experience, AI can grow into a valuable partner. Triggers and events become insights, and security evolves toward a future where context and reliability are central.