Prevention is one of the most frequently cited – and at the same time most frequently underestimated – principles in the security industry. As long as nothing happens, its benefits remain invisible. It is only when production lines come to a standstill, infrastructure fails or security situations escalate that it becomes clear just how costly a lack of precaution actually is. This is precisely where a modern understanding of security comes in: the focus is not on reacting to damage, but on the ability to identify risks at an early stage, assess them accurately and take countermeasures before damage occurs, if possible.

This fundamentally shifts the focus. Control centres, sensor technology, networked platforms and AI-supported analyses no longer serve merely to document or process incidents. They become tools for active risk management. The aim is to design operational processes in such a way that critical situations can be identified early and, where possible, mitigated – in other words, to ‘stay one step ahead of the situation’, as Kai Eckstein, Director of Sales for Germany/Switzerland at Advancis Software & Service GmbH, put it.

Prevention: simple in concept, complex in implementation

To illustrate this, Eckstein used a deliberately simple image: training wheels on a child’s bicycle. The underlying logic is clear – risks are minimised through appropriate framework conditions before they arise. Applied to industrial and critical infrastructures, however, this means far more than simple protective mechanisms. Here, the focus is on safeguarding highly complex processes.

In industrial reality, the stability of production chains directly determines economic success. A shutdown is not merely a technical problem, but a business-critical event. Accordingly, the focus is not on the individual security system, but on the question of which processes must be protected so that value creation can take place at all.

Thinking about security from a KRITIS perspective

This shift in perspective is particularly evident in critical infrastructure. Security incidents there are not isolated events, but triggers for potentially far-reaching consequences: production outages, supply disruptions, reputational damage or regulatory repercussions.

It is therefore crucial to link risk analysis, organisational preparedness and technological implementation. Prevention does not arise in the abstract, but from a systematic understanding of which events can have which effects – and which measures must be put in place in advance.

When the weather becomes a production risk

An industrial example from Neckarsulm illustrates just how closely physical hazards and operational impacts are linked. A heavy rainfall event there led to water penetrating critical infrastructure areas. The actual escalation, however, arose from consequential damage: short circuits, fires and, ultimately, the failure of central systems – resulting in days of production downtime.

Such scenarios highlight the importance of a preventive approach: the weather itself cannot be controlled, but preparation for it can. By combining weather data, risk assessments and defined escalation mechanisms, protective measures can be initiated at an early stage – for example, through automated barriers or targeted interventions.

A key success factor here is automation. In time-critical situations, a response must not simply arise, but must be prepared in advance. “The system must be designed so that certain protective measures are carried out automatically wherever possible,” emphasised Eckstein.

Prevention as a universal principle

This logic applies not only to natural events. Hail damage to industrial facilities or targeted attacks on banking systems also follow similar patterns: The actual damage is often merely the tip of a process that is foreshadowed by recognisable signs.

This is precisely where the potential of modern security architectures lies. Anomalies, technical changes or unusual behaviour patterns can be detected at an early stage – provided that data is consolidated and intelligently analysed. Security-relevant events rarely begin abruptly, but develop according to patterns.

The control centre as a central authority

In this context, the role of the control centre is also changing. It is no longer merely an operational unit for handling alarms, but is evolving into the central authority for situation assessment and decision support.

The added value arises from the ability to correlate data: individual data points – such as those from video surveillance, access control, sensor technology or external sources – only gain significance when considered in conjunction with one another. The challenge lies less in a lack of information than in the failure to integrate and analyse it in real time.

AI as an enabler, not an end in itself

Artificial intelligence plays a central role in this context, not as a substitute for human decision-making, but as an accelerator. It enables patterns to be recognised, anomalies to be highlighted and indications of potential risks to be derived from large volumes of data.

An example: if video surveillance detects unusual behaviour at an ATM, this information can be automatically linked to further data. Measures can then be derived from this – ranging from raising the alarm to automated responses such as triggering protective mechanisms.

However, it remains important to embed this within clear security logic. AI is only as effective as the data set and the processes into which it is integrated.

Detecting anomalies before damage occurs

AI is particularly relevant when analysing behavioural patterns. Routines in everyday working life generate stable patterns – deviations from these can indicate risks at an early stage. It is precisely these subtle changes that are often difficult for human observers to detect, but which data-based systems can evaluate.

For security organisations, this represents a paradigm shift: the focus is no longer on damage that has already occurred, but on identifying potential risks as early as possible.

From alarm processing to forecasting

Modern security architectures go one step further: they enable forecasting. By combining various data sources, scenarios can be anticipated before they occur.

A typical example is the integration of video analysis and timetable data in a rail context. It is only through this combination that a clear picture of the risks emerges – and with it the basis for preventive action.

The real added value therefore lies not in an increasing number of alerts, but in the quality of the decisions derived from them.

Prevention is more than just technology

Despite all the technological possibilities, prevention remains an organisational and strategic task. It requires clearly defined objectives, a deep understanding of risks and a willingness to systematically consider even unlikely scenarios.

Many organisations still have some catching up to do in this regard. Measures are often only implemented after an incident has occurred. The preventive approach, however, demands precisely the opposite: forward-looking action.

At the same time, awareness of this necessity is growing – not least due to regulatory requirements such as KRITIS or NIS2, as well as a general increase in sensitivity to resilience issues.

Prevention as a management task

The key insight: prevention is not an additional function, but a management task with direct operational relevance. Anyone wishing to design security effectively must consolidate data, structure processes and deploy technological capabilities in a targeted manner.

The bar is set high, but the goal is clear: it is about ensuring operational capability, security of supply and, ultimately, the protection of people and assets. Or, as Eckstein put it more pointedly: “If the vehicle doesn’t roll off the production line, the manufacturer doesn’t make any money.”

This makes it clear what is at the heart of the matter: security is not an end in itself. It is a prerequisite for functioning processes – and thus for economic and social stability.