AI-powered video analysis for critical infrastructure

Large, spread-out sites such as substations, gas storage facilities, waterworks, ports, chemical plants or airports are virtually impossible to monitor comprehensively using manual labour alone. At the same time, the threat landscape, regulatory requirements and the pressure on security and control centre staff are all increasing. Traditional video surveillance is reaching its limits: too many images, too little context, too high a manual workload. To truly put security architecture into practice today, it must be approached holistically: individual systems must no longer operate in isolation alongside one another, but must be more closely integrated, communicate with one another and consolidate relevant information in real time. Only then can a robust situational picture be created, on the basis of which security managers can respond more quickly, in a more targeted and coordinated manner.

Typical challenges:

Significant uncertainty surrounding manual video surveillance

Increasing demands regarding documentation and compliance

Delays in the detection of security-critical incidents

Reducing false alarms

AI-powered video analytics: the next step in security architecture

SecuSite AI is a modular platform for AI-powered video analytics that intelligently analyses video and sensor data and automatically detects security-critical events. The solution is specifically designed for KRITIS environments and combines powerful video analytics with robust operational models, scalable edge inference architecture and deep integration into existing security and IT landscapes. By processing data close to the source, SecuSite AI reduces latency, optimises bandwidth and enables stable operating conditions even in distributed infrastructures. Technologically, the solution is based, amongst other things, on NVIDIA technologies as an accelerator stack for video analytics applications.

Rather than simply generating an alarm message, detected events are enriched with contextual information such as time, location, asset reference and event type, and passed on to connected systems. A modular analytics framework allows for flexible expansion to accommodate new use cases. Security rules can also be defined and customised using natural language – even without in-depth programming knowledge.

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The combination of edge AI, rule-based sensor technology and modern vision-reasoning models (e.g. NVIDIA Cosmos) transforms video data into an intelligent analysis process: efficient at the local level, in-depth at the central level – for valid, context-based decisions.

Sensors & Triggers (Edge) – Rule-based activation on-site
– Combination of cameras, motion detectors, access control, etc
.– Defined rules trigger analysis and/or recording –
Focus solely on relevant situations
Event Filtering & Clip Creation – Intelligent pre-processing
– Selection of relevant events (no flood of raw data)
– Creation of short, focused video clips
– Initial structured description (metadata / tags)
Centralised AI Analysis (Cosmos Reason / Cosmos 3) – Scene understanding & context analysis
– Processing in the cloud or on secure customer infrastructure
– Vision reasoning models analyse:
– Relationships and interactions
– Sequences of events
– Anomalies and patterns
Context & recommended action – Interpreted events rather than raw data
– Automatic classification of the situation (e.g. security-relevant, critical)
– Reduction of false alarms
– Handover to control centre, systems or processes

Detection is not the end of the process. SecuSite AI helps to process detected incidents in a structured manner and integrate them into existing security processes. Events can be automatically documented, assessed and forwarded to the relevant departments or downstream processes. The associated records can also be specifically referenced to classify incidents more quickly, track measures taken and make decisions based on reliable information.

In addition to real-time detection, SecuSite AI can also serve as a basis for further analysis. This enables security-related events, patterns and processes to be analysed in a more targeted manner, for example to identify anomalies, vulnerabilities or areas for optimisation. Which analyses are appropriate and feasible in a specific context should be considered on a case-by-case basis and worked out in detail jointly.

Deployment scenarios

Perimeter protection

Detect unauthorised access immediately

Detects unauthorised entry into security-critical areas in real time – e.g. when someone scales a fence, enters defined zones without a security pass, or enters outside permitted hours – and triggers an alarm immediately.

Safety zones

Automatically identify breaches of regulations

Monitors defined security areas and automatically identifies breaches of operational guidelines – such as entering restricted areas, failure to wear protective equipment or unauthorised activities – in real time.

Emergency detection

Detect critical situations immediately

Detects potential emergencies such as a person lying motionless on the floor, falls or unusual movement patterns, and enables an immediate alarm to be raised and measures to be taken swiftly.

The technical features in detail

Detects and classifies relevant objects such as people, vehicles or items in real time. This forms the basis for further analysis and security-related assessments.

Typical use cases:

PPE detection (helmet, vest, protective clothing)
Weapon detection (e.g. knives, firearms – depending on system capability)
Vehicle detection (cars, lorries, specialist vehicles)
Detection of abandoned or suspicious objects

Defines virtual security zones and automatically detects when these are entered, exited or used in an unauthorised manner.

Typical use cases:

Intrusion detection / perimeter breach
Unauthorised access to restricted areas
Entry into high-risk areas
Remaining in prohibited areas outside defined times

Analyses movement patterns and detects conspicuous or unusual behaviour based on defined rules or machine-learning models.

Typical use cases:

Fall detection / motionless individuals
Vandalism (e.g. damage to infrastructure)
Prolonged loitering in sensitive areas

SecuSite AI can automatically detect defined events and trigger targeted alerts or escalations based on them. Which alerts are triggered, prioritised or forwarded to specific parties depends on the individual security logic, operational procedures and the respective level of criticality. The specific configuration should therefore be considered on a case-by-case basis and defined in detail jointly.

Your benefits

Detecting events in real time

Our platform continuously analyses video data and automatically detects security-related incidents. Unauthorised access, perimeter breaches, suspicious movement patterns or critical behavioural anomalies are immediately identified and reported as a priority.

Relieving the burden on control centres and security staff

Instead of constantly monitoring all video streams manually, control centres receive targeted alerts about incidents that are actually relevant. This reduces the flood of information and improves operational focus.

Improving documentation and traceability

Relevant events are documented in a structured manner and can be consolidated into predefined security reports. This supports internal governance, compliance requirements and robust incident follow-up.

The implementation

1. Planning

Comprehensive advice on defining relevant areas and analysis objectives. Risk assessment: What events might occur and which processes should be prepared for? Development of escalation plans for anomalies, breaches and critical incidents.

2. Installation

We integrate the data streams with our platform in compliance with data protection regulations. The platform itself can be operated as a cloud service (on EU servers) or on-premises within your own infrastructure.

3. Development

Next comes the training of the artificial intelligence, which refines event detection and minimises false alarms. In addition, escalation levels are tested and the workflows that trigger them are refined.

4. Operation

The application is being put into live operation. Events are documented, video data is analysed, relevant events are reported and anomalies are flagged.

That’s why Materna

In KRITIS environments, it is not enough simply to detect anomalies. What is crucial is that a detected incident is turned into a robust process: with clear classification, rapid escalation, technical interoperability and traceable documentation. This is precisely where Materna’s strength lies. We combine AI-powered video analytics with integration expertise, regulatory knowledge and experience in complex security and IT environments.

Understanding KRITIS

We combine technological expertise with a clear understanding of the regulatory and operational requirements of critical infrastructure.

Integration rather than a stand-alone solution

Our platform integrates with existing security, control centre and IT systems, rather than creating new silos.

Self-sufficient operating models

Depending on the protection requirements, the solution can be deployed on-premises, at the edge, in a hybrid environment or within defined cloud models.

From detection to response

We consistently view video analysis as part of an end-to-end security and escalation process.

Audit readiness right from the start

Documentation, traceability and interoperable reporting processes are taken into account right from the start.

End-to-end support

We support you every step of the way, from defining the use case right through to safe, large-scale operation.

Please feel free to contact us

Portrait von Ansprechpartner Marcus Goetting

Significant uncertainty surrounding manual video surveillance

Increasing demands regarding documentation and compliance

Delays in the detection of security-critical incidents

Reducing false alarms

AI-powered video analytics: the next step in security architecture

How SecuSite AI detects incidents

How incidents are dealt with

What other analyses are possible

Perimeter protection

Safety zones

Emergency detection

Object Detection & Classification

Zone-based event detection

Movement & Behaviour Analysis

Event-based alerting

Detecting events in real time

Relieving the burden on control centres and security staff

Improving documentation and traceability

1. Planning

2. Installation

3. Development

4. Operation

Understanding KRITIS

Integration rather than a stand-alone solution

Self-sufficient operating models

From detection to response

Audit readiness right from the start

End-to-end support

Please feel free to contact us

Marcus Götting Leiter Competence Center IoT

Marcus Götting
Leiter Competence Center IoT