Author: Herrmann Friesen, Head of Cybersecurity Services Sales, Atos Group Germany,
How AI Detects Attacks Before They Strike
Europe’s energy networks are facing an unprecedented wave of coordinated cyberattacks. The invisible threat created by the convergence of IT and OT systems is particularly dangerous. Artificial intelligence (AI) is emerging as an essential instrument for identifying and neutralising attacks on critical infrastructure before they cause disruption.
Energy supply is the backbone of modern society — and an increasingly attractive target for cybercriminals. The number of incidents affecting European utilities has been rising for years. According to the EU Agency ENISA, more than 200 cyber incidents were recorded in the energy sector in 2023 alone, over half of them within Europe. The appeal to attackers is clear: few industries offer such leverage. A single successful intrusion can trigger supply shortages, financial losses, and political pressure. The 2025 incident in Norway, in which suspected Russian hackers temporarily seized control of a hydroelectric dam, underscored the severity of the risk.
IT/OT Convergence as an Entry Point
A major vulnerability lies in the tight interconnection between information technology (IT) and operational technology (OT). While IT systems are generally well protected through established security frameworks, many OT environments still rely on legacy protocols designed for isolated networks.
Attackers exploit this weakness, gaining access via known software flaws or compromised supply chains before moving laterally into the OT domain. Once inside, they can remain undetected for months, later manipulating control commands, falsifying data, or shutting down operations. These “silent” attacks are especially dangerous — invisible to traditional, signature-based security systems.
From Visibility to Resilience
In this context, transparency becomes a prerequisite for resilience. Experts agree that limited visibility is one of the sector’s greatest risks. Energy operators can only respond effectively if they achieve continuous, end-to-end monitoring — from cloud applications and corporate IT down to field devices and edge controllers.
This holistic visibility is both a technical and organisational challenge. Integrating different systems, interfaces, and responsibilities is essential for detecting and assessing threats in real time. Only by combining IT and OT telemetry can organisations build a unified threat picture and coordinate an appropriate response.
Modern Detection Beyond Signatures
Conventional security tools based on known attack signatures are no longer sufficient. Threat actors continuously adapt, creating new exploits that bypass static defences. Instead, modern protection depends on detecting anomalies before disruptions occur.
Machine learning and advanced analytics now make this possible. These systems identify patterns in large data streams, flag unusual protocol activity, and reveal abnormal communication between assets. Time-series analysis models the expected behaviour of critical systems, while algorithms such as Random Forest or Support Vector Machines (SVM) enable real-time network monitoring with accuracy rates exceeding 99%.
For more complex threats, deep learning methods like Long Short-Term Memory (LSTM) networks enhance intrusion detection systems (IDS) through adaptive learning. The results are dramatic: detection times shrink from weeks or months to hours or even minutes — giving operators valuable time to isolate incidents and prevent cascading damage. The key lies in the systems’ ability to “learn” what constitutes normal operational behaviour.
Resilience Through Collaboration
Even the most advanced technology cannot stand alone. Cyberattacks on the energy sector are now so sophisticated and transnational that resilience depends on cooperation. Europe’s response is multifaceted. A central instrument is the NIS2 Directive, which obliges operators of critical infrastructure to strengthen their cybersecurity and report incidents promptly.
Initiatives such as GAIA-X aim to build sovereign European data and cloud infrastructures, reducing dependency on non-European providers. In parallel, cross-sector Computer Emergency Response Teams (CERTs) and shared threat-intelligence platforms facilitate the rapid exchange of attack indicators and patterns. In an environment as interconnected as energy, this collective awareness is key to breaking coordinated attack campaigns.
AI further amplifies this collaboration. Within CERTs and Security Operations Centers (SOCs), AI functions as an intelligent assistant — pre-filtering massive alert volumes, correlating events, and automating initial threat triage. Routine tasks such as context enrichment or attacker profiling can be delegated to AI systems, allowing analysts to focus on critical decision-making. This “human-in-the-loop” model accelerates response times while enhancing accuracy and consistency.
Conclusion: Energy Security Equals Cybersecurity
The lessons are clear: energy security today is inseparable from cybersecurity. Attacks on grids and control systems are attacks on society itself. To safeguard operations, providers must invest in three pillars:
- End-to-end visibility across the entire infrastructure,
- Intelligent anomaly detection powered by machine learning, and
- Active participation in European cooperation and information sharing.
As the energy landscape evolves toward greater digitalisation and decentralisation, the attack surface expands — making advanced protection strategies indispensable. True resilience cannot be achieved overnight. It is the result of continuous investment, disciplined processes, and international collaboration.
