AI Security: How to Protect Industrial Operations in 2026

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As Artificial Intelligence transforms critical infrastructure and industrial operations, securing these fast-evolving models has become a top priority for organisations.

Understanding the cyber risk landscape and regulatory requirements for AI in critical infrastructure and industrial environments

Why is industrial AI adoption creating new cybersecurity risks?

Artificial Intelligence adoption in the industrial sector is surging at an unprecedented speed. Today, 61%[1] of industrial organisations already run AI in live operations, powering everything from energy grids and transport networks to industrial control systems. However, this rapid rollout often occurs long before comprehensive security risk assessments are completed.

Standard risk assessments struggle to keep pace because AI models are replaced within months, and unexpected capabilities continuously emerge. This rapid cycle opens up novel attack surfaces that standard IT security isn’t equipped to handle:

Data Poisoning & Manipulation: AI systems can be tricked, manipulated, or poisoned with malicious data feeds.
Prompt Injection & Jailbreaks: Vulnerabilities in large language models (LLMs) allow unauthorised access or tool misuse.
Opaque Supply Chains: Complex third-party model integration creates hidden risks across your entire infrastructure.
Shadow AI: 47%[2] of organisations report limited or zero visibility into the actual AI tools used by their employees.

How can you navigate complex AI regulations like the EU AI Act?

AI regulations rarely emerge in isolation; they overlap directly with existing operational, security, and data privacy obligations. Demonstrating compliance requires an architecture built to withstand offensive security tests and satisfy strict auditing standards.

Key regulatory frameworks impacting industrial AI include:

Regulation EU 2024/1689

EU AI Act

Risk-based application across AI lifecycle: classification, data governance, documentation, human oversight, robustness. Deadlines have shifted since adoption; what matters is knowing which of your systems are in scope and what evidence you could actually produce.

In force in Germany via NIS2UmsuCG

NIS2

Risk management, supply chain security, incident reporting and management accountability for essential and important entities. Where AI enters those environments it inherits the same duties, and widens the supply chain that has to be governed.

Example for regulated sector - aviation

Part-IS

Information security management for aviation organisations, with a direct line of airworthiness and operational safety. Where AI touches those systems, security and safety evidence has hold together rather than sit in separate files.

Regulation (EU) 2016/679

GDPR

AI systems require a clear legal basis for training and inference data, alongside DPIAs and controls automated decision-making. Furthermore, organisations must ensure the system can honour data-subject rights, even after personal data has passed through a model or retrieval pipeline

What is a 3-pillar approach to end-to-end AI security?

To successfully secure AI systems across cloud, hybrid, and on-premises environments, organisations must combine compliance, architectural engineering, and offensive testing:

1. AI Risk & Compliance: Establish clear threat modeling, regulatory gap analysis, and practical remediation roadmaps.
2. AI Security Architecture: Engineer security directly into the underlying tech stack from concept through vendor evaluation and integration.
3. Offensive Security & Penetration Testing: Simulate real-world LLM attacks (such as RAG data-boundary exploitation and privilege escalation) to remediate threats before breach.

Securing your AI journey

Securing industrial AI requires an integrated approach where safety, cybersecurity, and regulatory compliance go hand-in-hand. Ready to assess your AI security posture? Request your AI Security & Compliance Assessment with Airbus Protect today!

[1] Cisco & Sapio Research, 2026 State of Industrial AI Report, survey of 1,000+ operational technology decision-makers across 19 countries and 21 industrial sectors. https://www.rcrwireless.

com/20260408/ai/industrial-ai-cisco-2026

[2] CISA, ACSC, BSI (Germany), NCSC-UK and partners, Principles for the Secure Integration of Artificial Intelligence in Operational Technology, December 2025. https://www.cisa.gov/resources-

tools/resources/principles-secure-integration-artificial-intelligence-operational-technology

[3] Bitdefender, published July 2026; 47.4% of respondents report only partial or no visibility into shadow AI tools and personal accounts used for work. https://www.securitymagazine.com/

articles/102399-nearly-half-of-organizations-lack-full-visibility-into-employee-ai-usage

Orientation document, August 2026. General information only, not legal advice. Applicability of any regulation depends on your systems, sector and role in the supply chain.