Executive Summary - GU Brief information for companies 11.2025
AI is increasingly becoming the backbone of industrial production processes, chemical plant control systems and medical diagnostics. At the same time, the attack surface for new, highly developed cyber threats is growing. Data poisoning - the targeted manipulation of training or operational data - is now considered one of the most critical attack vectors on AI systems.
The consequences range from production downtime and quality defects to misdiagnoses and environmental and patient hazards. In regulatory terms, the GDPR, NIS2, the IT Security Act 2.0 and the EU AI Act are significantly tightening the requirements and liability risks. Companies must embed data poisoning protection as an integral part of their AI strategy.
1. introduction
The use of AI is growing in all critical sectors:
- Industry/Industry 4.0: process automation, robotics, predictive maintenance
- Chemical industry: process monitoring, reaction optimization, emission control
- Healthcare: Diagnostics, image analysis, clinical decision support
While traditional cyberattacks target systems or networks, data poisoning attacks directly attack data integrity - the core of every AI model. Modern malware uses AI itself to place manipulations in an automated, scalable and inconspicuous manner.
Data poisoning is therefore a strategic risk today and not just a technical problem.
2 Functionality and types of data poisoning
2.1 Definition
Data poisoning refers to the deliberate modification of data in order to corrupt the behavior of an algorithm or AI model. The aim can be sabotage, disinformation, manipulation or covert control.
2.2 Typical types of attack
Label Poisoning
Incorrect assignment of training labels (e.g. defective products are marked as "OK").
Feature Poisoning
Imperceptible changes to individual characteristics that change the model behavior in the long term.
Backdoor Attack / Trojanization
A "trigger" in the data stream triggers specific wrong decisions - particularly dangerous in robotics or diagnostics.
Online Poisoning
Manipulation of real-time data such as sensor data, measured values or monitoring streams.
3. relevance for industry, chemistry and healthcare
3.1 Industrial sector
Industry 4.0 relies on networked sensors, autonomous systems and learning models. Data poisoning can:
- Falsify product quality
- Trigger robot misbehavior
- Manipulate maintenance systems
- Cause system downtimes
- Destabilizing supply chains
Edge AI, IoT devices and continuous learning models are particularly at risk.
3.2 Chemical industry
Data poisoning is particularly critical here, because even minor data errors can lead to:
- Overpressure/underpressure situations
- thermal runaways
- toxic intermediates
- incorrect emission measurements
- False alarms or non-detection of hazards
can lead to process errors. Backdoor-based sabotage is particularly dangerous here, as it leads to unnoticed process deviations.
3.3 Healthcare
AI models are used for:
- Medical image analysis
- Cancer diagnostics
- Drug development
- Clinical decision support
- Patient monitoring
Manipulated data can lead to:
- Misdiagnoses
- incorrect therapy recommendations
- clinical risks
- financial liability cases
can lead to a risk. In addition, medical data contains highly sensitive personal information - a double risk of data and health protection.
4. AI-supported malware as an attack vector
Modern threats use AI for automation:
- Identification of suitable data sources for manipulation
- iterative adaptation of the attacks based on the model response
- Camouflage through statistical normalization
- Deepfake data generation
- Adaptive backdoor anchoring
This makes data poisoning attacks more precise, less conspicuous and suitable for mass use.
5 Regulatory and legal consequences
5.1 Data protection law (GDPR)
Data poisoning is a data breach as soon as personal data is affected. Consequences:
- Fines of up to € 20 million or 4% of global turnover
- Obligation to report to authorities within 72 hours
- Claims for damages by affected parties
- Proof of "appropriate technical and organizational measures"
Particularly relevant in healthcare, but also in production-related HR processes with AI.
5.2 EU AI Act
Many AI systems in industry, chemistry and healthcare are considered high-risk AI. Companies must:
- Fully document data quality and data origin
- Analyze and mitigate risks
- Implement tamper protection
- Keep track of logs and model versions
- Ensure continuous monitoring
Violations may result in liability risks and product bans.
5.3 NIS2 / IT Security Act 2.0
Companies in critical infrastructure must:
- Treat AI systems as security-relevant assets
- Provide incident response plans for AI threats
- undergo annual audits
- Report IT security incidents
Data poisoning can be considered a breach of cyber security regulations.
5.4 Criminal law
In the worst case, data poisoning can lead to:
- Health hazards (e.g. incorrect diagnoses)
- Environmental hazards (chemical reactions)
- Industrial accidents
lead. Managers can be held liable in accordance with §130 OWiG if organizational or technical duties of care have been breached.
6 Strategic impact on the company
Operative
- Production losses
- Quality problems
- Robotics/automation error
- Incorrect maintenance decisions
Financial
- Direct damage repair costs
- Contractual penalties
- Liability and recourse claims
- Investment risks
Reputation
- Loss of trust among customers, patients, partners
- Negative media coverage
- Regulatory monitoring
7 Prevention and protective measures
7.1 Technical measures
- Signing and hashing of all data sources
- Zero-trust architecture for data pipelines
- adversarial robustness testing
- Model monitoring and anomaly detection
- Backdoor detection (neuron activation clustering, spectral signatures)
7.2 Organizational measures
- AI governance framework
- Define roles and responsibilities (data owner, model owner)
- Training for employees in dealing with AI risks
- Version and documentation obligations
7.3 Regulatory measures
- Preparation for EU AI Act compliance
- Involvement of legal departments and data protection officers
- Regular compliance audits
8. recommendations for action for top management
- Strategic prioritization of AI security AI security must be part of the corporate strategy, not just the IT department.
- Establishment of an AI security and data integrity program Based on BSI/ENISA best practices.
- Anchoring AI risks in Enterprise Risk Management (ERM) Data poisoning as an explicit risk.
- Investment in cybersecurity technologies for AI Special tools for data validation, model robustness, monitoring.
- Preparation for future regulatory audits, particularly in the context of the EU AI Act and NIS2.
9. conclusion
Data poisoning by AI malware is one of the most significant threats to companies in the industrial, chemical and healthcare sectors. The combination of increasing system complexity, high regulatory requirements and potential personal injury and environmental damage makes the issue security-critical.
Companies must therefore invest proactively in:
- Robust technical AI safety measures
- organizational governance
- Regulatory compliance
- Continuous monitoring of data integrity
This is the only way to ensure that AI systems are operated reliably, securely and legally compliant.
If you have any questions about planning and implementing the necessary compliance (technical and legal) in your company, please contact us for an initial assessment.
Alexander Goldberg
Lawyer
Specialist lawyer for intellectual property law
Specialist lawyer for information technology law (IT law)
Lecturer at the University of Wuppertal
for intellectual property and competition law
at the Schumpeter School of Business and Economics
