The Enemy Within: Data Poisoning

Blindly Fed – How the Poison Gets Into the Data 

AI models are based on data-driven learning. During the training phase, they analyze large amounts of data to identify patterns, correlations, and probability distributions. This is precisely where attackers come in: by introducing deliberately manipulated or misleading data, they can influence the AI system. Unlike traditional hacks, such attacks are difficult to detect. Often, the manipulation goes unnoticed until the system makes inexplicable or even dangerous decisions or statements in productive use.

The most dangerous attacks can be divided into two main categories: manipulation during the creation of training data and targeted poisoning via external data sources during operation. Systems that constantly learn from new data – such as in customer service, lending, or automated decision-making processes in healthcare – are particularly vulnerable. A key challenge in the use of AI systems is their lack of transparency. Their decision-making processes are difficult for outsiders to understand. This not only makes it difficult to validate results, but also to detect targeted manipulation at an early stage. 

Which Industries are Most at Risk?

The threat of Data Poisoning affects almost all industries. However, the focus is particularly on industries in which AI is involved in security-related or business-critical decisions. These include: 

Finance

Algorithms for fraud detection can be deliberately weakened by manipulated data. Data Poisoning can result in fraudulent transactions being classified as unremarkable and not detected. 

Healthcare

AI systems manipulated by poisoned data could consistently recommend certain therapies or medications from specific providers.

Defense & Security

Manipulated training data can result in enemy targets not being detected or civilian objects being falsely classified as threats. This weakens the operational capability of security-critical systems and endangers human lives in an emergency.

Antidote: Red Teaming and Robust Data Management

Traditional IT security management is no longer sufficient to protect against such attacks. AI requires its own security strategies and new ways of thinking. Two approaches are particularly promising:

Robust Data Management

Companies need to know exactly where their training data comes from, how it was collected, and whether it is trustworthy. This includes validation by multiple data sources, the use of synthetic test data, and the targeted exclusion of manipulable inputs.

Red Teaming for AI

Here, so-called red teams take on the perspective of potential attackers to specifically uncover vulnerabilities in the training data, model architecture, or application interfaces. These tests must be realistic, creative, and continuous, because attackers and threat scenarios are also evolving. Red teaming helps to review and consolidate organizational and regulatory security goals.

Regulatory Requirements and Ethical Perspectives

In addition to transparency and documentation requirements, the EU AI Act also calls for risk assessments and mechanisms to prevent errors. Companies that use AI will therefore have to prove in future that their systems are safe, fair, and transparent. This applies in particular to so-called high-risk systems.

A key aspect here is protection against targeted manipulation, such as Data Poisoning. To counter these risks, proactive testing methods such as red teaming are becoming increasingly important: they help to identify vulnerabilities in data, models, and interfaces at an early stage. Ethical requirements are also associated with this. This is because faulty or manipulated AI decisions can harm people, reinforce discrimination, or destroy trust in digital systems. Companies must therefore assume not only technical but also moral responsibility: through fairness checks, algorithm transparency, and conscious handling of data.

Conclusion: Vigilance is the Best Protection

One of the biggest security threats to AI systems is Data Poisoning. To protect themselves, companies need to develop new security strategies, understand attack vectors, and continuously test their models.

Red teaming, robust data management, and compliance with regulatory standards are not just exercises for emergencies, but form the foundation for the safe, trustworthy, and responsible use of AI.

We’ve helped organizations strengthen their AI security through effective red teaming and robust data management strategies. Looking to protect your AI? Reach out today for a free consultation.