The invisible danger: How Data Poisoning can turn AI into a threat

Published on 4 March 2025

In a world where artificial intelligence (AI) is key to technology, the quality of the data that feeds it becomes crucial. But what happens when this data is manipulated? This is where "data poisoning" comes into play, a silent threat that can transform AI into a latent danger.

What is Data Poisoning?

Data poisoning is a cyberattack technique that involves the malicious manipulation of data used to train AI systems. It is similar to subtly but deliberately altering the foundation of a building, affecting its stability. An attacker could insert erroneous or biased information to distort the way AI makes decisions, which could lead to failures in fraud detection, security systems, or medical diagnoses.

How the attack works

AI systems learn by identifying patterns from large volumes of data. If these data are corrupted, the models generate incorrect results. Data poisoning attacks can be classified into three main types:

• Availability Attacks: These reduce the model’s effectiveness, making it less accurate or unusable.
• Integrity Attacks: These introduce specific vulnerabilities that can be exploited under certain circumstances.
• Backdoors: These create hidden access points that allow attackers to control the system in the future.

Real-world case: The AI that didn't protect the money

Imagine a financial system based on AI that analyzes millions of transactions to detect fraud. An attacker manages to modify the training data so that certain fraudulent activities are interpreted as normal. When the model is deployed, malicious transactions go unnoticed, allowing millions of dollars to be stolen undetected.

This type of attack does not only affect the financial sector. In healthcare, it could lead to incorrect diagnoses, and in cybersecurity, it could allow threats to enter by ignoring suspicious patterns in network traffic.

Protection against Data Poisoning

While the risk is real, there are strategies to mitigate this threat:

1. Data Validation: Implement audits and advanced analyses to ensure the integrity of the information used in AI training.
2. Active Monitoring: Continuously assess the model’s behavior to detect suspicious deviations.
3. Robust Models: Apply training techniques that minimize the impact of corrupted data.
4. Federated Learning: Distribute AI training across multiple nodes to prevent a single compromised data source from affecting the entire system.

The future of secure AI

Data poisoning reminds us of the importance of security in AI development. Companies, governments, and users must work together to ensure data integrity and model resilience. In a world where AI is becoming increasingly influential, the question is not if these attacks will happen, but whether we are prepared to face them.