Algorithmic sabotage: the risk of data poisoning in in-house AI models

The use of Artificial Intelligence in business environments is growing at a very high speed: 92% of financial institutions already use it in critical operations, and more than 87% of organizations suffer AI-powered cyberattacks.

In this scenario, data poisoning or algorithmic sabotage has become one of the most advanced threats. Manipulating the data used by a model can alter its behavior and lead to incorrect decisions with a direct and critical impact on the business.

What data poisoning is in Artificial Intelligence models

Data poisoning is a cyberattack technique that consists of introducing false or manipulated information into the data used by an Artificial Intelligence system for training. Unlike more direct attacks, such as prompt injection in chatbots, its goal is to silently alter the model’s behavior so it makes incorrect decisions without being detected.

When an AI learns from compromised data, it can automate errors, approve fraudulent operations, ignore security risks, or generate incorrect business decisions, with a direct impact on operations and organizational continuity.

How to identify and detect sabotage in your data pipelines

Detecting a data poisoning attack is particularly complex because the Artificial Intelligence continues to operate normally on the surface. However, there are several indicators that may signal possible manipulation:

• Sudden drop in model accuracy: an unexpected reduction in prediction or output quality may indicate that the AI is learning from altered data.
  
  
• Anomalous responses or decisions: if the system starts generating unusual behavior for certain profiles, operations, or scenarios, it may be applying rules that were not part of its original configuration.
  
  
• Unauthorized access to databases: attackers typically need access to datasets or the development environment before manipulating the information used by the AI.
  
  
• Unexpected modifications in data used by the AI: unexplained changes in records, labels, or data sources should be investigated to rule out malicious alteration.
  
  
• Anomalous data loading or update patterns: sudden increases in data volume or updates coming from unusual sources can be a warning sign.

Continuous monitoring of these indicators helps identify potential sabotage attempts before compromised data affects the performance and behavior of the Artificial Intelligence system.

Essential steps to contain an attack against your AI

If it is confirmed that the data used by your Artificial Intelligence model has been manipulated, the first hours are critical to contain the incident and reduce operational, legal, and reputational impact. Key actions include:

Isolate the model and stop automated processes

Disconnect affected systems from the production environment and suspend any automated model update or training workflows. This prevents altered data from continuing to influence the system or spreading to connected environments.

Audit data traceability

Review the change history to identify what information was modified, when it happened, and which users or processes were involved. This helps locate the origin of the incident.

Roll back to a verified system version

Restore the model to a previous secure version using backups and validated data, ensuring no corrupted information is included.

Analyze the scope of the incident

Determine which decisions, processes, or business areas may have been affected while the model was compromised.

Notify and document the breach

Record the incident in detail and, if personal or financial data is affected, notify the relevant authorities within the 72-hour legal timeframe.

Strengthen controls and monitoring

Once the situation is contained, reinforce access controls and monitoring systems to reduce the risk of recurrence and improve early anomaly detection.

Key recommendations to prevent data poisoning

To protect Artificial Intelligence models, it is essential to adopt a Zero Trust approach, validating every piece of data before it is used. This reduces the risk of manipulated information influencing system behavior.

In addition, strengthening access control and data encryption is critical, along with regular audits and the use of cryptographic signatures. These measures help detect unauthorized changes and ensure data integrity at all times.

ESED, assess your exposure to cyberattacks

At ESED, we work with a fixed monthly fee that includes proactive services designed to keep systems continuously protected and operational. This model enables organizations to anticipate security incidents and reduce reliance on reactive actions or unexpected costs.

You can also assess your company’s readiness against a cyberattack through our cybersecurity assessment test. No advanced technical knowledge is required, and it consists of 36 questions based on industry standards, designed to identify your real level of exposure and security maturity.