Preventing Data Poisoning in AI

In spite of numerous benefits AI/ML technologies offer to business, cyber criminals now turn to AI/ML to launch attacks themselves. Data poisoning is a crucial problem for cyber security professionals and becoming more vicious than conventional attacks. Here attackers try to make the inputs accepted into training data hence affecting the ability of system to produce accurate predictions. Malicious data will be introduced deliberately by attackers into an ML system training data and manipulate the systems behavior or cause it to produce inaccurate results.

To prevent data poisoning in AI, organizations can consider below points:

Ensure Data Integrity: It’s essential to establish data governance and make sure that the data used to train machine learning models is trustworthy and free from any malicious attacks. This can involve using encryption, access controls, and other security measures.

Data Validation: It’s essential to validate the data before it enters the AI system. The data should be checked for accuracy, consistency, and quality.

Monitor Data Inputs: To detect and prevent data poisoning attacks, it’s crucial to monitor data inputs carefully. This includes monitoring the source of the data, the types of data being used, and any unusual patterns or trends.

Data Filtering: Filtering the data before it enters the system can help prevent malicious data from entering the AI model. This involves removing data that doesn’t meet the quality standards.

Data Diversification: Diversity in the data can help prevent data poisoning. It’s important to use a diverse set of data to train the AI model to reduce the risk of an attacker manipulating a specific dataset.

Implement Outlier Detection: Outlier detection can help to identify and flag any data points that are significantly different from the norm. This can be used to prevent malicious data from being introduced into the system.

Conduct Regular Security Audits: Regular security audits can help to identify any vulnerabilities or weaknesses in the machine learning system. This can help to prevent data poisoning attacks and improve overall security.

Access Control: Access control mechanisms should be in place to restrict access to the data used to train the AI model.

Robust Models: Building a robust AI model can help prevent data poisoning. A robust model can identify and ignore malicious data or learn to adapt to changes in the data.

Frequent Label Checks: Scientists who develop AI models should frequently check whether labels used in the training data are accurate.

Open Source Data Usage: Though usage of open source data makes the development of accurate models easier but such models will be a lucrative target for attackers.

Penetration Testing: Penetration testing and offensive security testing can find vulnerabilities that provide outsiders access to data training models.

Additional Layer of AI/ML: Designing a second layer of AI and ML to seize potential errors in data training can also be considered.

Awareness Of Employees:  Attackers often rely on employee’s unawareness to invade into a company. Employees need to be trained against traditional social engineering attacks such as phishing.

Data Poisoning is a major area of concern. Proper planning should be put in place to prevent attacks as much as possible.

Author Details

Sajin Somarajan

Sajin is a Solution Architect at Infosys Digital Experience. He architects microservices, UI/Mobile applications, and Enterprise cloud solutions. He helps deliver digital transformation programs for enterprises, by leveraging cloud services, designing cloud-native applications and providing leadership, strategy, and technical consultation.

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