Today’s growing digitization & automation across various sectors & industries is because of the rapid growth of AI. Artificial Intelligence (AI) has revolutionized the automation industry, and every company is adopting it. The advancements in AI have paved the way for various business operations, finance, healthcare, and software development – leading to efficiency and creating new opportunities. But, with every new technology comes the responsibility to protect customer data and other assets associated with the firm or the users.
Over the past 2-3 years, the growth of AI data leakage has spiked exponentially. That is concerning, as AI relies on vast data for model training and self-learning. Data leakages by AI can compromise the potential of AI in use and the privacy of customers’ sensitive data. This article is a complete walkthrough of AI data leakage, its types, potential impact, consequences, preventive measures, and best practices to prevent AI data leakages.
What is AI data leakage?
Data leakage refers to a situation wherein sensitive data gets leaked or exposed unintentionally or accidentally to unauthorized individuals. Sensitive data leakage, when exposed by AI from its model’s dataset, is called AI-based data leakage. AI systems often encounter situations where information outside the training dataset unintentionally influences an AI model. Such AI systems can cause overfitting or biased predictions. Data leakage by AI can also occur if the model has access to real-world customers’ or users’ details which is not ethical normally. Many companies use real-world user data to train their model for perfection.
It was in the news when OpenAI’s ChatGPT revealed sensitive company data by sharing legit Windows 10 product keys with users. Another incident reveals news where security researchers demonstrated how they could trick Slack’s AI into leaking data from private channels via prompt injection. Thus, many companies like Amazon and IBM strictly notified their employees not to share sensitive employee details or other internal company reports to any generative AI.
AI data leakage also comprises users’ personally identifiable information (PII), financial records, behavioural data, and transactional histories. AI-driven sensitive data exposure happens when companies train AI models by inadvertently accessing, using, or exposing data in ways that conflict with users’ privacy & security policies.
Types of AI Data Leakage
Data leakage through AI is subtle. They come in various forms. The categorization depends on how AI leaks the data. Here are some of the most common types:
1. Target leakage
It occurs when AI models inadvertently learn from features or data that hold information about a target variable. For example, taking user data and learning when to take loans or predict loan default risks.
2. Training test data leakage
This type of AI data leakage occurs when AI leaks sensitive information from the test dataset. It happens because of improper data handling, inappropriate algorithm designs, and misuse of actual user data.
3. Cross-validation leakage
This type of AI-based data leakage occurs when data points do not completely separate between training & validation splits. It often cause data in one fold to influence predictions in another.
4. Feature-based data leakage
This type of data leakage arises when the company should not have released a feature or characteristic functionality associated with the AI model that logically releases user data or skewed model results.
Potential Risks and Impacts Associated with AI Data Leakage
Data leakage can cause various conflicts and loss of customers at large. Here are some impacts associated with data leakage when caused by AI systems.
1. Compliance-related risks
Companies whose AI models leak sensitive user data might face lawsuits and legal notices because of sensitive customer data leakage. Thus, it can conflict with corporate compliance like GDPR and HIPAA.
2. Loss of customers' faith
No customer will accept the fact that AI will leak their data. This directly implies that the company responsible for creating the AI model is using customers’ actual data rather than dummy data. It directly impacts business reputation and customer loyalty.
3.Vulnerable to cyberattacks & prompt injection
Sensitive AI data exposure can become a lucrative target for cybercriminals who can exploit vulnerability for identity theft, phishing attacks, or fraud. Cybercriminals can also use prompt injection to reveal sensitive customer data in bulk.
4. Financial consequences
Because of the loss of customers’ trust, the overall business ROI will be affected. Highly regulated sectors like healthcare & banking sectors can face a financial setback because of AI-based data leakage.
Preventive Measures for AI Data Leakage
To mitigate AI-based data leakage in AI systems, AI engineers & security professionals should deliver AI models or AI algorithms with utmost care. They should also integrate model training protocols, data governance techniques, and other data privacy strategies to prevent data leakage.
1. Data Anonymization
AI can leak data during the model training phase during the AI system development. When outputs of the model training become accessible by unintended parties, it can lead to mass data leakage. That is when the data anonymization technique comes in. By anonymizing data, sensitive details like Personally Identifiable Information (PII) such as names, contact information, and financial records are removed or transformed into something dummy. Thus, it becomes difficult to trace back to any individual. It provides a shield against unintended exposure in case of model access by unauthorized users or external attacks. Preventing data leakage through anonymization uses techniques like data masking, generalizing data, pseudonymization, differential privacy, and suppression.
2. Encrypting data
Another way to protect data leakage by AI is through cryptographic methods. Using encryption, AI companies can safeguard sensitive customer data from leakage by AI. Even in the worst case, the data gets leaked & will reside in an encrypted format (also called cipher text), which is not readable unless decrypted. Encryption provides a security layer for both data at rest and in motion.
3. Data Governance Policies & Access Control
Companies should limit access to customer data. Security professionals & AI engineers should pay attention to data governance by implementing restrictions through role-based access controls (RBAC) and regularly auditing access permissions. It will ensure that all data handling practices adhere to privacy policies. Also, enforcing access control frameworks can protect sensitive customer data across various AI workflows.
4. Leveraging Federated Learning
With federated learning, AI systems can minimize data movement. Federated learning enables AI models to train across different devices, servers, or systems without transferring data to a centralized position. Thus, with lesser data movement, the risk of data leakage also reduces significantly. The federated learning data management architecture ensures customer data stays locally, on-premise, or in one position while supporting AI development. Since data do not stay centrally, cybercriminals cannot perform data breaches by directly targeting any centralized data repository.
5. Deploy Automated Leak Detector Systems
Many enterprises have started leveraging data leak detectors and algorithms in their AI systems. By integrating these automated systems during AI system development or model building, AI engineers and security professionals can easily identify and flag potential leakage sources before deployment. Thus, it helps data scientists and AI security experts take corrective measures. Many platforms that support AI development offer detection tools like DataRobot, Amazon SageMaker, Azure Machine Learning, etc.
AI Data Management Best Practices
To minimize AI data leakage risks and ensure robust AI model performance, enterprise AI engineers and security professional should consider these best practices for data management:
- Data minimization is one of the first best practices AI companies should follow to minimize AI data leakage. AI engineers and security professionals should be intentional about what data the AI systems are using for model development & why rather than collecting as much data as possible.
2. Enterprises creating, deploying, or using AI solutions should prepare themselves with an incident response plan that can help identify threats, prepare detection mechanisms, and define prevention measures to respond, investigate, and recover from AI data leakage incidents.
3. Security professionals should also use adversarial learning techniques as a best practice to train ML models using real-life fraud datasets. It will help AI systems identify security threats on their own.
4. Another best practice for defending against AI data leakage is to perform regular audits to adhere to compliance. Enterprises should perform security audits for data safety that will ensure the storing, processing, and sharing of data under compliance & standards like GDPR and CCPA.
Conclusion
We hope this article delivered a comprehensive guide to AI data leakage. It also highlighted the various types of AI data leakage and potential risks associated with data leakage. Then we came across the preventive measures & some best practices enterprises should follow to stay ahead of such incidents. Enterprises that build AI systems or train models should remain cautious about what type of data AI models use for model training and development. It is essential to protect customer or user data and prevent them from leakage. At VE3, we are committed to helping enterprises safeguard their AI systems and ensure the integrity and security of their data. For more information or to discuss how we can support your organization in preventing data leakage, visit us or contact us directly.
