With our last LinkedIn article, “Balancing Transparency and Intellectual Property: Obligations of Data Processing Service Providers in the Medical Device Industry”, we were asked about a reliable method to prove data leakage through AI.
This is a critical issue, particularly in industries such as healthcare and MedTech, where data security is strictly regulated. Our article focuses on controlling sensitive data—such as patient health information (PHI) or intellectual property (IP)—at a sub-processor providing AI services and how these controls can be established.
Mitigating Data Loss Risks in AI Sub-Processing
Data loss poses a significant risk for a sub-processor, but several mitigation measures can be implemented by a data controller:
- Retain sensitive information within the data controller’s premises and provide only a copy to sub-processors. This enables the data processor to anonymise sensitive data using data anonymisation solutions, thereby minimising the impact of potential data leaks at the sub-processor’s level.
- Implement contractual security controls and a “need-to-know” security protocol for services provided by sub-processors. This includes regular verification of current security certifications (e.g. SOC II reports, ISO 27xxxx certificates).
- Enforce stricter penalties for AI misuse. The U.S. Department of Justice has recently published guidance regarding corporate compliance in AI programmes (ECCP Revision 2024 0922 FINAL CLEAN.pdf).
Methods for Detecting AI-Related Data Leaks
To determine whether sensitive information has been leaked via AI, it is essential to clarify whether the concern is detecting data alteration (unauthorised modifications, such as AI flow-breaking attacks) or detecting data loss within an organisation’s own premises.
Here are some key methodologies that can support data leakage detection in AI systems:
- Data traceability and watermarking
Embedding unique markers within datasets allows organisations to track their use in AI models and identify unauthorised access or misuse. In highly regulated environments such as MedTech, this approach strengthens accountability and transparency. - AI model behaviour analysis
Analysing how AI models process data and reviewing their outputs for patterns that could reveal sensitive information serves as an indirect method for identifying leakage risks. Data controllers and sub-processors can integrate these checks to enhance client trust. - Differential privacy for data protection
Employing differential privacy techniques ensures that individual data points are obfuscated, reducing the risk of sensitive information being exposed through AI outputs. This approach aligns with GDPR and other global data protection standards. - Regulatory-driven insights
Regulations such as GDPR, HIPAA, and the EU AI Act emphasise transparency in AI. Ensuring compliance with these frameworks not only mitigates risks but also provides structured guidelines for addressing data leakage concerns.
By implementing these strategies, organisations can strengthen their AI security measures while maintaining compliance with global regulatory standards. Get in touch with us today to safeguard your sensitive data and stay ahead of regulatory challenges: info@eumediq.eu.