Best Practices for AI Training Data Protection

Data volume: AI training data is typically massive, often requiring millions or even hundreds of millions of records, including images, videos, audio files, or unstructured data like documents. Securing and protecting such a vast amount of data is a significant task. 

Disparate data types: AI training data can encompass diverse types of information, making it challenging to assume uniformity across all data records or to support specific technologies without adaptation. 

Non-continuous use: AI training data isn’t continuously used unlike operational data. It’s only needed during active model training, with intermittent retraining using the same data at later points. Cost-effectively storing this data for future use is paramount. 

Sensitive information: AI training data often includes sensitive information, such as personally identifiable information (PII) related to customers, vendors, or employees. Proper security and compliance measures must be in place to protect this data from unauthorized access or misuse. 

Encrypt data end-to-end: Encrypting data at rest and in transit is a fundamental data protection measure. Even if your data is expected to remain within your organization during training, encryption adds an extra layer of security in case of unauthorized access. 

Log and monitor data access: Tracking and monitoring data access helps detect unauthorized activities and potential security threats. 

Comprehensively back up your data: A robust backup strategy ensures the recovery of training data in case of accidental or deliberate loss, crucial for ongoing retraining. 

Manage third-party data access: When external vendors engage in AI training or model management, ensuring compliance and auditing data access becomes more complex. 

Data minimization: This involves collecting and utilizing only the necessary data for a particular AI application. For instance, if you’re training using emails but only certain emails are relevant, filter out the remainder as irrelevant. This approach can accelerate training operations (due to less data to process), reduce the data volume for backup, and minimize data loss in case of a breach. 

Data compliance strategy: Identifying the compliance and regulatory requirements necessary training data must adhere to is essential. Apart from the usual standards for sensitive information, rapidly changing AI regulations may specify ways to manage or store training data. 

Secure data storage: Due to the high volume of AI training data, businesses often opt for cost-effective solutions, commonly found in Cloud storage services. However, it's crucial to select a Cloud storage provider that offers strong security features, including encryption, network security measures, and compliance with industry standards and certifications (like ISO 27001 or SOC 2). Prioritize data security over choosing the cheapest storage option to avoid putting your data at risk. 

Managing third-party vendor risk: If your AI strategy involves granting external vendors access to training data, establish clear policies regarding the permissible uses of your data. Additionally, assess their internal security controls, policies, and incident response capabilities. Remember, you could be held accountable for compliance or security incidents resulting from inappropriate use of your data, even if it’s by a third party. It’s crucial to prioritize data protection even when your training data is managed by an external organization.