With the rise of AI, data protection challenges are evolving alongside new technologies that threaten and protect enterprise data assets. When training AI, the large amounts of data utilized by AI models pose new and unique data protection challenges that require innovative solutions.
To update your enterprise data protection strategy to address AI training data needs, you must first understand the specific challenges and solutions associated with training AI models.
What is AI training data?
AI training data refers to the data used to train generative AI models. These models typically analyze vast amounts of information to recognize patterns and trends and use them to create new content. Model performance is often improved by adding more relevant data that is accurate and governed by easily identifiable standards.
AI data protection challenges
AI training data is often reused from existing data sources. For example, companies are building models using data originally generated for other purposes, such as emails, IT tickets, customer support conversations, or even legacy data such as weather forecasts or historical supply chain distribution timelines. May be trained. This approach helps the model to better understand the context and optimize various business processes.
That said, the data that organizations use for AI training purposes is different from the data that exists in other contexts, which poses unique challenges.
-
amount of data: AI training data is typically massive, often requiring millions or even hundreds of millions of records, including unstructured data such as images, videos, audio files, and documents. Safely protecting such huge amounts of data is a major challenge.
-
different data types: AI training data can contain diverse types of information, making it difficult to assume uniformity across all data records or to support specific technologies without adaptation. .
-
Discontinuous use: AI training data, unlike operational data, is not used continuously. This is only required during active model training, with intermittent retraining using the same data at later points. Storing this data cost-effectively for future use is paramount.
-
Confidential information: AI training data often includes sensitive information such as personally identifiable information (PII) related to customers, vendors, and employees. Appropriate security and compliance measures must be taken to protect this data from unauthorized access and misuse.
How to effectively protect AI training data
To create a data protection strategy for your AI data, start by implementing the following basic data protection practices that are important for all types of data:
-
Encrypt your data end-to-end. Encrypting data at rest and in transit is a fundamental data protection measure. Even if you expect your data to remain within your organization during training, encryption provides an added layer of security in case of unauthorized access.
-
Data access logging and monitoring: Tracking and monitoring data access helps detect unauthorized activity and potential security threats.
-
Back up your data comprehensively. A robust backup strategy ensures that your training data can be restored in the event of accidental or intentional loss. This is important for continuous retraining.
-
Manage third-party data access. Ensuring compliance and auditing data access becomes more complex when external vendors handle AI training and model management.
In addition to these basic measures, several additional data protection strategies can help protect your AI training data.
-
Minimize data: Collecting and utilizing only the data necessary for a specific AI application. For example, if you are training using emails and only certain emails are relevant, filter out the remaining emails as irrelevant. This approach speeds up training operations (because there is less data to process), reduces the amount of data for backup, and minimizes data loss in the event of a breach.
-
Data compliance strategy: It is essential to identify the compliance and regulatory requirements that the required training data must adhere to. Apart from the usual standards for sensitive information, rapidly changing AI regulations may dictate how training data is managed or stored.
-
secure data storage: With large amounts of AI training data, companies often opt for cost-effective solutions commonly found in cloud storage services. However, it's important to choose a cloud storage provider that offers strong security features, such as encryption, network security measures, and compliance with industry standards and certifications (such as ISO 27001 and SOC 2). To avoid putting your data at risk, prioritize data security over choosing the cheapest storage option.
-
Managing third-party vendor risk: If your AI strategy includes giving external vendors access to training data, establish clear policies regarding acceptable uses of the data. Additionally, assess internal security controls, policies, and incident response capabilities. Keep in mind that you can be held liable for compliance or security incidents that result from improper use of your data, even by a third party. Even if your training data is managed by an external organization, it's important to prioritize data protection.
As AI becomes more pervasive, the need to manage and protect AI training data increases. The unique challenges posed by AI data protection are clear. A smart first step for companies is to devise a security-enhancing strategy to protect this critical AI data, similar to how companies protect other internal data. Companies should then consider how AI can enhance their existing security strategies through tools and accurate and efficient threat detection capabilities. This assessment allows businesses to be ready to take advantage of AI and have peace of mind that their AI training data is well-protected.