AI and Data Privacy: 3 Things You Need to Know

Artificial intelligence (AI) and data privacy is an emerging field that grows more important by the day. Properly protecting sensitive AI and machine learning (AI/ML) data within existing tech stacks remains a top concern for development and testing teams worldwide. 

The stakes for AI and data privacy are high. Cyber breaches are rising. Regulatory bodies enact new privacy regulations each year. And concerns are rising as the AI/ML boom continues. Businesses like yours must be aware of key AI data privacy challenges. Read on to learn what these challenges are and how to address them.

Businesses’ 3 Largest AI Privacy Concerns

Delphix by Perforce surveyed 200+ global leaders in The State of Data Compliance and Security Report. One of the report’s key purposes was to examine leaders' AI data privacy concerns.

The results were interesting. Over 82% of respondents expressed concerns about the following key areas:

1. Theft of model training data

2. Regulatory non-compliance

3. Personal data re-identification

Data theft, non-compliance, and data re-identification challenges face enterprises in many areas of their work. But AI and data privacy bring additional complications to these concerns. Knowing the unique AI-related issues that overlay these general concerns will help your business thoroughly address them.

1. AI Privacy Concerns Are Most Dire in AI Model Training Data 

In The State of Data Compliance Report, 82% were concerned with AI model training data. Model training data is essential to enterprises. It serves as a company’s intellectual property (IP) behind its AI/ML workflows. This makes it extremely valuable to businesses.

But AI environments are particularly at risk to internal bad actors. AI environments are already less structured than other non-production environments. This means that teams with less strict data processes and policies can access AI environments. Businesses must be vigilant with their model training data to ensure that it isn’t compromised.

AI/ML model training data also usually contains sensitive data. This makes it especially vulnerable. At face value, AI/ML model data theft endangers its competitive edge. But with sensitive data included, model training data theft produces even more issues with non-compliance.

2. AI and Data Privacy Compliance Will Only Grow Stricter

Another concern with model training data (and other AI training data) is that data privacy regulations strictly govern it. Indeed, 85% of respondents reported concerns with regulatory non-compliance in their AI environments.

Compliance is a major concern among businesses as-is. Non-compliance penalties can be severe. They include hefty fines, potential prison time, and damaged reputations. But AI complicates compliance, because AI is still in an immature regulatory space. Very few regulations specifically govern AI. In August 2024, the European Union enacted the AI Act, which it calls “the first-ever legal framework on AI.”

But plenty of data privacy regulations govern the data that feeds AI models. And future AI-specific laws are almost guaranteed to emerge. So, organizations must be flexible and nimble with managing data privacy in AI. It will most definitely grow stricter.

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3. Irreversible Anonymization: The Best Way to Ensure the Data Privacy in AI Environments

83% of respondents to The State of Data Compliance and Security report express concerns about personal data re-identification in AI environments. 

This concern highlights a key point in data privacy: whether to allow re-identification of data in anonymization processes.

Some anonymization techniques (like tokenization) are reversible. This means the data can be re-identified — by employees or bad actors. And some, like dynamic data masking, leave the underlying data unchanged, and thus still vulnerable to attack. 

But these methods open key security gaps. Bad actors could access an AI environment to steal tokenized training data. Or they could trick an AI model into exposing that data. If bad actors steal a business’s tokenized model training data and gain access to the tokens, they could reverse the process and gain access to the business’s sensitive data. This would damage the business’s competitive edge and open it to legal action. 

Other methods, like static data masking, are irreversible. Irreversible anonymization is the most secure form of data protection. It prevents all bad actors from re-identifying the data. And teams can generally decipher masked data points by using other data on hand. 

How to Relieve AI Privacy Concerns

By adopting irreversible techniques like static data masking, your business can relieve re-identification concerns. But you can also thoroughly secure your model training data. And you can future-proof your AI environments against compliance that way.

Static data masking is a popular method for irreversibly masking data. In The State of Data Compliance report, 89% of respondents said they have used static data masking. 66% currently use it, while 23% used it in the past.

Static data masking replaces sensitive values with fictitious, yet realistic equivalents. With static data masking, data is changed and written to the data source. There is no path back to the original data.

Delphix: Your Partner in Ensuring AI Data Privacy

Delphix offers a comprehensive data compliance and security solution that reduces data risks by delivering compliant data via static data masking. 

Delphix enables users to automatically discover sensitive data and mask it for compliance. Delphix masking uses a rich library of pre-built and customizable algorithms. These algorithms allow you to mask all kinds of data with ease and speed. Delphix static data masking can also be applied to various sources. This includes databases (such as SQL Server and Oracle) and analytical sources, such as Snowflake and Databricks.

Using Delphix ensures that data is irreversibly masked to become fictitious data values that maintain referential integrity. It also ensures that your data complies with all data privacy regulations worldwide. This removes data bottlenecks, enabling development and testing teams to move faster. It also enables teams to deliver higher quality software faster.

With Delphix your organization can perfectly balance agility with utility in its masking processes. You can ensure production-like data is delivered, enabling teams to deliver higher quality software faster.

Our team of masking experts is here to help you. With Delphix, you can build your ideal data masking campaign and carry it out flawlessly.

Request your demo today to explore Delphix static data masking, AI data privacy, and beyond.