Blog
October 29, 2025
AI and Data Privacy: 3 Top Concerns and What to Do About Them
Security & Compliance,
Data Management,
AI
The stakes for AI and data privacy are high. Risks in non-production environments are increasing. 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.
Table of Contents
Businesses’ Biggest AI and Data Privacy Concerns
Perforce Delphix surveyed 280 global leaders for our State of Data Compliance and Security Report. One of the report’s key purposes was to examine leaders' AI and data privacy concerns.
The results were interesting. The vast majority (91%) of respondents think sensitive data should be allowed in AI model training and testing.
But at least 67% of respondents said they are “very” or “extremely” concerned about the following key areas:
- Theft of model training data (78%).
- Privacy compliance and audits (68%).
- Personal data re-identification (67%).
Data theft, non-compliance, and data re-identification challenges exist across enterprises. But AI and data privacy add new complications. Knowing the unique AI-related issues will help your business address them, especially as your AI adoption becomes more mature.
Concern #1: Theft of Model Training Data
In the report, most respondents (82%) think it's safe to use sensitive data in AI model training and fine-tuning. But the numbers indicate some uncertainty: 78% are highly concerned about theft or breach of 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.
AI environments are particularly at risk to internal bad actors because they’re less structured than other non-production environments. This allows teams with looser data processes and policies easier access.
AI/ML training data often includes sensitive information, making it particularly vulnerable. While data theft threatens a model's competitive edge, the inclusion of sensitive data further amplifies the risk.
Concern #2: Privacy Compliance and Audits
Another concern with model training data (and other AI training data) is that data privacy regulations strictly govern it. Indeed, 68% of respondents reported being highly concerned about data privacy risks and regulatory non-compliance in their AI environments.
And 100% of surveyed organizations reported having data that is subject to privacy regulations in their non-production environments. The top regulations cited include:
Compliance is already a major concern. Non-compliance penalties include hefty fines, potential prison time, and damaged reputations. But AI is still in an immature regulatory space with very few regulations specifically governing it. In August 2024, the European Union enacted the AI Act, which it calls “the first-ever legal framework on AI.”
Data privacy regulations govern the data feeding AI models, and future AI-specific laws will inevitably emerge. So, organizations must be flexible with managing data privacy as regulations grow stricter.
📘 Further reading: The Intersection of GDPR & AI
Concern #3: Personal Data Re-identification
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 steal tokenized training data or trick an AI model into exposing sensitive data. If bad actors steal tokenized model training data and access the tokens, they could reverse the process to expose sensitive data. This would damage a business’s competitive edge and trigger legal action.
Static data masking is irreversible — the most secure form of data protection. It prevents bad actors from re-identifying the data while allowing teams to decipher masked data points by using other information on hand.
📘 Further reading: Complete Guide to Data Masking Techniques
Back to topAI Adoption is On the Rise. Is Your Data Privacy Strategy Keeping Up?
94% of organizations are already past the initial stages of AI adoption. But as enterprises move up the AI maturity curve, concerns of data theft, non-compliance, and exposure of sensitive data are mounting. Only 42% of respondents believe there are sufficient approaches and tools to tackle these challenges.
The gap is real.
Navigate the AI landscape and establish a secure foundation for your AI initiatives with insights from 280 global enterprise leaders from our 2025 State of AI and Data Privacy Report.
Back to topRemember: AI Models Can Inadvertently Expose Sensitive Data
There's a layer to AI and data privacy that enterprises must grasp: AI models themselves can become the vulnerability.
According to our Data Compliance and Security Report, 60% of organizations have experienced data breaches or theft in non-production environments. AI environments are no exception. They face unique exposure risks that traditional data security measures don't address.
AI models can leak the very data they were trained on. Bad actors can query models repeatedly to reconstruct sensitive information or determine whether specific data was used in training, revealing confidential information. And with large language models, prompt injection attacks can manipulate systems into exposing PII or proprietary data.
It’s no surprise why enterprise leaders have various concerns when it comes to AI-specific vulnerabilities in model training data.
Back to topAI Data Privacy Best Practices for Enterprise Leaders
Protecting AI and data privacy requires a comprehensive approach that addresses vulnerabilities at every stage of your AI pipeline.
Here are the essential practices enterprise leaders should implement.
Don’t Let Sensitive Data Enter AI Pipelines
Because AI doesn’t forget the data it’s trained on, you should never allow senstitive data to enter AI pipelines in the first place. If you want to use production data in AI model training, ensure it is irreversibly masked first — using a tool like Perforce Delphix.
Learn more >> What is Perforce Delphix?
Require Access Controls for AI Environments
AI environments often have looser access controls, creating risks for data leaks or breaches. Implement strict role-based access controls. Limit who can access model training data. Ensure that only authorized personnel can interact with AI systems.
Keep Audit Trails for Model Training Data
Maintain comprehensive logs of who accessed training data, when, and for what purpose. Audit trails ensure compliance and help you detect suspicious activity before breaches occur. Regular audits of your AI environments should be standard practice.
Implement Privacy-by-Design Principles
Embed privacy protections into your AI workflows from the start. Don't treat them as an afterthought. Design your systems with compliance in mind. Use irreversible anonymization techniques like static data masking to protect data from the source.
In our report, organizations viewed static masking as “very” or “extremely” effective for:
- Preventing sensitive data breach or theft (81%)
- Scalability (79%)
- Cost efficiency (76%)
This makes it an ideal solution for future-proofing your AI environments against evolving compliance requirements.
Back to topPerforce Delphix: Your Partner in Ensuring Data Privacy for AI
Your MLOps and AI pipelines require accurate, accessible, and compliant data to drive innovation. And manual data compliance methods simply can’t keep up.
Perforce Delphix reduces data risks by empowering your teams to deliver AI-ready, compliant data at scale through automated data discovery, masking, and delivery.
The results speak for themselves.
According to IDC research, 77% more data and data environments are masked and protected with Delphix static data masking.* This enables enterprises to innovate with AI while maintaining the highest standards of data privacy and compliance.
Automate Compliance Across Your Entire Data Ecosystem
Stay compliant with expanding data privacy regulations like GDPR, HIPAA, and CCPA. Delphix enables automatic sensitive data discovery and masking across your entire data ecosystem, from databases (such as SQL Server and Oracle) to analytical sources, such as Snowflake and Databricks. Static data masking ensures your data is irreversibly protected while maintaining referential integrity for high-quality AI model training.
See how it works: Perforce Delphix for Snowflake [demo]
Accelerate AI Projects Without Compliance Bottlenecks
Speed matters when you’re keeping track with your AI initiatives. Delphix integrates directly into your MLOps workflows and AI toolchains, masking up to 4 billion rows per hour. Deliver production-like data to your teams in record time and enable them to deliver higher-quality software faster.
Balance Innovation, Security, and Speed
Don't sacrifice quality for compliance. Delphix helps you strengthen AI and data privacy while empowering your teams to focus on insights and innovation. Organizations using Delphix release applications 2x faster.
Ready to Secure Your AI Environments?
See how industry leaders are solving AI and data privacy challenges with Delphix. Get a personalized, no-pressure demo and discover why forward-thinking organizations trust Delphix to accelerate their AI initiatives and ensure innovation.
*IDC Business Value White Paper, sponsored by Delphix, by Perforce, The Business Value of Delphix, #US52560824, December 2024
This blog was originally written by Roberto Seminario and was updated by Steve Karam.