Data masking gains importance as AI and cloud adoption expose sensitive data

Why data masking matters now

Data masking is a cybersecurity technique that replaces sensitive information with realistic but fictitious values. This allows organizations to use data for testing, analytics, and AI training without exposing the original sensitive details. According to TechRadar, the method is likened to a "Halloween costume for data," enabling its use while concealing its true identity.

Visibility and security gaps persist

Despite its benefits, data masking alone cannot address the broader challenge of data visibility. The 2026 Thales Data Threat Report found that only 34% of organizations have complete knowledge of where their data is stored. Additionally, just 39% can classify all their data, leaving the majority vulnerable to breaches and compliance risks.

The report also highlights that 61% of organizations experienced attacks on their AI applications, with sensitive data as the primary target. Rapid changes in the AI ecosystem were cited as the most common security risk, emphasizing the need for adaptive protection measures.

DSPM and the future of data protection

Data security posture management (DSPM) is gaining traction as a solution to these challenges. DSPM tools focus on discovering, classifying, and protecting sensitive data across cloud and AI systems. They help organizations understand where data resides, how it is used, and who has access to it.

The average organization relies on seven different tools for data protection and monitoring, according to TechRadar. This fragmentation underscores the need for streamlined solutions that can provide comprehensive visibility and control.

As AI systems require more data to function effectively, techniques like masking and tokenization are becoming critical. The principle of least privilege—granting access only to what is necessary—is being extended to data, ensuring security without hindering productivity.

What this means

Lazyfounder analysis — our interpretation, not reported fact.

For founders and operators, the rise of data masking and DSPM reflects a broader shift in cybersecurity priorities. As AI adoption accelerates, the volume of sensitive data being processed grows, and so do the risks. Many organizations are still struggling with basic data visibility, which leaves them exposed to breaches and compliance violations.

The reliance on multiple disjointed tools for data protection suggests an opportunity for startups to offer integrated solutions that simplify security management. Meanwhile, the focus on least-privilege access for data reinforces the need for granular control mechanisms, particularly in cloud and AI-driven environments.

This trend also signals a growing market for DSPM and data masking tools, as organizations seek to balance innovation with security. Founders building AI or cloud-based products should take note: data protection is no longer a secondary concern but a core requirement for scalable and trustworthy systems.

Key takeaways

• Founders and operators must prioritize data masking to enable secure AI training and analytics without exposing sensitive information.
• The reliance on multiple data protection tools underscores the need for integrated solutions to simplify security management.
• The low visibility into data location and classification highlights a critical gap in data security strategies, particularly as AI adoption grows.
• DSPM tools can address these challenges by providing better visibility and control over sensitive data, reducing the risk of breaches.

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