This Halloween, make sure your sensitive data Is wearing a mask

As cybersecurity professionals gain experience, they frequently employ analogies and metaphors. In October, discussions about security threats and lurking dangers abound. However, a comparison that often goes unnoticed is the use of a mask during Halloween. In the context of data security, this analogy may be the most valuable today.

Masking does not conceal the underlying data, but it prevents revealing its true identity to those who encounter it. This concept is crucial when dealing with sensitive data. Developers require data for testing applications, analysts need it to identify patterns, and AI tools and agents demand access to enterprise information for useful operations.

Nevertheless, the need to use this data does not always entail the necessity to see every sensitive value it contains. This is where data masking plays a vital role. By replacing sensitive information with realistic yet unusable values, organizations can maintain data usefulness without unnecessarily exposing the concealed information.

As AI increasingly enables data movement, access, and potential action, this distinction becomes even more significant. In essence, data masking serves as a security-oriented disguise, allowing data to participate in parties without disclosing its true identity. This distinction matters because safeguarding data should not merely mean hindering its use.

Modern businesses rely on analyzing, testing, and even training AI systems with enterprise information. Therefore, the question should be: does this individual, application, or AI system truly require the original sensitive value to carry out its task? For instance, a developer testing an application might need representative customer data, but not necessarily genuine customer identities.

Similarly, an analytics workload should preserve relationships and patterns without unveiling every account number or personal identifier. If the real value is not required, exposing it introduces risks without providing corresponding business value. Masking eliminates this unnecessary exposure. The technique selection depends on the use case, but organizations can also employ tokenization and encryption.

The key principle is to align protection with how information is used rather than defaulting to exposing raw sensitive data. Not everything can be masked, however. Discovering where sensitive data resides is a significant challenge. According to the 2026 Thales Data Threat Report, only 34% of surveyed organizations possess complete knowledge of their data storage locations.

Only 39% could classify all of it. Additionally, the average organization utilizes seven different tools for data protection and monitoring. Cloud infrastructure further complicates the issue, as data can be duplicated into data lakes, SaaS platforms, development environments, and backups. Unstructured documents may contain hidden sensitive information, and applications created years after data collection might suddenly gain access to it.

AI exacerbates the visibility problem, as these systems become adept at locating information. This is where Data Security Posture Management (DSPM) becomes essential. Instead of solely viewing data security as an infrastructure defense matter, DSPM begins with data itself: discovering and classifying sensitive information, understanding its location, access, usage, and appropriate protection measures.

Once organizations identify unnecessarily exposed sensitive data, they can determine whether to remove, restrict, or protect access through controls like masking, tokenization, or encryption. Addressing AI data security is particularly crucial as businesses transition from AI experimentation to embedding AI in everyday operations.

AI applications necessitate data; sometimes, they require more extensive data as they are designed not only to generate content but also to retrieve information, make decisions, and potentially perform actions across various systems. The security implications are already apparent, with 61% of organizations reporting AI applications targeted by attackers, with sensitive data being the primary target.

Rapid changes within the AI ecosystem also pose the most commonly cited AI security risk. However, the question should not be whether AI can be secured, but rather: what data are we allowing AI to see in the first place? The principle of least privilege, commonly applied to human identities, can also be applied to data: expose what is necessary for the task and protect what is not.

This may involve masking personal information before it reaches development and testing environments, tokenizing particularly sensitive values used by an application, encrypting information, and controlling decryption access tightly. In some cases, it might mean preventing an AI system from accessing a dataset altogether. It is essential not to complicate AI security more than necessary.