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Practical Data: When is Data Masking just Data Entitlements in Disguise?

Picture of Andrew Bush By   ·  6 minute read

In this latest article in our Practical Data series, we dig into Data Masking and Data Entitlements as two standard controls organizations can use to protect their data.  Data masking and Data entitlements are designed to target different objectives, but sometimes converge in practice. In addition, we conclude this article by discussing data controls in AI implementations and highlighting some additional AI-specific risks.

Quick Summary:

  • Masking: Obscures sensitive values while preserving usability
  • Entitlements: Define access through policies and permissions
  • Masking works best for test data, vendor access, and low-trust environments where structure is important
  • Entitlements work best for fine-grained operational access and regulatory compliance
  • Overlap: Masking can act like entitlements when applied at an application level for structured data, creating confusion, technical debt, and security and governance issues if misused
  • Masking and Entitlements both play an essential part in protecting AI systems, along with additional AI-specific controls

Introduction

Many enterprises rely on proprietary, sensitive, and personal information as a foundation of their business models. Protecting this data is critical for intellectual property, competitive advantage, regulatory compliance, and customer trust. The rise of AI has amplified both the value of enterprise data and the risks of misuse, making effective protection strategies more urgent. Organizations must balance data utility (analytics, testing, AI training, and operations) with data protection through two complementary approaches: data masking, which obscures values, and data entitlements, which govern access policies. Understanding where these methods differ and where they overlap is essential to building resilient governance.

Core Concepts

Data Masking

Data masking "hides" sensitive data content by providing an alternative, anonymous, or equivalent content to prevent exposure, usually while keeping the overall structure and format of the data consistent to maintain overall usability.

Implementation Types:

  • Static Masking: Permanently transforms data stored at rest from its original sensitive values to safe values before storage and reuse. This approach is often used in bulk for creating non-production environments
  • Dynamic Masking: Masks data as needed in real time at the query or application level for presentation or onward distribution. Data remains stored in its original form and values

Content Types:

  • Redaction Masking: Replaces the sensitive text with a consistent set of pre-determined values independent of data content. For example, replacing both email addresses and phone numbers with the same "XXXXXXXX"
  • Format-Preserving Masking: Replaces sensitive data with structurally similar and potentially statistically equivalent data. For example, replacing the email address with abcdefg.hijklmn@opqrst.com and a telephone number with 917 123 4567
  • Tokenization and Hashing: Replaces values with unique tokens that can be restored through privileged lookup or decryption functions. For example, replacing an email address with EMAIL(0xabd67fg890a34ee3) and a telephone number with PHONE(0x1735abe8b937843c78)

Data Entitlements

Data entitlements prevent access to data based on properties of the user, purpose, and data content. We generally describe types of Data Entitlements based on the kind of information leveraged to determine access. We typically use the term "scope" to describe the population of data that is allowed (or denied) based on the entitlement rules.

Implementation Types:

  • Role-Based Access Control (RBAC): The most common approach, it controls permission to access data tied only to the data consumer's role. Generally controls access to whole records. For example, HR staff can view Payroll records.
  • Attribute-Based Access Control (ABAC): Controls permission to access specific data records that are tied to a combination of role and the data content. Generally controls access to whole records. For example, US HR staff can see US Payroll records, and India HR Staff can see India Staff Records.
  • Intersection-Based Access Control (IBAC): Controls permission to access specific data attributes of specific records based on role and data content. For example, Managers can see the salary of the staff they manage, while HR Staff can see sensitive PII fields for the employees in their region

Most organizations have well-established governance approaches, including access logging and entitlement control functions, to manage entitlement rules resulting from these implementations. This ensures that individuals and external entities have access to only the data they need to perform their roles.

Quick Comparison

  • Masking is preferred when sharing datasets with developers, vendors, analytics functions, or researchers, or when complying with data minimization requirements, as it maintains the overall structure of the data, which is still valuable
  • Entitlements are preferred when enforcing least-privileged access in business applications and controls, allowing multiple teams to leverage business operation applications and control data flows

Data Masking as Data Entitlements in Disguise

When determining the best way to protect structured data within an application, organizations often choose to "mask" specific highly sensitive attributes in the front-end or API instead of implementing this as an "entitlement." For example, Credit Card Information from Customer Support Staff in a CRM profile.

Why This Happens: Masking implementation often appears faster to implement, creates apparent compliance, and avoids dependency on enterprise entitlement systems and complex multi-dimensional IBAC implementations. Masking also feels more useful to end users as they can still understand that the data exists without seeing the actual value.

Attribute Masking vs. IBAC – Essentially the Same

  • Architecture: Both rely on user/contextual attributes
  • Implementation: Both need consistent policies, identity integration, audit logging, rule management, and maintenance
  • Outcomes: Both deliver tailored views of data aligned with least-privilege

Risks of Masking in Structured Data Applications

When masking is implemented in the front end or API layer of structured applications without the additional capabilities usually associated with Data Entitlements, organizations introduce considerable risks.

  • Security gaps: Masking hides values but doesn’t block record access, and vulnerable applications may end up exposing the real data by accident or through malicious actions
  • Audit weakness: Masking can only be evidenced at a point in time with screenshots or API response dumps. Regulators may reject masking as an entitlement substitute without evidence-based logging and controls to show who could see what and when
  • Complexity: Maintaining masking rules eventually becomes as complicated as maintaining entitlement rules, which leads to duplication of code and oversight requirements—often without the enforced equivalent governance that organizations have implemented in the IAM process

Final Recommendation

For business applications involving structured data, treat masking as an incremental entitlements capability, not a substitute. Consider a single "access control" implementation where the access decision is no longer binary, ALLOWED vs DENIED; instead, providing an outcome of ALLOWED vs MASKED, or DENIED based on entitlements. This encourages the correct audit log and governance oversight implementations.

Bonus Material: Masking and Entitlements in Generative AI Scenarios

As generative AI expands rapidly within organizations, its scale and speed make balancing data utility with data controls increasingly difficult. Data privacy and security considerations must be taken into account at all stages of the AI implementation, including model training, prompt input, and generated output stages.

In addition to Masking and Entitlements, generative AI implementations introduce additional methods by which data can be exposed, which require additional controls beyond masking and entitlements, which are also included below.

Best Practice Controls for Data Training

  • Masking: Use masking to protect specific PII, PCI, or PHI fields in training datasets. Techniques that maintain the structure and statistical significance of the data while anonymizing real data values allow for AI capabilities to be trained on valid data without compromising security and privacy.
  • Entitlements: Use entitlements to control who can contribute training data and which datasets are eligible

Best Practice Controls for Model Use - Prompt Controls

  • Masking: Use masking to pre-process input prompts and remove any sensitive data before it is sent to a model that could expose specific PII, PCI, or PHI data. Especially if models are hosted externally to an organization, entity recognition tools and techniques exist to detect and hide sensitive elements within the data.
  • Entitlements: Use entitlements to control which users, systems (and other AI agentic agents) can access specific models, and which models based on their role or intended purpose. Minimize access to "sensitive" models to 
  • Prompt Hacking and Prompt Injection: Prompt Injection controls help prevent malicious actors from manipulating AI models to bypass their in-built controls, output data, or alter model behavior that is not intended.
  • Context & Anomaly Detection: Anomaly detection controls also help prevent malicious or inappropriate use of models by assessing prompts for appropriateness and intent based on typical usage patterns. Unexpected prompts can be further validated or blocked.   

Best Practice Controls for AI Model Use - Output Controls.

  • Masking: Again, leverage masking to pre-process the output from models as soon as possible before onward dissemination to consumers to ensure that generated output does not contain specific PII, PCI, or PHI data that could have originated from its training population.
  • Entitlements: Entitlements should have already been applied at the prompting stage!
  • Bias and Harm Assessments: Bias and Harm controls help to ensure that outputs from models do not contain any materials that may be considered harmful or biased in an everyday business context.
  • Intellectual Property: Intellectual property assessments check that the generated output does not closely relate to pre-existing published materials

Conclusion

Data masking and data entitlements are complementary tools for protecting sensitive information while enabling its use. Masking provides structural usability while obscuring details, and entitlements enforce precise, auditable access. In practice, the strongest governance frameworks integrate both approaches rather than treating them as substitutes. The rapid adoption of AI amplifies both the opportunities and risks of data use, making unified strategies even more critical. Enterprises should:

  • Treat masking as an extension of entitlements, not a replacement
  • Implement governance that spans masking, entitlements, and AI-specific controls
  • Monitor and audit all access decisions and transformations consistently
  • Prepare for growing regulatory scrutiny around AI and data privacy

The future will likely see the convergence of masking and entitlement technologies into unified access and privacy platforms. Organizations that adopt a holistic strategy today will be better positioned to maintain compliance, safeguard trust, and harness data responsibly in the age of AI.