Protegrity vs Ubiq

Executive Summary

Protegrity provides an enterprise data security platform for protecting sensitive data across analytics, AI, compliance, and data sharing workflows. Its platform includes capabilities such as discovery, classification, governance, tokenization, masking, encryption, anonymization, synthetic data, policy management, and audit.

These capabilities are valuable and have been used by large enterprises to protect sensitive data across complex environments.

Ubiq addresses a similar high-level problem, but with a different architecture and operating model: protecting sensitive values directly and governing whether users, applications, service accounts, APIs, pipelines, BI tools, AI workflows, and downstream systems can access those values in cleartext at runtime.

The key distinction is not whether both platforms protect sensitive data. They do. The distinction is how each platform is deployed, integrated, governed, and extended across modern application, database, warehouse, API, BI, pipeline, and AI workflows.

The strongest comparison is architectural: Protegrity is a broad enterprise data security platform with centralized policy and multiple deployment patterns. Ubiq is designed as a modern runtime sensitive data protection layer with identity-aware field and record controls, developer-friendly integrations, and enforcement across applications, databases, warehouses, APIs, analytics tools, AI workflows, and downstream systems.

Key Takeaways

• Protegrity and Ubiq both focus on protecting sensitive data, but they differ in architecture, implementation model, and runtime enforcement approach.
• Protegrity is a broad data security platform with discovery, governance, protection, privacy, policy management, tokenization, masking, encryption, and anonymization capabilities.
• Ubiq focuses on runtime sensitive data protection: protecting sensitive values and governing cleartext access by identity, role, application, dataset, and context.
• Ubiq is designed for modern implementation patterns across SDKs, APIs, databases, warehouses, BI tools, pipelines, and AI workflows without requiring a heavy agent, proxy, or appliance-centric architecture.
• Ubiq is especially useful when organizations need fine-grained runtime enforcement across modern data workflows, service accounts, BI tools, AI/RAG systems, exports, and downstream environments.

Control Boundary View

Where Protegrity Helps

Protegrity provides a broad enterprise data security platform for protecting sensitive data across analytics, AI, compliance, and data sharing workflows.

Its platform can help teams:

• Discover and classify sensitive data
• Define centralized data protection policies
• Apply field-level protection
• Tokenize sensitive data
• Use vaultless tokenization patterns
• Mask sensitive values
• Encrypt sensitive fields
• Anonymize or de-identify data
• Generate synthetic data for privacy-preserving use cases
• Govern sensitive data use across multiple environments
• Audit and monitor protected data workflows
• Support compliance requirements
• Enable protected analytics and AI workflows

These capabilities are valuable for enterprise data security programs.

They help answer questions such as:

• Where does sensitive data exist?
• Which data types need protection?
• Which fields should be tokenized, masked, encrypted, or anonymized?
• Which policies should apply to which data?
• How can sensitive data be used for analytics, AI, or data sharing while reducing exposure?
• How can data protection policies be governed and audited centrally?

For organizations with large data estates, Protegrity can provide a broad platform for sensitive data discovery, governance, protection, and privacy workflows.

Where Ubiq Is Different

Ubiq is focused on runtime sensitive data protection.

That means Ubiq is designed to answer a specific operational question:

Should this user, application, service account, pipeline, BI tool, AI workflow, or downstream system receive this sensitive value in cleartext right now?

Ubiq protects selected sensitive fields and records, then enforces cleartext access through identity-aware policy at runtime.

This allows organizations to:

• Protect sensitive values directly
• Govern cleartext access by identity, role, application, dataset, and context
• Apply protection across applications, databases, warehouses, APIs, BI tools, pipelines, and AI workflows
• Restrict cleartext access for service accounts and automation
• Reduce exposure in BI and analytics workflows
• Support AI, RAG, notebook, MCP, and agent workflows without broadly exposing sensitive values
• Preserve protection when data is copied, exported, embedded, indexed, replicated, or consumed downstream
• Maintain separation between system access and sensitive value authorization

The difference is not that Protegrity protects data and Ubiq does not, or vice versa.

The difference is how Ubiq delivers runtime enforcement for sensitive values across modern data workflows with identity-aware authorization and lightweight integration patterns.

Comparison Matrix

Key Architectural Differences

Broad Data Security Platform vs Runtime Sensitive Value Enforcement

Protegrity is positioned as a broad enterprise data security platform.

It includes discovery, classification, governance, protection, privacy, policy, audit, and monitoring capabilities.

Ubiq is more focused on runtime sensitive data protection.

Ubiq’s core question is:

Which identities and workflows should be able to access selected sensitive values in cleartext?

This distinction matters because organizations may already have discovery, catalog, governance, or DSPM tools. In those environments, the key missing control is often runtime enforcement over sensitive values after access to a system has already been granted.

Policy Governance vs Cleartext Authorization

Both platforms use policy.

The difference is how the policy is expressed and enforced in modern workflows.

Protegrity emphasizes centralized policy management across data security programs.

Ubiq emphasizes identity-aware cleartext authorization at runtime.

With Ubiq, the question is not only:

Which fields are sensitive and how should they be protected?

The question becomes:

Is this user, application, service account, API, pipeline, BI tool, or AI workflow allowed to see this sensitive value in cleartext right now?

That distinction is especially important when many identities and workflows touch the same data but should not receive the same level of access.

Enterprise Platform Footprint vs Lightweight Runtime Integration

Protegrity supports multiple enterprise deployment patterns, including agent, proxy, and API-based models.

Those patterns can be valuable in large, centralized data security programs, but they may also introduce additional architectural planning, operational ownership, and deployment complexity.

Ubiq is designed to integrate into modern application and data workflows through lightweight runtime enforcement patterns.

This makes Ubiq well suited for:

• Application-layer protection
• Database integrations
• Warehouse integrations
• API workflows
• BI access patterns
• Service accounts and automation
• AI, RAG, notebook, MCP, and agent workflows
• Downstream data protection

The distinction is not simply “which tool protects data.” The distinction is how quickly and cleanly the protection model can be embedded into modern workflows.

Tokenization and Protection Methods vs Identity-Governed Data Use

Protegrity is well known for tokenization, including vaultless tokenization, as well as masking, encryption, anonymization, and privacy capabilities.

Ubiq also supports protection methods such as encryption, tokenization, and masking.

The architectural difference is the emphasis on identity-governed data use.

Ubiq is designed to control whether protected values should be revealed in cleartext based on the identity and context of the access request.

This helps support scenarios such as:

• Same table, different users
• Same dataset, different applications
• Same pipeline, different service accounts
• Same BI dashboard, different authorization levels
• Same AI workflow, different data exposure rules
• Same downstream data copy, protected values unless cleartext is explicitly authorized

Traditional Data Protection Programs vs Modern AI and Analytics Workflows

Protegrity has deep roots in enterprise data protection programs.

Ubiq is designed around the modern reality that sensitive data is accessed by more than traditional applications and databases.

Sensitive values may be used by:

• Warehouses
• BI tools
• Data pipelines
• Event streams
• APIs
• RAG systems
• AI agents
• MCP tools
• Notebooks
• Vector stores
• Downstream replicas
• Vendor feeds

Ubiq is built to enforce sensitive value access across these runtime paths, not only inside a traditional application or database control point.

When to Use Both

Protegrity and Ubiq may both be relevant in large enterprise environments, depending on architecture, incumbent tooling, and desired operating model.

Organizations may continue using Protegrity where they need:

• A broad enterprise data security platform
• Centralized data security policy governance
• Existing Protegrity tokenization deployments
• Discovery, classification, protection, and privacy workflows in one platform
• Established data security operations built around Protegrity
• Supported integrations already deployed in production
• Enterprise data privacy and anonymization capabilities

Ubiq should be considered when organizations also need:

• Runtime sensitive value protection across modern workflows
• Identity-aware cleartext authorization by user, role, application, dataset, and context
• Lightweight integration into applications, APIs, databases, warehouses, BI tools, pipelines, and AI workflows
• Protection for service accounts and automation
• Cleartext control for AI, RAG, notebook, MCP, and agent workflows
• Protection that persists when data is copied, exported, embedded, indexed, replicated, or consumed downstream
• A modern developer experience for implementing sensitive data protection without unnecessary infrastructure complexity

The layered model is simple:

• Use existing Protegrity deployments where they already provide effective data security controls.
• Use Ubiq where runtime identity-aware sensitive value protection is needed across modern application, data, analytics, and AI workflows.

How Ubiq Differentiates from Protegrity

Ubiq differentiates from Protegrity through a focused runtime enforcement model for sensitive values.

With Ubiq, selected sensitive fields can remain encrypted, tokenized, masked, or otherwise protected by default. Cleartext access is granted only when the requesting identity or workflow is authorized by policy at runtime.

This allows organizations to:

• Protect sensitive values across applications, databases, warehouses, APIs, and analytics workflows
• Control cleartext access for users, applications, service accounts, pipelines, and AI systems
• Reduce exposure in BI and reporting workflows
• Protect sensitive data used by AI, RAG, notebook, model, and agent workflows
• Preserve protection when data is copied, exported, embedded, indexed, replicated, or consumed downstream
• Maintain separation between system access and sensitive value authorization
• Integrate sensitive data protection into modern software and data workflows

In this model:

• Protegrity provides a broad enterprise data security platform.
• Ubiq provides runtime sensitive value protection focused on identity-aware cleartext enforcement.

The right choice depends on the customer’s architecture, incumbent systems, deployment preferences, and whether the primary need is broad data security platform coverage or modern runtime enforcement across sensitive data workflows.

Internal Evaluation Questions

When evaluating Protegrity and Ubiq, teams should ask:

• Are we looking for a broad enterprise data security platform or a focused runtime sensitive data protection layer?
• Which sensitive fields require cleartext authorization at runtime?
• Which users, applications, service accounts, APIs, pipelines, BI tools, and AI workflows can access sensitive values today?
• Which workflows receive sensitive data in cleartext?
• How much infrastructure are we willing to deploy and operate?
• Do we need agent, proxy, or appliance-based patterns, or do we prefer lightweight SDK, API, database, warehouse, and workflow integrations?
• What happens when sensitive data is exported, copied, logged, joined, materialized, embedded, indexed, or replicated?
• Do BI tools, dashboards, extracts, and reports expose sensitive values?
• Do AI, RAG, notebook, MCP, vector store, model training, model inference, or agent workflows access sensitive values?
• Should service accounts, APIs, pipelines, or automation workflows receive cleartext, or only protected values?
• Which control determines whether a specific identity or workflow can see sensitive values in cleartext?
• Does the protection model need to work across platforms beyond a single application, database, or warehouse?

Summary

Protegrity provides a broad enterprise data security platform with capabilities for discovery, governance, tokenization, masking, encryption, anonymization, privacy, policy, and audit.

Ubiq addresses the same overall data protection problem with a focused runtime sensitive data protection model.

By protecting selected sensitive values directly and governing cleartext access through identity-aware policy, Ubiq helps organizations reduce exposure across users, applications, service accounts, APIs, pipelines, databases, warehouses, BI tools, AI workflows, exports, and downstream systems.

Protegrity is a broad enterprise data security platform.

Ubiq is a modern runtime sensitive value protection layer.

The best fit depends on architecture, deployment model, workflow coverage, and the level of identity-aware runtime enforcement required.