Protegrity vs Ubiq

Executive Summary

Protegrity provides a broad enterprise data security platform for protecting sensitive data across analytics, AI, compliance, data sharing, and enterprise data modernization workflows. Its platform includes capabilities for discovery, governance, tokenization, masking, encryption, anonymization, synthetic data, centralized policy management, and audit.

These capabilities are valuable, especially for organizations that want a broad data security platform spanning discovery, governance, privacy, and protection.

Ubiq addresses the same overall sensitive data protection problem with a different architecture and operating model. Ubiq is designed as a focused runtime sensitive data protection platform that protects sensitive values directly and governs 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 key distinction is how they are deployed, integrated, operated, and extended across modern application, database, warehouse, API, BI, pipeline, and AI workflows.

Protegrity is a broad enterprise data security platform with discovery, governance, policy, privacy, and protection capabilities. Depending on the use case, Protegrity deployments may involve multiple product areas, centralized policy administration, enforcement points, native integrations, proxies, SDKs, and enterprise platform planning.

Ubiq is a single runtime data protection platform designed to integrate through software libraries, APIs, database and warehouse integrations, BI patterns, and AI/data workflow enforcement without requiring a broad enterprise data security platform footprint.

Ubiq also supports modern AI, RAG, and vector-driven workflows by separating protection of sensitive source data from AI/vector computation. Sensitive records and identifiers can remain protected and identity-governed, while AI workflows operate on controlled derived representations that preserve semantic search, retrieval, and analysis functionality without broadly exposing plaintext sensitive values.

Key Takeaways

• Protegrity and Ubiq both help protect sensitive data, but they differ in architecture, deployment model, operational focus, and runtime enforcement approach.
• Protegrity is strong for broad enterprise data security, including discovery, governance, tokenization, masking, encryption, anonymization, synthetic data, centralized policy, and audit.
• Protegrity deployments can involve multiple product areas, policy administration, enforcement points, native integrations, proxies, SDKs, and enterprise platform planning depending on the use case.
• Ubiq is designed as a focused runtime sensitive data protection platform with software libraries, APIs, and data workflow integrations that are easier for application, data engineering, analytics, and security teams to deploy and operate.
• Ubiq protects selected sensitive values and controls whether an identity or workflow can access those values in cleartext at runtime.
• Ubiq is especially useful when organizations need field and record-level enforcement across applications, databases, warehouses, APIs, service accounts, pipelines, BI tools, AI/RAG workflows, exports, and downstream systems.
• Ubiq can also support AI/vector-driven workflows where traditional encryption or tokenization may break semantic meaning, similarity search, or vector-based computation if applied directly to the values the AI workflow needs to interpret.

Where Protegrity Helps

Protegrity provides a broad enterprise data security platform for protecting sensitive data across complex environments.

Its capabilities can help teams:

• Discover and classify sensitive data
• Define centralized data protection policies
• Govern sensitive data usage across systems
• Apply field-level tokenization
• Apply vaultless tokenization
• Apply masking
• Apply encryption
• Apply anonymization or de-identification
• Generate or use synthetic data for privacy-preserving workflows
• Protect sensitive data across analytics, AI, operations, and data sharing workflows
• Audit and monitor protected data activity
• Support compliance and privacy requirements

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?
• How can a central data security team standardize protection across different environments?

For organizations with existing Protegrity deployments, Protegrity can provide broad data security controls across discovery, governance, protection, privacy, and audit 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, agent, and vector-driven workflows without broadly exposing sensitive plaintext
• Preserve protection when data is copied, exported, embedded, indexed, replicated, or consumed downstream
• Maintain separation between system access and sensitive value authorization
• Separate protection of sensitive source data from controlled AI/vector computation where semantic functionality is required

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

The difference is that Protegrity is a broad enterprise data security platform, while Ubiq is a focused runtime data protection layer designed to be easier to integrate and operate across modern software and data workflows.

Comparison Matrix

Key Architectural Differences

Broad Enterprise Data Security Platform vs Focused Runtime Data Protection

Protegrity is a broad enterprise data security platform.

It includes discovery, classification, governance, tokenization, masking, encryption, anonymization, synthetic data, policy, audit, and privacy capabilities.

That breadth can be valuable when an organization wants a central platform for multiple data security and privacy use cases.

However, that breadth can also make implementation and operation broader than what some application and data teams need when the immediate requirement is runtime enforcement for sensitive values.

Ubiq is intentionally more focused.

Ubiq’s core question is:

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

Ubiq is designed to protect sensitive values and enforce runtime cleartext access through software libraries, APIs, database integrations, warehouse integrations, BI patterns, and AI/data workflow enforcement.

This makes Ubiq easier to implement in modern application and data environments where teams need field and record-level runtime protection without deploying a broader enterprise data security platform first.

Multiple Platform Capabilities vs One Runtime Protection Platform

Protegrity can involve multiple product areas depending on the desired outcome.

For example, a deployment may involve:

• Discovery
• Governance
• Policy management
• Tokenization
• Encryption
• Masking
• Anonymization
• Synthetic data
• Native integrations
• Proxies
• SDKs
• Audit and reporting workflows

Those capabilities are powerful, but they may also require broader architecture planning, implementation decisions, operational ownership, and ongoing platform administration.

Ubiq is designed as one runtime sensitive data protection platform.

Instead of requiring teams to assemble a broader discovery, governance, privacy, and protection platform before enforcing runtime access to sensitive values, Ubiq provides a single protection model for:

• Encryption
• Tokenization
• Masking
• Identity-aware policy enforcement
• Field and record-level cleartext authorization
• Application, database, warehouse, API, BI, pipeline, and AI workflow integrations

This difference matters when the goal is to protect sensitive values quickly and consistently across modern systems without adding unnecessary operational complexity.

Broad Governance and Privacy Workflows vs Runtime Cleartext Decisions

Protegrity can help organizations discover data, define policies, govern sensitive data usage, and apply multiple protection methods across enterprise environments.

Ubiq focuses more narrowly on runtime enforcement.

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 cleartext access.

Complex Integration Patterns vs Software Libraries and Simple APIs

Protegrity supports multiple enforcement patterns, including native integrations, proxies, and SDKs. That breadth can be valuable for broad enterprise data security programs, but it can also require more planning around the right enforcement point for each workflow.

Ubiq is designed for software and data workflow integration.

Ubiq can be embedded where sensitive data is created, queried, transformed, analyzed, or consumed through:

• Software libraries
• Simple APIs
• Application integration
• Database integration
• Warehouse integration
• BI integration patterns
• Data pipeline workflows
• AI and RAG workflows

This is a major operational difference.

With Ubiq, application, data, analytics, and security teams can focus on the actual data protection questions:

• Which fields or records need protection?
• Which identities can see cleartext?
• Which applications or workflows need enforcement?
• What should service accounts receive?
• What should BI users see?
• What should AI workflows receive?
• What happens when data is copied, exported, or consumed downstream?

They do not need to start by deploying a broad platform footprint before enforcing runtime protection.

AI and Vector Workflows Without Broad Plaintext Exposure

AI, RAG, and vector search workflows create a difficult data protection challenge.

Data teams often want to run semantic search, similarity matching, retrieval, model enrichment, or agent workflows on sensitive data. But traditional encryption or tokenization can break semantic meaning, similarity search, or vector-based computation if applied directly to the values the AI workflow needs to interpret.

Ubiq supports this by separating protection of sensitive source data from AI/vector computation.

Sensitive source records, identifiers, and regulated fields can remain protected and identity-governed, while AI/vector workflows operate on controlled derived representations that preserve the functionality required for semantic search, retrieval, or analysis.

This allows organizations to support AI-driven workflows without broadly exposing plaintext sensitive data or weakening the protection model around the original sensitive values.

This is especially important for regulated data environments where teams want to enable AI use cases but cannot simply decrypt, copy, or expose raw sensitive values into notebooks, vector stores, RAG pipelines, model workflows, or downstream AI systems.

Data Protection Methods vs Identity-Governed Data Use

Protegrity offers strong data protection methods, including tokenization, vaultless tokenization, masking, encryption, anonymization, and synthetic data.

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

Enterprise Data Security Program vs Workflow-Level Runtime Enforcement

Protegrity is often positioned as part of a broader enterprise data security program. That can make sense when an organization wants to centralize discovery, governance, protection, privacy, and audit across many systems.

However, application and data teams may experience that model as broader than necessary if their immediate need is to enforce field and record-level protection in specific application, database, warehouse, BI, pipeline, or AI workflows.

Ubiq is designed to be easier for application, data engineering, analytics, and security teams to deploy and operate directly in the workflows where sensitive data is actually used.

That means teams can protect sensitive values through familiar implementation patterns rather than routing every use case through a broader enterprise data security program first.

This matters when organizations need to move quickly across:

• Modern applications
• APIs
• Warehouses
• Databases
• Data pipelines
• BI tools
• AI and RAG workflows
• Downstream systems

Traditional Data Protection Programs vs Modern AI and Analytics Workflows

Protegrity has deep roots in enterprise data protection and is actively positioned around analytics, AI, and governed data use.

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.

How Ubiq Differentiates from Protegrity

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

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, agent, and vector-driven workflows
• Preserve protection when data is copied, exported, embedded, indexed, replicated, or consumed downstream
• Maintain separation between system access and sensitive value authorization
• Separate sensitive source data protection from controlled AI/vector computation
• Integrate sensitive data protection into modern software and data workflows
• Avoid unnecessary platform complexity when the primary requirement is runtime sensitive value protection

In this model:

• Protegrity provides broad enterprise discovery, governance, tokenization, encryption, masking, anonymization, synthetic data, privacy, policy, and audit capabilities.
• Ubiq provides focused runtime sensitive value protection with identity-aware cleartext enforcement and simpler software-based integration patterns.
• Ubiq can also support AI/vector-driven workflows by allowing sensitive source data to remain protected while controlled derived representations support semantic search, retrieval, and analysis.

The right choice depends on the customer’s architecture, incumbent systems, deployment preferences, governance needs, privacy requirements, AI/data workflow needs, and the level of identity-aware runtime enforcement required.

Internal Evaluation Questions

When evaluating Protegrity and Ubiq, teams should ask:

• Are we looking for a broad enterprise data security platform or focused runtime sensitive data protection?
• Do we have existing Protegrity deployments that should remain in place?
• Which use cases require discovery, governance, anonymization, synthetic data, privacy workflows, or centralized enterprise policy?
• Which use cases simply require field and record-level runtime protection?
• Which sensitive fields require identity-aware cleartext authorization at runtime?
• Which workflows receive sensitive data in cleartext today?
• Which users, applications, service accounts, APIs, pipelines, BI tools, and AI workflows can access sensitive values today?
• How much platform footprint are we willing to deploy and operate?
• Do application and data teams need a simpler integration model using software libraries, APIs, database integrations, and workflow-level enforcement?
• 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?
• Do we need semantic search, similarity matching, retrieval, enrichment, or vector workflows on sensitive data?
• Would direct encryption, tokenization, masking, or anonymization of sensitive values break semantic interpretation or vector-based computation?
• Can sensitive source records and identifiers remain protected while AI/vector workflows operate on controlled derived representations?
• 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, storage system, warehouse, or AI workflow?

Summary

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

Ubiq addresses the same overall data protection problem with a focused runtime sensitive data protection model and a simpler software-based integration approach.

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.

Ubiq also helps organizations support AI, RAG, and vector-driven workflows where teams need semantic search, retrieval, or analysis without broadly exposing sensitive source values in plaintext or weakening encryption posture.

Protegrity is a broad enterprise data security platform.

Ubiq is a focused runtime sensitive value protection layer.

Protegrity is often the better fit when the primary need is centralized discovery, governance, privacy, anonymization, synthetic data, and broad enterprise data security program management.

Ubiq is often the better fit when the primary need is easier deployment, lower operational overhead, software-based integration, identity-aware runtime enforcement, and AI/vector workflow support across modern data workflows.

The best fit depends on architecture, deployment model, workflow coverage, governance requirements, privacy needs, AI/data workflow requirements, and the level of identity-aware runtime enforcement required.