Protegrity vs Ubiq
Compare Protegrity with Ubiq runtime sensitive data protection. Learn how Protegrity supports enterprise data security with discovery, governance, tokenization, masking, encryption, anonymization, synthetic data, policy, and audit, and how Ubiq closes the runtime gap between identity access and sensitive data access across applications, databases, warehouses, APIs, BI tools, AI workflows, exports, and downstream systems.
Executive Summary
Protegrity provides enterprise data security capabilities 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 useful 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 not only an encryption, tokenization, masking, or FPE product. Ubiq provides identity-governed runtime data protection for sensitive data. Ubiq sits where sensitive data access actually happens and determines what sensitive data each identity can see and use based on identity, context, and policy.
The key distinction is not whether both platforms protect sensitive data. They do.
The key distinction is the control model.
Protegrity is an enterprise data security platform focused on discovery, governance, tokenization, masking, encryption, anonymization, synthetic data, centralized policy, privacy, and audit. 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 an identity-governed runtime data protection platform. It protects sensitive values directly and governs how those values are revealed to users, applications, service accounts, APIs, data pipelines, BI tools, AI agents, MCP workflows, and downstream systems.
This matters because IAM and IGA systems can determine who gets into an application, database, warehouse, API, or tool. But once access is granted, sensitive data is often still overexposed. Ubiq closes the runtime gap between identity access and sensitive data access.
With Ubiq, the same sensitive data can produce different outcomes depending on who or what is accessing it.
| Accessing identity or workflow | Possible runtime outcome |
|---|---|
| Authorized business user | Full authorized value |
| Support user | Masked or partially redacted value |
| Analytics workflow | De-identified or tokenized value |
| AI/RAG workflow | Controlled derived representation |
| Unauthorized identity | No sensitive data |
| Downstream system | Protected value remains encrypted, tokenized, or masked |
Ubiq also supports modern AI, RAG, semantic search, and vector-driven workflows by separating protection of sensitive source data from AI/vector computation. Sensitive records, identifiers, and regulated fields 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.
This is especially important for regulated data environments where teams want to use AI agents, RAG pipelines, semantic search, vector databases, notebooks, and MCP workflows without turning protected source data into uncontrolled plaintext.
Key Takeaways
- Protegrity and Ubiq both help protect sensitive data, but they differ in architecture, deployment model, operating model, and runtime control approach.
- Protegrity is focused on discovery, governance, tokenization, masking, encryption, anonymization, synthetic data, centralized policy, privacy, 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 provides identity-governed runtime data protection for sensitive data.
- Ubiq evaluates identity, context, and policy at the point of sensitive data access.
- Ubiq can return full data, masked data, de-identified data, tokenized data, encrypted data, or no sensitive data depending on identity and policy.
- Ubiq is designed for software-based integration across applications, databases, warehouses, APIs, BI tools, data pipelines, AI workflows, and downstream systems.
- Ubiq helps close the runtime gap between identity access and sensitive data access.
- Ubiq supports AI/vector-driven workflows by separating protection of sensitive source data from controlled derived representations used for semantic search, retrieval, and analysis.
- Ubiq helps enterprises enable AI use cases on regulated data without broadly expanding plaintext exposure or weakening encryption posture.
- Ubiq provides visibility into sensitive data access patterns, protected records, unprotected records, active datasets, active identities, and top identities.
- Ubiq can map relationships between identities, access groups, and datasets through Access Graph capabilities.
- Ubiq can surface anomalous sensitive data access patterns, such as new identities, new access paths, unusual dataset access, or unexpected protected/unprotected data activity.
Where Protegrity Helps
Protegrity provides enterprise data security capabilities for protecting sensitive data across complex environments.
Its capabilities are commonly used for discovery, governance, tokenization, masking, encryption, anonymization, synthetic data, policy management, and audit. These patterns can be useful when organizations want a broad data security program that spans visibility, governance, privacy, and protection across supported systems.
Protegrity helps answer questions such as:
| Question | Protegrity focus |
|---|---|
| Where does sensitive data exist? | Discover and classify sensitive data across supported environments |
| Which data types need protection? | Identify sensitive fields, data classes, and protection requirements |
| Which fields should be tokenized, masked, encrypted, or anonymized? | Apply supported protection methods based on data type and policy |
| Which policies should apply to which data? | Centralize data protection policy and administration |
| How can sensitive data be used for analytics, AI, or data sharing while reducing exposure? | Apply protection, privacy, anonymization, or synthetic data patterns |
| How can data protection policies be governed and audited centrally? | Provide policy management, governance, and audit workflows |
| How can a data security team standardize protection across different environments? | Apply platform-based data protection patterns across supported systems |
Protegrity is generally associated with enterprise data security patterns where discovery, governance, tokenization, masking, anonymization, synthetic data, centralized policy, and audit are important.
Where Ubiq Is Different
Ubiq is focused on identity-governed runtime data protection.
That means Ubiq is designed to answer a more specific and operational question:
What should this identity be allowed to see or use right now?
That identity may be a user, application, API, service account, data pipeline, BI tool, AI agent, notebook, MCP workflow, or downstream system.
Ubiq protects selected sensitive fields and records, then enforces the runtime data outcome through identity-aware policy. Depending on identity, context, and policy, Ubiq can return full authorized data, masked data, partially redacted data, de-identified data, tokenized data, encrypted data, or no sensitive data.
This is the key difference.
Protegrity focuses primarily on enterprise data security capabilities such as discovery, governance, tokenization, masking, encryption, anonymization, synthetic data, centralized policy, and audit. Ubiq uses protection methods too, but the larger control model is identity-governed runtime data control.
Ubiq allows organizations to protect sensitive values directly while governing how those values are revealed across applications, databases, warehouses, APIs, BI tools, pipelines, AI workflows, and downstream systems. It helps teams maintain separation between system access and sensitive data access, so access to an application or database does not automatically mean access to every sensitive value in cleartext.
Ubiq also helps teams see and understand sensitive data access. This includes visibility into protected and unprotected records, active datasets, active identities, top identities, and sensitive data access patterns. Access Graph capabilities can map relationships between identities, access groups, and datasets, while anomalous event detection can surface unusual access paths or unexpected protected/unprotected data activity.
Comparison Matrix
| Capability / Concern | Protegrity | Ubiq |
|---|---|---|
| Primary purpose | Enterprise data security platform for discovery, governance, tokenization, encryption, masking, anonymization, privacy, policy, and audit | Identity-governed runtime data protection for sensitive data |
| Core control model | Protect and govern sensitive data using discovery, policy, tokenization, masking, encryption, anonymization, synthetic data, and audit | Determine the runtime data outcome based on identity, context, and policy |
| Runtime data outcome | Applies protection methods such as tokenization, masking, encryption, anonymization, and synthetic data through supported policies and enforcement patterns | Can return full data, masked data, de-identified data, tokenized data, encrypted data, or no sensitive data depending on identity and policy |
| Product footprint | Multiple platform capabilities across discovery, governance, protection, privacy, anonymization, synthetic data, policy, and audit | One focused runtime data protection platform for encryption, tokenization, masking, identity-governed access, and cleartext authorization |
| Installation model | May require planning around platform components, policy administration, enforcement points, native integrations, proxies, SDKs, and operational ownership depending on use case | Designed for software libraries, APIs, database integrations, warehouse integrations, BI patterns, pipelines, and AI/data workflows |
| Infrastructure requirements | May involve enterprise platform services, proxies, integration components, policy infrastructure, and deployment planning depending on environment | Primarily software-based integration patterns designed to reduce infrastructure footprint and operational overhead |
| Operational model | Typically operated as part of a broader enterprise data security, privacy, governance, or compliance program | Designed for application, data engineering, analytics, and security teams to deploy runtime protection directly into enterprise workflows |
| Main control point | Centralized data security policy, governance, protection methods, and supported enforcement points | The runtime access point where sensitive data is requested, revealed, masked, tokenized, encrypted, de-identified, or denied |
| Data protection methods | Tokenization, vaultless tokenization, masking, encryption, anonymization, synthetic data, and related protection methods | Encryption, tokenization, masking, and identity-governed runtime data outcomes |
| Discovery and classification | Core platform capability | Can complement discovery outputs, but runtime enforcement is the primary focus |
| Governance and policy | Centralized policy, governance, audit, and insight across supported systems | Runtime policy enforcement focused on sensitive data outcomes by identity, context, and policy |
| Identity-governed access | May integrate with identity or policy systems depending on architecture and deployment model | Core control model: same sensitive data, different identities, different outcomes |
| Runtime cleartext authorization | Supported through Protegrity policy and enforcement patterns | Core design focus using identity, role, application, dataset, and context |
| Implementation experience | Enterprise platform implementation may require coordination across policy, governance, platform components, integrations, proxies, SDKs, and operations teams | Integration through software libraries, APIs, and data workflow patterns designed to reduce deployment complexity |
| Service accounts and automation | Can enforce policies through supported platform integrations | Can restrict whether non-human identities receive sensitive values in cleartext, masked form, de-identified form, tokenized form, encrypted form, or not at all |
| BI and analytics workflows | Supports protected analytics and data use across supported environments | Can enforce identity-governed sensitive data outcomes for BI, dashboards, reporting, analytics, and extracts |
| AI, RAG, and agent workflows | Can support data protection patterns through tokenization, masking, encryption, anonymization, synthetic data, and policy where integrated | Helps controls follow identity through AI tools, RAG workflows, notebooks, agents, MCP tools, APIs, databases, warehouses, vector stores, and downstream systems |
| AI and vector workflows | Tokenization, masking, encryption, anonymization, and synthetic data can reduce exposure, but direct protection of values may disrupt semantic meaning, similarity search, or vector computation if applied directly to values that AI workflows need to interpret | Separates protection of sensitive source data from AI/vector computation so teams can support semantic search, retrieval, and analysis without broadly exposing plaintext sensitive values |
| Semantic utility and regulated data | Sensitive data protection may require tradeoffs when semantic meaning or similarity matching is needed by AI/vector workflows | Preserves semantic utility through controlled derived representations while keeping sensitive source records, identifiers, and regulated fields protected and identity-governed |
| AI agent and MCP workflows | Can protect sensitive data through supported data protection methods and integration patterns | Helps ensure sensitive data controls follow identity through agents, MCP servers, APIs, applications, databases, warehouses, and downstream tools |
| Access visibility | Visibility depends on platform modules, logs, integrations, policy workflows, and audit capabilities | Provides visibility into protected records, unprotected records, active datasets, active identities, top identities, and sensitive data access patterns |
| Access graph | Not typically the primary control model | Maps relationships between identities, access groups, and datasets so teams can understand who or what has access to sensitive data |
| Anomalous access patterns | May require external SIEM, DSPM, DLP, or monitoring workflows depending on architecture | Can surface unusual sensitive data access patterns such as new identities, new access paths, unusual dataset access, or unexpected protected/unprotected activity |
| Downstream persistence | Supports persistent protection patterns across supported environments | Protected values can remain protected when copied, exported, embedded, indexed, replicated, or consumed downstream |
| Architectural orientation | Enterprise data security platform for discovery, governance, tokenization, masking, encryption, anonymization, synthetic data, policy, and audit | Identity-governed runtime data protection across modern application, data, analytics, AI, and downstream workflows |
Key Architectural Differences
Enterprise Data Security vs Identity-Governed Runtime Data Control
Protegrity is an enterprise data security platform. It is known for discovery, classification, governance, tokenization, masking, encryption, anonymization, synthetic data, policy, audit, and privacy capabilities.
That model is useful when an organization wants a broad data security program that spans discovery, governance, privacy, and protection. However, traditional enterprise data security platforms often focus primarily on the protection method, governance workflow, or data security program: discover the data, classify it, tokenize the value, mask the value, encrypt the value, anonymize it, generate synthetic data, define policy, or audit activity.
Those capabilities are important, but they do not fully solve the runtime access problem.
The harder question is:
What should this identity be allowed to see or use right now?
Ubiq is designed around that runtime question. Ubiq evaluates identity, context, and policy at the point of sensitive data access. Based on that decision, the same sensitive data can produce different outcomes for different identities, applications, service accounts, BI tools, AI workflows, and downstream systems.
This is the runtime gap between identity access and sensitive data access.
IAM and IGA systems can determine who gets into an application, database, warehouse, API, or tool. But once access is granted, sensitive data is often still overexposed. Ubiq closes that gap by making the runtime data access point the control layer for sensitive data.
Protection Method vs Runtime Data Outcome
Protegrity provides protection and privacy methods such as tokenization, masking, encryption, anonymization, and synthetic data. These capabilities are useful when organizations need to reduce exposure, support analytics, enforce privacy controls, or support governed data use across supported environments.
Ubiq also supports data protection methods such as encryption, tokenization, and masking. The architectural difference is that Ubiq emphasizes the runtime data outcome.
With Ubiq, the question is not only:
Which fields should be discovered, classified, tokenized, masked, encrypted, anonymized, or protected?
The question becomes:
What should this user, application, service account, API, pipeline, BI tool, AI agent, or downstream workflow receive at runtime?
| Runtime scenario | Ubiq data outcome |
|---|---|
| Full access is authorized | Full sensitive value |
| Limited access is authorized | Masked or partially redacted value |
| Analytics access is authorized | De-identified or tokenized value |
| AI/vector workflow needs semantic utility | Controlled derived representation |
| Access is not authorized | No sensitive data |
| Data moves downstream | Protected value remains encrypted, tokenized, or masked |
That distinction is especially important when many identities and workflows touch the same data but should not receive the same level of sensitive data visibility.
Multiple Platform Capabilities vs One Runtime Protection Platform
Protegrity deployments can involve multiple platform capabilities depending on the desired outcome. A deployment may involve discovery, governance, policy management, tokenization, encryption, masking, anonymization, synthetic data, native integrations, proxies, SDKs, audit workflows, reporting workflows, and operational administration.
Those capabilities may require architecture planning, implementation decisions, operational ownership, platform administration, and ongoing coordination across security, privacy, data, analytics, and application teams.
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, runtime data outcome control, and 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.
Complex Integration Patterns vs Software-Based Integration
Protegrity supports multiple enforcement patterns, including native integrations, proxies, and SDKs. That breadth can be useful 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. It 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, and AI/RAG workflows.
With Ubiq, application, data, analytics, and security teams can focus on the actual data protection questions:
| Question | Why it matters |
|---|---|
| Which fields or records need protection? | Defines the sensitive data control surface |
| Which identities can see full data? | Separates system access from sensitive data access |
| Which identities should receive masked or de-identified data? | Supports least privilege at the data outcome level |
| Which applications or workflows need enforcement? | Extends control across runtime access paths |
| What should service accounts receive? | Reduces overexposure through automation |
| What should AI workflows receive? | Supports AI use cases without broad plaintext exposure |
| What happens when data is copied or exported? | Keeps protection attached to downstream data movement |
Teams do not need to start by deploying a broad data security platform footprint before enforcing runtime protection.
AI, RAG, and Vector Workflows Without Broad Plaintext Exposure
AI, RAG, semantic search, vector databases, notebooks, MCP servers, and agent workflows create a difficult data protection challenge.
Data teams want to use sensitive or regulated data for semantic search, similarity matching, retrieval, model enrichment, customer intelligence, fraud analysis, clinical search, financial research, support automation, and AI-assisted decisioning. But if sensitive values are encrypted, tokenized, masked, or anonymized in the wrong way, the semantic meaning needed for vector search and retrieval can break.
This creates a painful tradeoff for regulated enterprises:
| Tradeoff | Result |
|---|---|
| Keep sensitive data strongly protected | AI, search, and retrieval usefulness may be limited |
| Enable AI/vector search with broad plaintext access | Sensitive data exposure expands across notebooks, vector stores, prompts, agents, logs, and downstream systems |
| Create separate AI copies with weaker controls | Governance, auditability, and compliance become harder |
| Tokenize, mask, or anonymize everything before AI processing | Semantic meaning, similarity matching, and retrieval quality may degrade |
| Let AI agents use existing service accounts | Sensitive data access may be inherited from broad system-level permissions rather than governed at the data outcome level |
Ubiq helps avoid this tradeoff by separating sensitive source data protection from AI/vector computation.
Sensitive source records, identifiers, and regulated fields can remain strongly protected and identity-governed. AI/vector workflows can operate on controlled derived representations that preserve the functionality required for semantic search, similarity matching, retrieval, enrichment, and analysis.
This does not mean raw encrypted, tokenized, masked, or anonymized values automatically preserve semantic meaning. They usually do not. The point is that Ubiq supports architectures where the sensitive source data remains protected, while the derived representations used for semantic and vector workflows are controlled, governed, and separated from the protected source data.
That separation matters because AI access is often indirect. A user may prompt an AI agent, which calls an MCP server, which calls an API, which queries a database, warehouse, application, or vector store. Traditional identity controls may verify the user or service account at the system boundary, but they often do not determine what sensitive data should be revealed at each runtime step.
Ubiq helps controls follow identity through the workflow.
| AI workflow component | Sensitive data risk | Ubiq runtime control |
|---|---|---|
| RAG pipeline | Sensitive source records may be retrieved into prompts or context windows | Govern what source data can be revealed and when |
| Vector database | Embeddings or metadata may expose regulated context if not controlled | Separate protected source data from controlled derived representations |
| AI agent | Agent may inherit broad tool, API, or service account access | Evaluate identity, context, and policy at runtime data access points |
| MCP workflow | Tool calls may indirectly access sensitive systems | Control sensitive data outcomes across the workflow, not only at login |
| Notebook or data science workflow | Analysts may copy, inspect, export, or enrich sensitive data | Return full, masked, de-identified, tokenized, encrypted, or no data based on policy |
| Downstream AI system | Sensitive data may move into logs, caches, model outputs, or derived datasets | Keep protected values governed as data is copied, exported, embedded, indexed, or consumed downstream |
This allows enterprises to enable AI-driven workflows without turning sensitive source data into uncontrolled plaintext or weakening the protection model around the records and identifiers that matter.
The goal is not to weaken encryption, anonymize everything blindly, or tokenize everything before AI processing. The goal is to allow AI/vector workflows to function while keeping sensitive source data protected, governed, and revealed only according to identity, context, and policy.
Visibility, Access Graph, and Anomalous Access Patterns
Runtime data control is not only about enforcing access. It is also about understanding how sensitive data is being accessed and used.
Ubiq can provide visibility into protected records, unprotected records, active datasets, active identities, top identities, and sensitive data access patterns. This helps teams understand not only what data is protected, but who and what is interacting with that data.
Ubiq can also map relationships between identities, access groups, and datasets through Access Graph capabilities. This helps teams understand who or what has access to sensitive data across applications, APIs, databases, warehouses, BI tools, AI workflows, and downstream systems.
That visibility matters because sensitive data exposure often comes from runtime access paths that are difficult to see from IAM, IGA, database permissions, policy logs, or audit workflows alone.
Ubiq can also surface anomalous events, including new identities accessing sensitive datasets, existing identities using new access paths, unusual dataset access, unexpected protected or unprotected data activity, and sensitive data access through a new application, service, API, notebook, BI tool, or AI workflow.
These capabilities help organizations move beyond static protection and toward runtime governance of sensitive data access.
Enterprise Data Security Program vs Workflow-Level Runtime Enforcement
Protegrity is commonly positioned as part of an enterprise data security program with discovery, governance, privacy, protection, and audit across many systems. That approach can make sense when an organization wants a broad data security program with centralized policy and multiple protection methods across supported environments.
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/RAG workflows, and downstream systems.
Traditional Data Protection Programs vs Modern Identity, AI, and Analytics Workflows
Protegrity is commonly used in enterprise environments with data discovery, governance, privacy, analytics, data sharing, and regulated data protection requirements.
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 users, applications, APIs, service accounts, data pipelines, warehouses, BI tools, dashboards, event streams, RAG systems, AI agents, MCP tools, notebooks, vector stores, downstream replicas, and vendor feeds.
Identity worked better when data access was direct.
Sensitive data now has more consumers than ever. Controls need to follow identity through the workflow.
Ubiq is built to enforce sensitive value access across these runtime paths, not only inside a traditional application, database, governance, or privacy control point.
How Ubiq Differentiates from Protegrity
Identity-governed runtime outcomes
Ubiq controls what sensitive data each identity can see and use at runtime. Instead of only asking whether a field should be discovered, governed, tokenized, masked, encrypted, anonymized, or synthesized, Ubiq asks what outcome should be returned for this identity, in this context, at this moment.
That runtime outcome can be full data, masked data, de-identified data, tokenized data, encrypted data, or no sensitive data.
Modern workflow coverage
Ubiq is designed to enforce sensitive data protection across modern software and data workflows, including applications, APIs, databases, warehouses, service accounts, data pipelines, BI tools, AI agents, MCP workflows, vector stores, and downstream systems.
This helps organizations maintain control even when sensitive data moves beyond the original application, database, warehouse, analytics environment, or governance workflow.
AI, RAG, and vector workflow support
Ubiq supports AI/vector-driven workflows by allowing sensitive source data to remain protected while controlled derived representations support semantic search, similarity matching, retrieval, enrichment, and analysis.
This matters for RAG pipelines, vector databases, semantic search, AI agents, notebooks, MCP workflows, and downstream AI systems that need to operate on regulated data without expanding plaintext exposure.
The goal is not to weaken encryption, anonymize everything blindly, or tokenize everything before AI processing. The goal is to allow AI/vector workflows to function while keeping sensitive source data protected, governed, and revealed only according to identity, context, and policy.
Visibility and governance
Ubiq gives teams visibility into who and what is accessing protected data. It can show protected records, unprotected records, active datasets, active identities, top identities, and sensitive data access patterns.
Access Graph capabilities help map relationships between identities, access groups, and datasets. Anomalous event detection can surface unusual or suspicious sensitive data access patterns, such as new identities, new access paths, unusual dataset access, or unexpected protected/unprotected data activity.
The key evaluation question is not only which product can discover, classify, tokenize, mask, encrypt, anonymize, or synthesize data.
The key question is:
Which platform controls what sensitive data each identity can see and use at runtime across applications, APIs, service accounts, data pipelines, BI tools, AI workflows, and downstream systems?
Internal Evaluation Questions
Runtime access control
- Are we trying to protect data only, or control how sensitive data is revealed at runtime?
- Can we control what sensitive data each identity sees and uses?
- Can the same sensitive field return full, masked, de-identified, tokenized, encrypted, or no data depending on policy?
- Can we enforce this across users, applications, service accounts, APIs, BI tools, AI workflows, and downstream systems?
Implementation and operating model
- How much platform footprint are we willing to deploy and operate?
- Do we need a broad discovery, governance, privacy, and protection program, or do we need software-based integration into modern applications and data workflows?
- Which use cases require discovery, governance, anonymization, synthetic data, privacy workflows, centralized policy, or platform-specific enforcement patterns?
- Which use cases require field and record-level runtime protection?
AI, RAG, vector search, and downstream exposure
- 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?
- Can controls follow identity through AI agents, MCP tools, APIs, databases, warehouses, and downstream systems?
- What happens when sensitive data is exported, copied, logged, joined, materialized, embedded, indexed, or replicated?
Visibility and governance
- Can we see which identities are accessing protected records?
- Can we distinguish protected vs unprotected record activity?
- Can we map identities, access groups, and datasets?
- Can we detect new or unusual sensitive data access paths?
- Can we understand how AI agents, service accounts, and pipelines access sensitive data?
Summary
Protegrity provides enterprise data security capabilities for discovery, governance, tokenization, encryption, masking, anonymization, synthetic data, privacy, policy, and audit.
Ubiq addresses the broader runtime access problem: controlling what sensitive data each identity can see and use at the point of access.
By protecting selected sensitive values directly and governing runtime data outcomes through identity, context, and 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, semantic search, and vector-driven workflows where teams need search, retrieval, or analysis without broadly exposing sensitive source values in plaintext or weakening encryption posture.
Protegrity is centered on enterprise data security capabilities such as discovery, governance, tokenization, masking, encryption, anonymization, synthetic data, centralized policy, privacy, and audit.
Ubiq is an identity-governed runtime data protection platform centered on runtime data control: same sensitive data, different identities, different outcomes.
For organizations trying to close the runtime gap between identity access and sensitive data access, Ubiq provides a software-based approach to controlling how sensitive data is revealed across modern applications, APIs, databases, warehouses, analytics tools, AI workflows, and downstream systems.