Voltage SecureData vs Ubiq
Compare OpenText Voltage SecureData with Ubiq runtime sensitive data protection. Learn how Voltage supports enterprise data protection with format-preserving encryption, tokenization, masking, hashing, and key management, 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
OpenText Voltage SecureData, now part of OpenText’s broader data privacy and protection portfolio, provides enterprise data protection capabilities for protecting sensitive data across complex environments. Its capabilities include format-preserving encryption, tokenization, masking, hashing, centralized policy, key management, and enterprise-scale data protection patterns.
These capabilities are useful for organizations that need format-preserving encryption, enterprise tokenization, legacy application compatibility, centralized data protection policy, and broad data protection coverage across regulated environments.
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.
Voltage SecureData is an enterprise data protection platform focused on format-preserving encryption, tokenization, masking, hashing, key management, and centralized control. Depending on the deployment model, Voltage implementations may involve centralized services, policy infrastructure, integration components, application changes, proxies, gateways, or other enterprise deployment patterns.
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
- OpenText Voltage SecureData and Ubiq both help protect sensitive data, but they differ significantly in architecture, deployment model, operating model, and runtime control approach.
- Voltage SecureData is focused on format-preserving encryption, tokenization, masking, hashing, centralized control, key management, and enterprise data protection use cases.
- Voltage deployments may involve multiple enterprise components, centralized policy services, integration patterns, infrastructure planning, and operational ownership 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 OpenText Voltage SecureData Helps
OpenText Voltage SecureData provides enterprise data protection capabilities for sensitive data across complex environments.
Its capabilities are commonly used to protect sensitive fields through format-preserving encryption, tokenization, masking, hashing, centralized policy, and key management. These patterns can be useful when protected values need to retain their original format for legacy applications, databases, payment workflows, or other regulated systems.
Voltage SecureData helps answer questions such as:
| Question | Voltage SecureData focus |
|---|---|
| Which sensitive fields require encryption, tokenization, masking, or hashing? | Identify and apply data protection methods to sensitive values |
| Which values need format preservation? | Protect data while maintaining field structure for compatible systems |
| How can protected data remain usable in legacy systems? | Use FPE or tokenization patterns where format compatibility matters |
| Which policies should apply to protected fields? | Centralize protection policy and administration |
| How should keys be managed centrally? | Manage data protection keys and related cryptographic controls |
| How can regulated data be protected across enterprise environments? | Apply protection patterns across supported applications, databases, and workflows |
Voltage SecureData is generally associated with enterprise data protection patterns where format preservation, legacy compatibility, centralized policy, and enterprise tokenization 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.
Voltage SecureData focuses primarily on data protection methods such as format-preserving encryption, tokenization, masking, hashing, centralized policy, and key management. 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 | OpenText Voltage SecureData | Ubiq |
|---|---|---|
| Primary purpose | Enterprise data protection using FPE, tokenization, masking, hashing, key management, and centralized control | Identity-governed runtime data protection for sensitive data |
| Core control model | Protect sensitive data using data protection methods and centralized policy | Determine the runtime data outcome based on identity, context, and policy |
| Runtime data outcome | Primarily applies protection methods such as encryption, tokenization, masking, hashing, or FPE through supported integrations and policy 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 | Enterprise data protection platform with capabilities for FPE, tokenization, masking, hashing, key management, policy, and supported integrations | One focused runtime data protection platform for encryption, tokenization, masking, identity-governed access, and cleartext authorization |
| Installation model | May require planning around centralized services, policy infrastructure, integration components, proxies, gateways, application changes, or operational ownership depending on deployment model | Designed for software libraries, APIs, database integrations, warehouse integrations, BI patterns, pipelines, and AI/data workflows |
| Infrastructure requirements | May involve centralized data protection services, enterprise infrastructure, key management components, integration layers, or managed deployment patterns | Primarily software-based integration patterns designed to reduce infrastructure footprint and operational overhead |
| Operational model | Typically operated as part of a broader enterprise data protection, compliance, or security program | Designed for application, data engineering, analytics, and security teams to deploy runtime protection directly into enterprise workflows |
| Main control point | Voltage data protection policies, key management, FPE/tokenization services, and supported enterprise integrations | The runtime access point where sensitive data is requested, revealed, masked, tokenized, encrypted, de-identified, or denied |
| Data protection methods | Format-preserving encryption, tokenization, masking, hashing, and related enterprise data protection patterns | Encryption, tokenization, masking, and identity-governed runtime data outcomes |
| Format preservation | Core capability, especially for legacy application and database compatibility | Supported where format-preserving or tokenized workflows are required |
| 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 Voltage policy and integration patterns | Core design focus using identity, role, application, dataset, and context |
| Implementation experience | Enterprise platform implementation may require coordination across policy, key management, infrastructure, integrations, applications, 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 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 through supported platform integrations | 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 encryption, tokenization, masking, 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 | Traditional encryption or tokenization can protect sensitive values, but 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, and audit workflows | 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 protection platform for FPE, tokenization, masking, hashing, and centralized control | Identity-governed runtime data protection across modern application, data, analytics, AI, and downstream workflows |
Key Architectural Differences
Enterprise Data Protection vs Identity-Governed Runtime Data Control
Voltage SecureData is an enterprise data protection platform. It is known for format-preserving encryption, tokenization, masking, hashing, centralized policy, and key management.
That model is useful when an organization needs format preservation, legacy application compatibility, or enterprise tokenization and encryption patterns. However, traditional enterprise data protection platforms often focus primarily on the protection method: encrypt the value, tokenize the value, mask the value, hash the value, preserve the format, or centralize key and policy management.
Those methods 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
Voltage SecureData has deep roots in format-preserving encryption and tokenization. These capabilities are useful when protected values must retain their original structure for legacy systems, databases, payment workflows, or regulated applications.
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 encrypted, tokenized, masked, 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 Components and Deployment Patterns vs One Runtime Protection Platform
Voltage deployments can involve multiple enterprise components depending on the desired outcome. A deployment may involve format-preserving encryption, tokenization, masking, hashing, centralized policy, key management, application integration, proxy or gateway-style integration patterns, infrastructure planning, and operational administration.
Those capabilities may require architecture planning, procurement decisions, deployment coordination, infrastructure ownership, operational monitoring, and ongoing platform administration.
Ubiq is designed as one runtime sensitive data protection platform. Instead of requiring teams to assemble and operate multiple components to protect sensitive values across workflows, 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
Voltage can support enterprise integration patterns for applications, databases, and data workflows, but those patterns may involve centralized services, policy infrastructure, proxies, gateways, application changes, key management components, or platform-specific integrations.
That is often appropriate for legacy systems, regulated environments, format-preserving requirements, or large enterprise data protection programs.
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 protection 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, or masked 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 or mask 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 or tokenized 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 or tokenize everything blindly. 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, or encryption logs 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.
Centralized Enterprise Platform vs Workflow-Level Runtime Enforcement
Voltage SecureData is commonly deployed as part of an enterprise data protection program with centralized policy, key management, and integration planning. That approach can make sense for large regulated environments, especially where there are legacy format-preserving requirements.
However, application and data teams may experience that model as heavier if they need to coordinate with platform owners, configure centralized services, align key management, modify integration paths, and wait for shared infrastructure before protecting sensitive fields.
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 large centralized infrastructure project.
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
Voltage SecureData is commonly used in enterprise environments with structured data, legacy applications, and regulated workloads.
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 or database control point.
How Ubiq Differentiates from OpenText Voltage SecureData
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 encrypted, tokenized, masked, or format-preserved, 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, or analytics environment.
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 or tokenize everything blindly. 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 encrypt, tokenize, mask, or preserve format.
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 infrastructure are we willing to deploy and operate?
- Do we need legacy FPE compatibility, or do we need software-based integration into modern applications and data workflows?
- Which use cases require centralized services, proxies, gateways, or platform-specific integration 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, or masking 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
OpenText Voltage SecureData provides enterprise data protection capabilities for format-preserving encryption, tokenization, masking, hashing, key management, and centralized control.
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.
Voltage SecureData is an enterprise data protection platform centered on data protection methods such as FPE, tokenization, masking, hashing, centralized policy, and key management.
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.