Thales CipherTrust vs Ubiq
Compare Thales CipherTrust with Ubiq runtime sensitive data protection. Learn how CipherTrust supports enterprise data security with key management, transparent encryption, tokenization, masking, discovery, and centralized policy, 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
Thales CipherTrust Data Security Platform provides enterprise data security capabilities across key management, data discovery, classification, transparent encryption, tokenization, masking, centralized policy, and enterprise-scale data protection.
These capabilities are useful for organizations that need centralized key management, transparent encryption for files, storage, databases, and infrastructure, enterprise tokenization, discovery, and broad cryptographic control across hybrid and cloud 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.
CipherTrust is an enterprise data security platform focused on key management, transparent encryption, tokenization, masking, discovery, classification, centralized policy, and infrastructure-oriented protection. Depending on the deployment model, CipherTrust implementations may involve CipherTrust Manager, key management services, transparent encryption agents, connectors, tokenization services, discovery and classification modules, policy administration, HSM or cloud KMS integrations, infrastructure planning, and ongoing platform operations.
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
- Thales CipherTrust and Ubiq both help protect sensitive data, but they differ significantly in architecture, deployment model, operating model, and runtime control approach.
- CipherTrust is focused on enterprise key management, transparent encryption, tokenization, masking, discovery, classification, centralized policy, and infrastructure-oriented data protection.
- CipherTrust deployments may involve multiple modules, centralized managers, connectors, agents, key management services, HSM integrations, cloud KMS integrations, and infrastructure 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 Thales CipherTrust Helps
Thales CipherTrust provides enterprise data security capabilities for complex hybrid and cloud environments.
Its capabilities are commonly used for centralized key management, transparent encryption, tokenization, masking, discovery, classification, policy management, and infrastructure-level data protection. These patterns can be useful when organizations need centralized control over keys, protection for storage and infrastructure layers, or data protection workflows across supported systems.
CipherTrust helps answer questions such as:
| Question | CipherTrust focus |
|---|---|
| Where are encryption keys managed? | Centralize key management across supported environments |
| Which systems need transparent encryption? | Protect files, storage, databases, big data, containers, and infrastructure workloads |
| Which data stores contain sensitive data? | Discover and classify sensitive data across supported repositories |
| Which values should be tokenized, masked, or redacted? | Apply supported protection methods based on data type and policy |
| Which policies should apply across hybrid environments? | Centralize data security policy and administration |
| Which privileged or infrastructure access paths need additional controls? | Apply infrastructure-oriented data protection and policy controls |
| Which data access or key usage events should be audited? | Provide logs, audit events, and visibility through supported platform workflows |
CipherTrust is generally associated with enterprise data security patterns where key management, transparent encryption, discovery, centralized policy, and infrastructure-level protection 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.
CipherTrust focuses primarily on enterprise data security controls such as key management, transparent encryption, tokenization, masking, discovery, classification, and centralized policy. 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 | Thales CipherTrust | Ubiq |
|---|---|---|
| Primary purpose | Enterprise data security platform for key management, discovery, transparent encryption, tokenization, masking, policy, and audit | Identity-governed runtime data protection for sensitive data |
| Core control model | Protect sensitive data through enterprise data security controls, key management, transparent encryption, tokenization, masking, and centralized policy | Determine the runtime data outcome based on identity, context, and policy |
| Runtime data outcome | Applies protection methods such as transparent encryption, tokenization, masking, redaction, and policy through supported modules and integrations | 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 and modules across key management, transparent encryption, tokenization, discovery, classification, 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 CipherTrust Manager, product modules, connectors, agents, key management configuration, HSM integrations, cloud KMS integrations, and infrastructure 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 centralized managers, VMs, appliances, HSM integrations, agents, connectors, tokenization services, or infrastructure components depending on deployment pattern | Primarily software-based integration patterns designed to reduce infrastructure footprint and operational overhead |
| Operational model | Typically operated by security, platform, infrastructure, or cryptography teams as part of a broader enterprise data security program | Designed for application, data engineering, analytics, and security teams to deploy runtime protection directly into enterprise workflows |
| Main control point | CipherTrust Manager, centralized key management, policies, transparent encryption agents/connectors, tokenization services, and supported integrations | The runtime access point where sensitive data is requested, revealed, masked, tokenized, encrypted, de-identified, or denied |
| Data protection methods | Transparent encryption, tokenization, vaultless tokenization, masking, redaction, key management, and related enterprise controls | Encryption, tokenization, masking, and identity-governed runtime data outcomes |
| Key management | Core capability, including enterprise key management and centralized control | Built-in KMS/HSM options, BYOK/CMK, and BYOHSM support depending on deployment requirements |
| Discovery and classification | Part of the broader CipherTrust platform | Can complement discovery outputs, but runtime enforcement is the primary focus |
| Transparent encryption | Core capability for infrastructure, files, storage, databases, big data, and related environments | Not the primary control model; Ubiq focuses on protecting sensitive values and governing runtime data outcomes |
| 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 CipherTrust policy and integration patterns | Core design focus using identity, role, application, dataset, and context |
| Implementation experience | Enterprise platform implementation may require coordination across modules, infrastructure, connectors, agents, key management, HSMs, 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 product modules and 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, policy, and supported integrations | 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 | Encryption, tokenization, masking, and transparent encryption can protect sensitive data, 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, 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 security platform for key management, transparent encryption, tokenization, masking, discovery, and centralized control | 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
CipherTrust is an enterprise data security platform. It is known for key management, transparent encryption, tokenization, masking, discovery, classification, centralized policy, and audit.
That model is useful when an organization needs centralized key management, transparent encryption, infrastructure-layer protection, discovery, or broad data security policy across supported systems. However, traditional enterprise data security platforms often focus primarily on the protection method or infrastructure control point: manage the key, encrypt the file, encrypt the database, tokenize the value, mask the value, discover sensitive data, or centralize policy.
Those controls 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
CipherTrust provides protection methods such as key management, transparent encryption, tokenization, masking, and redaction. These capabilities are useful when organizations need infrastructure protection, key control, discovery, or broad enterprise data security workflows.
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 system, file, database, or field 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 Modules and Infrastructure vs One Runtime Protection Platform
CipherTrust deployments can involve multiple enterprise components depending on the desired outcome. A deployment may involve CipherTrust Manager, enterprise key management, transparent encryption, tokenization, discovery and classification, connectors or agents, HSM or cloud KMS integrations, policy administration workflows, infrastructure planning, and operational ownership.
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
CipherTrust can support enterprise integration patterns for applications, databases, infrastructure, and data workflows, but those patterns may involve centralized managers, transparent encryption agents, connectors, key management integrations, HSM integrations, tokenization services, infrastructure components, or platform-specific deployment patterns.
That is often appropriate for infrastructure-level encryption, centralized key management, storage protection, or broad compliance-driven deployments.
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, 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
CipherTrust is commonly deployed as part of an enterprise data security program with centralized management, key management, infrastructure protection, and integration planning. That approach can make sense for large regulated environments, especially where there are broad key management, transparent encryption, or infrastructure-layer protection requirements.
However, application and data teams may experience that model as heavier if they need to coordinate with platform owners, configure centralized managers, align key management, deploy agents or connectors, 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.
Infrastructure-Level Protection vs Modern Identity, AI, and Analytics Workflows
CipherTrust is commonly used in enterprise environments with infrastructure, storage, database, key management, and transparent encryption 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 an infrastructure, storage, or database encryption control point.
How Ubiq Differentiates from Thales CipherTrust
Identity-governed runtime outcomes
Ubiq controls what sensitive data each identity can see and use at runtime. Instead of only asking whether a file, database, field, or storage layer should be encrypted, tokenized, masked, or protected, 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, storage system, 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 manage keys, encrypt files, tokenize values, mask fields, or discover sensitive 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 infrastructure are we willing to deploy and operate?
- Do we need centralized key management, transparent encryption, and infrastructure protection, or do we need software-based integration into modern applications and data workflows?
- Which use cases require centralized managers, agents, connectors, HSM integrations, cloud KMS integrations, or platform-specific deployment 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
Thales CipherTrust provides enterprise data security capabilities for key management, discovery, classification, transparent encryption, tokenization, masking, centralized policy, and infrastructure-oriented protection.
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.
CipherTrust is an enterprise data security platform centered on key management, transparent encryption, tokenization, masking, discovery, classification, and centralized policy.
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.