Skyflow vs Ubiq
Compare Skyflow Data Privacy Vault with Ubiq runtime sensitive data protection. Learn how Skyflow supports sensitive data isolation, tokenization, redaction, and API-brokered access, 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
Skyflow provides a data privacy vault designed to isolate, protect, tokenize, redact, and govern sensitive customer data such as PII, PCI, and PHI. Its architecture helps organizations centralize sensitive data into a dedicated vault and access that data through APIs, tokens, redaction, encryption, and policy controls.
These capabilities are useful when an organization wants to reduce sensitive data spread by moving sensitive fields into a dedicated privacy vault and brokering access through a controlled API layer.
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.
Skyflow is a privacy vault architecture. Its model centers on isolating sensitive customer data in a dedicated vault and routing sensitive data access through vault APIs, tokens, redaction, detokenization, and policy controls.
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 without requiring a centralized vault to become the primary system of record for sensitive data.
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
- Skyflow and Ubiq both help protect sensitive data, but they use different architectural models.
- Skyflow is centered on a data privacy vault architecture that isolates sensitive data and provides tokenization, redaction, encryption, API-based access, and governance controls.
- Skyflow deployments may require application and data flows to store, retrieve, tokenize, detokenize, or redact sensitive data through a vault API layer.
- 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 Skyflow Helps
Skyflow provides a data privacy vault architecture for isolating and protecting sensitive customer data.
Its capabilities are commonly used to centralize sensitive data, reduce the number of systems storing raw PII, tokenize sensitive values, redact or mask data, and broker access through vault APIs. This model can be useful when teams want a dedicated privacy layer for customer data collection, tokenization, detokenization, and controlled retrieval.
Skyflow helps answer questions such as:
| Question | Skyflow focus |
|---|---|
| Where should sensitive customer data be stored? | Centralize sensitive values in a dedicated privacy vault |
| How can we reduce the number of systems that store PII? | Replace sensitive values in downstream systems with tokens or redacted values |
| How can applications use tokens instead of raw sensitive values? | Route sensitive data collection and retrieval through vault APIs |
| Which applications or users can detokenize sensitive data? | Govern access through vault policies, API calls, and authorization controls |
| How can sensitive values be redacted before entering third-party or AI workflows? | Use redaction, masking, tokenization, or controlled detokenization workflows |
| How can sensitive data collection be governed? | Use a dedicated privacy API layer for collection, storage, and retrieval |
Skyflow is generally associated with privacy vault patterns where sensitive customer data isolation, API-brokered access, and tokenized application workflows 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.
Skyflow centers the architecture around a data privacy vault. Ubiq centers the architecture around the sensitive data access point.
Ubiq allows organizations to protect sensitive values where they already live 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, database, warehouse, or API 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 | Skyflow Data Privacy Vault | Ubiq |
|---|---|---|
| Primary purpose | Isolate, protect, tokenize, redact, and govern sensitive customer data through a dedicated privacy vault | Identity-governed runtime data protection for sensitive data |
| Core control model | Centralize sensitive data in a vault and broker access through APIs, tokens, redaction, detokenization, and policies | Determine the runtime data outcome based on identity, context, and policy |
| Runtime data outcome | Primarily governed through vault access, tokenization, detokenization, redaction, and API workflows | Can return full data, masked data, de-identified data, tokenized data, encrypted data, or no sensitive data depending on identity and policy |
| Product footprint | Data privacy vault, vault APIs, tokenization, detokenization, redaction, access controls, policy, and governance workflows | One focused runtime data protection platform for encryption, tokenization, masking, identity-governed access, and cleartext authorization |
| Architectural model | Centralized privacy vault for sensitive data isolation | Runtime protection across existing applications, databases, warehouses, APIs, BI, AI, and downstream workflows |
| Sensitive data location | Sensitive values are stored in or routed through the vault | Sensitive values can be protected where they already live |
| Installation model | May require application and data flows to insert, retrieve, tokenize, detokenize, or redact sensitive data through the vault | Designed for software libraries, APIs, database integrations, warehouse integrations, BI patterns, pipelines, and AI/data workflows |
| Infrastructure requirements | Requires a vault-centered architecture and API-based integration with applications and downstream systems | Primarily software-based integration patterns designed to reduce infrastructure footprint and operational overhead |
| Operational model | Often used as a dedicated privacy service for sensitive customer data collection, storage, tokenization, and retrieval | Designed for application, data engineering, analytics, and security teams to deploy runtime protection directly into enterprise workflows |
| Main control point | Vault APIs, tokenization, detokenization, redaction, encryption, policies, and access controls around vaulted data | The runtime access point where sensitive data is requested, revealed, masked, tokenized, encrypted, de-identified, or denied |
| Tokenization | Core capability for replacing sensitive values with tokens | Supported as one protection method alongside encryption, masking, and identity-governed runtime data outcomes |
| Detokenization / cleartext access | Governed through vault access controls and API workflows | Governed by runtime policy using identity, role, application, dataset, and context |
| Databases and warehouses | Often used to keep raw sensitive values out of primary databases or analytics systems | Can protect sensitive values inside databases, warehouses, and downstream data workflows |
| 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 |
| BI and analytics workflows | Can support tokenized or de-identified analytics patterns where data is routed through vault workflows | Can enforce identity-governed sensitive data outcomes for BI, dashboards, reporting, analytics, and extracts |
| AI, RAG, and agent workflows | Supports AI privacy patterns such as redaction, tokenization, controlled detokenization, and runtime AI data controls | 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 and redaction can reduce sensitive data exposure, but direct tokenization or redaction 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 vault APIs, tokenization, redaction, and controlled retrieval patterns | Helps ensure sensitive data controls follow identity through agents, MCP servers, APIs, applications, databases, warehouses, and downstream tools |
| Access visibility | Visibility depends on vault logs, API calls, 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 | Tokenized values can reduce exposure when used downstream | Protected values can remain protected when copied, exported, embedded, indexed, replicated, or consumed downstream |
| Architectural orientation | Privacy vault architecture for sensitive data isolation, tokenization, redaction, and API-brokered access | Identity-governed runtime data protection across existing application, data, analytics, AI, and downstream workflows |
Key Architectural Differences
Privacy Vault Architecture vs Identity-Governed Runtime Data Control
Skyflow is built around a data privacy vault model. In that model, sensitive data is isolated in a dedicated vault. Applications store sensitive values in the vault and use tokens, redacted values, or controlled retrieval to reduce exposure across the rest of the application stack.
That model can be useful when an organization wants to centralize sensitive customer data and route sensitive data access through a dedicated privacy API layer.
Ubiq is built around identity-governed runtime data control.
The harder question is:
What should this identity be allowed to see or use right now?
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.
Centralized Vault vs Protection Where Data Already Lives
A privacy vault can reduce sensitive data spread by centralizing sensitive fields. That can be useful for new applications, customer data collection, privacy workflows, and environments where teams want to keep PII out of primary application databases.
However, many enterprises already have sensitive data spread across existing systems: applications, databases, warehouses, data lakes, APIs, BI tools, data pipelines, AI workflows, vendor feeds, historical records, service accounts, and downstream systems.
Ubiq is designed for these environments.
It allows organizations to protect sensitive values without requiring every sensitive value to be moved into a centralized vault first. This is especially important when the sensitive data problem already exists across production systems, analytics platforms, historical records, service accounts, business workflows, and downstream copies.
Vault APIs and Middle-Layer Access vs Software-Based Runtime Enforcement
Skyflow’s model often requires applications and data flows to interact with the vault through APIs or SDKs to insert, retrieve, tokenize, detokenize, or redact sensitive values.
That API-centered architecture can be appropriate when teams want a dedicated privacy middle layer for sensitive customer data.
Ubiq uses a different model.
Ubiq is designed to integrate into the workflows where sensitive data is already 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 making a centralized privacy vault the primary control point for every sensitive value.
Protection Method vs Runtime Data Outcome
Skyflow commonly uses tokenization, redaction, controlled retrieval, and vault policies as part of its privacy vault architecture. Tokens replace sensitive values in applications and downstream systems, while the vault retains the sensitive values and controls detokenization.
Ubiq supports tokenization too, but tokenization is one protection method within a broader runtime protection model.
With Ubiq, the question is not only:
Should this value be tokenized, redacted, or stored in a vault?
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 |
With Skyflow, the architecture is often centered on the vault and the token.
With Ubiq, the architecture is centered on the sensitive value and the runtime authorization decision.
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, redacted, 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, redact, 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, tokenized, redacted, or masked 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, vault, 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.
Vault Access vs Identity-Governed Sensitive Data Access
Skyflow governs access to vaulted data through vault policies, APIs, tokens, and detokenization workflows.
Ubiq governs whether a specific identity or workflow should receive a sensitive value at runtime, and what form that value should take.
With Ubiq, the question is not only:
Can this application call the vault?
The question becomes:
What should this user, application, service account, API, pipeline, BI tool, AI workflow, or downstream system receive right now?
That distinction matters when many users, applications, service accounts, and workflows touch the same data but require different levels of sensitive data visibility.
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, vault logs, or tokenization events 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.
Privacy API Layer vs Broad Data Workflow Enforcement
Skyflow is centered on workflows where sensitive data is intentionally routed through a privacy API layer.
Ubiq is designed for broad data workflow enforcement.
This matters when sensitive data is accessed by databases, warehouses, BI tools, data pipelines, event streams, APIs, RAG systems, AI agents, MCP tools, notebooks, vector stores, downstream replicas, and vendor feeds.
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 through a centralized vault API.
How Ubiq Differentiates from Skyflow
Identity-governed runtime outcomes
Ubiq controls what sensitive data each identity can see and use at runtime. Instead of only asking whether a value should be vaulted, tokenized, redacted, or detokenized, 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.
Protection where sensitive data already lives
Ubiq can protect sensitive values across existing applications, databases, warehouses, APIs, BI tools, data pipelines, AI workflows, and downstream systems without requiring every sensitive value to be centralized into a dedicated privacy vault first.
This matters for enterprises with sensitive data already spread across production systems, analytics platforms, historical records, service accounts, vendor feeds, and downstream applications.
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, vault, 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 vault, tokenize, redact, or detokenize 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 centralize sensitive data in a privacy vault, 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 application and data flow redesign is required to adopt a vault-centered model?
- Do we need to route sensitive data through a privacy API layer, or do we need software-based integration into existing applications and data workflows?
- Which use cases require vault storage, tokenization, detokenization, redaction, or API-brokered access?
- Which use cases require field and record-level runtime protection where data already lives?
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, redaction, 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, vaults, 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
Skyflow provides a data privacy vault architecture for isolating, tokenizing, redacting, encrypting, and governing sensitive customer data through vault-centered workflows.
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.
Skyflow is centered on a privacy vault architecture for sensitive data isolation and API-brokered access.
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.