Fortanix vs Ubiq
Compare Fortanix with Ubiq runtime sensitive data protection. Learn how Fortanix supports data security with key management, HSM-backed operations, secrets management, tokenization, encryption, confidential computing, and cryptographic services, 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
Fortanix provides data security and cryptographic infrastructure capabilities across Data Security Manager, key management, HSM-backed cryptographic operations, secrets management, encryption, tokenization, confidential computing, and centralized policy across cloud, hybrid, and on-premises environments.
These capabilities are useful for organizations that need centralized cryptographic services, HSM modernization, secrets management, tokenization, key lifecycle management, confidential computing, or secure enclave-based application protection.
Ubiq addresses the same overall sensitive data protection problem with a different architecture and operating model.
Ubiq is not only an encryption, tokenization, masking, or FPE product. Ubiq provides identity-governed runtime data protection for sensitive data. Ubiq sits where sensitive data access actually happens and determines what sensitive data each identity can see and use based on identity, context, and policy.
The key distinction is not whether both platforms protect sensitive data. They do.
The key distinction is the control model.
Fortanix is a cryptographic infrastructure and data security platform focused on DSM, KMS, HSM-backed operations, secrets management, encryption, tokenization, confidential computing, and cryptographic policy. Depending on the deployment model, Fortanix implementations may involve DSM configuration, key objects, secrets, cryptographic services, tokenization services, HSM integrations, confidential computing environments, enclave management, and cryptographic operations planning.
Ubiq is an identity-governed runtime data protection platform. It protects sensitive values directly and governs how those values are revealed to users, applications, service accounts, APIs, data pipelines, BI tools, AI agents, MCP workflows, and downstream systems.
This matters because IAM and IGA systems can determine who gets into an application, database, warehouse, API, or tool. But once access is granted, sensitive data is often still overexposed. Ubiq closes the runtime gap between identity access and sensitive data access.
With Ubiq, the same sensitive data can produce different outcomes depending on who or what is accessing it.
| Accessing identity or workflow | Possible runtime outcome |
|---|---|
| Authorized business user | Full authorized value |
| Support user | Masked or partially redacted value |
| Analytics workflow | De-identified or tokenized value |
| AI/RAG workflow | Controlled derived representation |
| Unauthorized identity | No sensitive data |
| Downstream system | Protected value remains encrypted, tokenized, or masked |
Ubiq also supports modern AI, RAG, semantic search, and vector-driven workflows by separating protection of sensitive source data from AI/vector computation. Sensitive records, identifiers, and regulated fields remain protected and identity-governed, while AI workflows operate on controlled derived representations that preserve semantic search, retrieval, and analysis functionality without broadly exposing plaintext sensitive values.
This is especially important for regulated data environments where teams want to use AI agents, RAG pipelines, semantic search, vector databases, notebooks, and MCP workflows without turning protected source data into uncontrolled plaintext.
Key Takeaways
- Fortanix and Ubiq both help protect sensitive data, but they differ significantly in architecture, deployment model, operating model, and runtime control approach.
- Fortanix is focused on enterprise key management, HSM-backed operations, secrets management, tokenization, encryption services, confidential computing, and centralized cryptographic policy.
- Fortanix deployments may involve DSM configuration, key objects, secrets, cryptographic services, tokenization services, HSM integrations, confidential computing environments, enclave management, and cryptographic operations 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 Fortanix Helps
Fortanix provides enterprise cryptographic infrastructure and data security capabilities across cloud, hybrid, and on-premises environments.
Its capabilities are commonly used for centralized key management, HSM-backed cryptographic operations, secrets management, tokenization, encryption services, and confidential computing. These patterns can be useful when organizations need centralized control over keys, secrets, cryptographic objects, secure enclaves, or HSM-backed trust.
Fortanix helps answer questions such as:
| Question | Fortanix focus |
|---|---|
| Where are cryptographic keys managed? | Centralize key lifecycle management across supported environments |
| Which applications or services can use specific keys? | Control access to cryptographic operations and key objects |
| Where are secrets, passwords, API keys, and certificates managed? | Manage sensitive objects through a centralized secrets and cryptographic services model |
| Which workloads require HSM-backed key protection? | Use HSM-backed cryptographic operations and key protection |
| Which workloads should run in confidential computing environments? | Protect selected applications and data-in-use workflows through secure enclave patterns |
| Which values should be tokenized or encrypted? | Apply tokenization or encryption services where integrated |
| How can cryptographic operations be centralized? | Provide policy, audit, and cryptographic services across cloud, hybrid, and on-premises environments |
Fortanix is generally associated with cryptographic infrastructure patterns where key management, HSM-backed operations, secrets, tokenization, encryption services, and confidential computing 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.
Fortanix focuses primarily on cryptographic infrastructure capabilities such as key management, HSM-backed operations, secrets management, encryption, tokenization, confidential computing, and centralized cryptographic 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, key access, and sensitive data access, so access to a key, secret, 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 | Fortanix | Ubiq |
|---|---|---|
| Primary purpose | Data security and cryptographic infrastructure for DSM, KMS, HSM, secrets, encryption, tokenization, and confidential computing | Identity-governed runtime data protection for sensitive data |
| Core control model | Manage cryptographic infrastructure, keys, secrets, tokenization services, encryption services, and secure enclave workflows | Determine the runtime data outcome based on identity, context, and policy |
| Runtime data outcome | Applies protection through cryptographic services, tokenization, key access, secrets access, and confidential computing patterns | Can return full data, masked data, de-identified data, tokenized data, encrypted data, or no sensitive data depending on identity and policy |
| Product footprint | Multiple platform capabilities across key management, HSM, secrets, tokenization, encryption, cryptographic services, and confidential computing | One focused runtime data protection platform for encryption, tokenization, masking, identity-governed access, and cleartext authorization |
| Installation model | May require planning around DSM objects, keys, secrets, cryptographic services, HSM integrations, tokenization services, enclave workloads, and policy administration 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 cryptographic services, HSM-backed operations, secrets infrastructure, tokenization services, confidential computing environments, or enclave management | Primarily software-based integration patterns designed to reduce infrastructure footprint and operational overhead |
| Operational model | Typically operated by security, platform, infrastructure, cryptography, or cloud teams as part of a broader cryptographic services program | Designed for application, data engineering, analytics, and security teams to deploy runtime protection directly into enterprise workflows |
| Main control point | Fortanix DSM, keys, secrets, cryptographic objects, tokenization services, encryption APIs, HSM-backed controls, and confidential computing workflows | The runtime access point where sensitive data is requested, revealed, masked, tokenized, encrypted, de-identified, or denied |
| Data protection methods | Encryption, tokenization, format-preserving encryption, key management, secrets management, HSM-backed operations, and confidential computing | Encryption, tokenization, masking, and identity-governed runtime data outcomes |
| Key management | Core capability, including HSM-backed key management and multicloud key control | Built-in KMS/HSM options, BYOK/CMK, and BYOHSM support depending on deployment requirements |
| Secrets management | Core capability through DSM | Can integrate with enterprise identity and key management patterns, but runtime sensitive value enforcement is the primary focus |
| Confidential computing | Core capability through confidential computing and enclave management | Can complement confidential computing by protecting sensitive values and enforcing runtime data outcomes across 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 |
| Runtime cleartext authorization | Supported through Fortanix cryptographic policy and integration patterns | Core design focus using identity, role, application, dataset, and context |
| Implementation experience | Enterprise cryptographic platform implementation may require coordination across DSM, keys, secrets, cryptographic objects, tokenization, HSMs, confidential computing, and operations teams | Integration through software libraries, APIs, and data workflow patterns designed to reduce deployment complexity |
| Service accounts and automation | Can control key, secret, tokenization, and cryptographic operation access for applications and workloads | 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 | Can provide tokenization and encryption services for supported integrations | Can enforce identity-governed sensitive data outcomes for BI, dashboards, reporting, analytics, and extracts |
| AI, RAG, and agent workflows | Supports confidential computing and tokenization patterns that can protect AI-related workloads | 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 | Cryptographic services, tokenization, and confidential computing can protect sensitive data, but direct encryption or tokenization 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 cryptographic services, tokenization, secrets, and confidential computing patterns | Helps ensure sensitive data controls follow identity through agents, MCP servers, APIs, applications, databases, warehouses, and downstream tools |
| Access visibility | Visibility depends on DSM logs, cryptographic operation logs, integrations, policy workflows, and audit capabilities | Provides visibility into protected records, unprotected records, active datasets, active identities, top identities, and sensitive data access patterns |
| Access graph | Not typically the primary control model | Maps relationships between identities, access groups, and datasets so teams can understand who or what has access to sensitive data |
| Anomalous access patterns | May require external SIEM, DSPM, DLP, or monitoring workflows depending on architecture | Can surface unusual sensitive data access patterns such as new identities, new access paths, unusual dataset access, or unexpected protected/unprotected activity |
| Downstream persistence | Supports data protection through tokenization and encryption where integrated | Protected values can remain protected when copied, exported, embedded, indexed, replicated, or consumed downstream |
| Architectural orientation | Cryptographic infrastructure platform for key management, HSM, secrets, tokenization, encryption, and confidential computing | Identity-governed runtime data protection across modern application, data, analytics, AI, and downstream workflows |
Key Architectural Differences
Cryptographic Infrastructure vs Identity-Governed Runtime Data Control
Fortanix is a cryptographic infrastructure and data security platform. It is known for key management, HSM-backed cryptographic operations, secrets management, tokenization, encryption services, and confidential computing.
That model is useful when an organization needs centralized cryptographic services, key control, secrets management, HSM modernization, or secure enclave-based workload protection. However, cryptographic infrastructure platforms often focus primarily on keys, secrets, cryptographic operations, tokenization services, or enclave execution.
Those capabilities are important, but they do not fully solve the runtime access problem.
The harder question is:
What should this identity be allowed to see or use right now?
Ubiq is designed around that runtime question. Ubiq evaluates identity, context, and policy at the point of sensitive data access. Based on that decision, the same sensitive data can produce different outcomes for different identities, applications, service accounts, BI tools, AI workflows, and downstream systems.
This is the runtime gap between identity access and sensitive data access.
IAM and IGA systems can determine who gets into an application, database, warehouse, API, or tool. But once access is granted, sensitive data is often still overexposed. Ubiq closes that gap by making the runtime data access point the control layer for sensitive data.
Cryptographic Operation vs Runtime Data Outcome
Fortanix provides cryptographic infrastructure capabilities such as key management, HSM-backed operations, secrets management, tokenization, encryption, and confidential computing. These capabilities help answer questions such as which workload can use a key, which application can retrieve a secret, which cryptographic operation is allowed, or which workload should run inside a confidential computing environment.
Those are important questions.
Ubiq addresses a different runtime question:
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 |
This distinction matters because key access, secret access, tokenization access, or enclave execution does not always equal sensitive data authorization. A workload may be authorized to use a key or call a cryptographic service, but different users, applications, service accounts, BI tools, or AI workflows may still require different levels of sensitive data visibility.
Multiple Cryptographic Services vs One Runtime Protection Platform
Fortanix deployments can involve multiple cryptographic services depending on the desired outcome. A deployment may involve Data Security Manager, key management, HSM-backed cryptographic operations, secrets management, tokenization services, encryption services, certificate and object lifecycle management, confidential computing, secure enclave workloads, policy administration, and audit workflows.
Those capabilities may require architecture planning, deployment coordination, operational ownership, policy administration, and ongoing cryptographic services management.
Ubiq is designed as one runtime sensitive data protection platform. Instead of requiring teams to assemble and operate multiple cryptographic services before enforcing runtime access to sensitive values, Ubiq provides a single protection model for encryption, tokenization, masking, identity-aware policy enforcement, field and record-level cleartext authorization, runtime data outcome control, and application, database, warehouse, API, BI, pipeline, and AI workflow integrations.
This difference matters when the goal is to protect sensitive values quickly and consistently across modern systems without adding unnecessary operational complexity.
Complex Cryptographic Infrastructure vs Software-Based Integration
Fortanix supports many enterprise cryptographic patterns, but those patterns may involve DSM configuration, key objects, app objects, secrets, policies, tokenization services, HSM-backed operations, enclave workloads, or cryptographic service integration.
That is often appropriate for centralized KMS/HSM, secrets, tokenization, cryptographic services, and confidential computing 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, key access, and 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 cryptographic services 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, secret, cryptographic service, enclave, 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, service account, secret, or key 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 |
| Confidential computing workflow | Workload may be protected in an enclave, but downstream data exposure still needs governance | Control how sensitive data is revealed before, during, and after workflow execution |
| 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.
Key Access vs Sensitive Data Authorization
Fortanix can govern which applications, workloads, or users can access keys, secrets, tokenization services, or cryptographic operations.
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:
Is this workload allowed to use this key, secret, or cryptographic operation?
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 multiple users or workflows share the same application, dataset, service account, key, secret, or database access path but require different levels of sensitive data visibility.
Confidential Computing vs Data Workflow Protection
Fortanix has a strong position around confidential computing, including running applications and data in secure enclaves or trusted execution environments.
Confidential computing can reduce exposure of data and code during processing, especially in cloud or multi-party environments.
Ubiq addresses a different problem: controlling sensitive value exposure across data workflows.
Sensitive values may be accessed by applications, APIs, databases, warehouses, BI tools, data pipelines, event streams, RAG systems, AI agents, MCP tools, notebooks, vector stores, downstream replicas, and vendor feeds.
Ubiq is built to enforce sensitive value access across these runtime paths, whether or not the workload itself runs inside a confidential computing environment.
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, key permissions, secret access, cryptographic operation logs, or enclave boundaries 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.
Cryptographic Services Platform vs Workflow-Level Runtime Enforcement
Fortanix is commonly deployed as part of a cryptographic services program with centralized key management, HSM-backed operations, secrets management, tokenization services, and confidential computing.
That approach can make sense for environments with broad KMS/HSM, secrets, tokenization, or enclave requirements.
However, application and data teams may experience that model as heavier if they need to coordinate with platform owners, configure cryptographic objects, integrate tokenization services, manage secrets, configure enclave workloads, or wait for central cryptographic 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 centralized cryptographic 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.
Cryptographic Infrastructure vs Modern Identity, AI, and Analytics Workflows
Fortanix is commonly used in enterprise environments with key management, HSM, secrets, tokenization, encryption, and confidential computing requirements.
Ubiq is designed around the modern reality that sensitive data is accessed by more than traditional applications and databases. Sensitive values may be used by users, applications, APIs, service accounts, data pipelines, warehouses, BI tools, dashboards, event streams, RAG systems, AI agents, MCP tools, notebooks, vector stores, downstream replicas, and vendor feeds.
Identity worked better when data access was direct.
Sensitive data now has more consumers than ever. Controls need to follow identity through the workflow.
Ubiq is built to enforce sensitive value access across these runtime paths, not only inside a key management, secrets, cryptographic operation, or confidential computing control point.
How Ubiq Differentiates from Fortanix
Identity-governed runtime outcomes
Ubiq controls what sensitive data each identity can see and use at runtime. Instead of only asking whether a key can be used, a secret can be retrieved, a value can be tokenized, or a workload can run inside an enclave, Ubiq asks what data 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, cryptographic service, enclave, 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, protect secrets, perform cryptographic operations, tokenize data, or run workloads in confidential computing environments.
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 keys, secrets, and cryptographic operations 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 cryptographic infrastructure are we willing to deploy and operate?
- Do we need centralized key management, HSM-backed operations, secrets management, or confidential computing, or do we need software-based integration into modern applications and data workflows?
- Which use cases require DSM objects, keys, secrets, tokenization services, HSM integrations, enclave workloads, 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, cryptographic services, enclaves, 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
Fortanix provides data security and cryptographic infrastructure capabilities for key management, HSM services, secrets management, tokenization, encryption, confidential computing, and cryptographic operations.
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.
Fortanix is centered on cryptographic infrastructure capabilities such as key management, HSM-backed operations, secrets management, tokenization, encryption services, and confidential computing.
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.