Product Comparison

Protecto vs Limina AI

AI workflows need more than masking.

Limina AI, formerly Private AI, identifies and masks sensitive data. Protecto identifies and masks too, then carries that protection through the rest of the workflow: consistent tokens across sessions, policy-based unmasking per user and agent, and native connections into RAG, tool calls, and streaming responses.

If you are building AI agents, masking covers the first step only.

Summary

Both find the sensitive data. Only one is built for what happens next.

Limina delivers a strong privacy-processing component. Protecto delivers the data-control layer that an AI workflow runs on.

Advanced identification and masking: agentic data classification built for typos, broken words, mixed languages, unstructured conversation, and business-sensitive content beyond traditional PII
Context-preserving protection: sensitive values are protected while the meaning and relationships AI needs to reason effectively are retained
Consistent tokenization: the same customer stays the same token across sessions, retrieved documents, tool calls, and asynchronous flows
Complete AI runtime controls: policy-based masking and unmasking, tenant management, session continuity, and selective data reveal by user, agent, tool, and task
Ready for AI integration: APIs and gateways with reference architectures and sample code for RAG, agents, tool calls, streaming, and long sessions
Proven at enterprise volume: 13M+ long-form texts processed per day and 50M+ records handled in production
SOC 2 and ISO 27001 certified; HIPAA BAA, GDPR, CCPA, PCI DSS, DPDP

How they compare

Protecto or Limina for Agentic AI data security?

Limina identifies and masks PII, PHI, and PCI with a traditional NER-based approach. Protecto does that too.

The difference shows up after masking. In an agentic system the same record is retrieved into a prompt, chunked into a vector store, passed to a tool, held in memory across a long session, and returned in a streamed response. Each of those handoffs needs the token to stay consistent, the meaning to survive, and the reveal decision to be made per user and per agent. Limina leaves that orchestration to your engineering team. Protecto ships it.

Limina gives you one privacy component. Protecto gives you the complete AI data layer.

Add to your stack

Add Protecto where your AI workflow needs runtime control

Limina covers identification and masking. Protecto covers identification and masking, plus every workflow layer above it.

Context-preserving protection

Sensitive values are protected while the meaning and relationships the model needs to reason effectively are retained. Markers and redaction strip that signal out. Protecto keeps masked data usable, not just safe.

Consistent tokens across the whole workflow

The same entity maps to the same token across sessions, retrieved documents, tool calls, and asynchronous flows, so the AI still understands that two mentions refer to one customer.

Policy-based selective reveal

Unmasking is decided by user, tenant, agent, tool, and task. An authorized CRM tool receives the real email address in the same run where the LLM never sees it.

AI integration without custom orchestration

Connect through APIs and gateways, with reference architectures and sample code for RAG, agents, tool calls, streaming, and long sessions. No orchestration layer for your team to design, integrate, test, and maintain.

Why AI teams choose Protecto

Built for the data path AI teams actually ship

Agentic classification for messy input

Real AI inputs are not clean records. Protecto classifies typos, broken words, mixed languages, unstructured conversation, and business-sensitive content that falls outside traditional PII lists. Identification is the first step, not the finished system.

Same customer across the workflow

A customer appears in a prompt, a retrieved document, a tool response, and a later conversation turn. Protecto keeps the identity consistent so the AI understands the relationship.

A tool needs the original value

An LLM does not need a customer's email address, but an authorized CRM tool does. Protecto reveals only the required value to the authorized tool.

Tool metadata stays intact

Masking a function name, tool ID, or parameter schema can break an agent mid-run. Protecto protects sensitive runtime values without changing the tool-call structure.

Proven accuracy and deployment speed

Enterprise evidence for regulated AI workflows.

Superior F1 score vs AWS Comprehend and Microsoft Presidio in an independent benchmark by UT Dallas / DataXpert.
99% recall and 96% precision including Arabic-language content at a Middle East bank.
85% semantic accuracy retained after masking in an enterprise bank case study, so downstream reasoning holds up.
POC completed in under one week for a health analytics customer.
13M+ long-form texts processed per day and 50M+ records handled, with 5,000+ downstream enterprise customers protected through a single Automation Anywhere deployment.

Comparison

Protecto vs Limina capability matrix

For production AI systems the question is not only whether sensitive data is found. It is what the data can still do afterward, who is allowed to see it, and how much of the surrounding system you have to build yourself.

Capability	Protecto	Limina
Identification and masking
Detects PII, PHI, and PCI	✓	✓
Classification approach	Agentic classification for complex AI inputs	Traditional NER-based approach
Handles typos, broken words, and mixed languages	✓	✕
Masking method	Context-preserving for AI reasoning	Markers, redaction, and synthetic replacements
Meaning and relationships retained after masking	✓	✕
AI runtime controls
Token consistency scope	Sessions, RAG, tools, and asynchronous flows	Within supported request or connection scope
Session continuity across long conversations	✓	✕
Selective unmasking	Policy-based by user, tenant, agent, tool, and task	Re-identification API
Tenant management	✓	✕
Tool-call structure preserved during masking	✓	✕
AI integration
API integration	✓	✓
Gateways for AI traffic	✓	✕
Reference architectures and sample code for RAG, agents, tool calls, streaming	✓	✕
Customer engineering required to design, integrate, test, and maintain orchestration	✕	✓
Outcome
What you deploy	AI-ready data-control layer	Privacy-processing component

Disclaimer: Claims about Limina AI are based on public product documentation current as of August 2026.

Coming from Limina AI?

Identify better. Mask intelligently. Make protected data usable across AI.

Protecto adds to your existing stack through APIs and gateways. No platform replacement, no data migration, and no custom orchestration layer for your team to build and maintain.

See Protecto in Action

Protect data across your entire AI stack, from database to prompt to response, without migration or platform replacement.

Building AI agents with sensitive data?

Limina stops at identify and mask. Protecto governs the entire path, from the input your users send to the values your tools receive. No platform replacement, no data migration, and no orchestration layer for your team to build and maintain.