Protecto vs Protegrity
Protegrity protects data at the database layer. Protecto controls what each user and agent can see inside AI workflows.
Protegrity secures data across databases, data warehouses, and cloud platforms, and has added AI guardrails. Protecto protects sensitive data inside AI workflows: prompts, RAG, agents, MCP calls, tools, responses, and controlled unmasking.
If you are building AI agents, protecting the database is only the first step.
Summary
Protecto protects the data and governs every AI interaction on top of it.
Protecto protects sensitive data before it enters your AI workflow and enforces policy at every layer inside it.
- Data protection before AI: Protecto Vault detects and masks PII, PHI, and PCI in structured and unstructured sources before data enters your AI workflow.
- Per-interaction runtime enforcement: every call carries the user, agent, task, and destination. Protecto masks, rewrites, filters, blocks, or selectively reveals content to match. Protegrity's public policy model uses role, region, and data type.
- Context-Based Access Control (CBAC): finds and controls sensitive business context that entity masking misses, including discount terms, legal strategy, and internal risk analysis.
- Context-preserving masking: the same entity maps to the same token across prompts, documents, and workflows, so AI accuracy holds.
- GPTGuard MCP: applies Vault and CBAC at document ingestion and returns only the chunks allowed for the current user, agent, and task.
- Proven at scale, API-first: 13M+ long-form texts a day for one SaaS company. 50M+ records handled for a healthcare insurer. Runs as SaaS, on-prem, private cloud, or air-gapped.
- SOC 2 Type II and ISO 27001 certified, with HIPAA BAA, GDPR, CCPA, PCI DSS, and DPDP coverage.
How they compare
Protecto or Protegrity for AI data security?
Protegrity is strong at protecting fields in Snowflake, Databricks, databases, and mainframes. It also has AI editions and Semantic Guardrails for prompts and responses. Protecto also protects data in your sources.
The gap is what happens next. In agentic AI, sensitive data does not stay in a field. It moves as text: retrieved into prompts, chunked into vector stores, passed between agents, and returned in responses. The question changes from “is this field protected?” to “should this user, through this agent, for this task, see this passage, and in what form?”
Protegrity’s public materials describe policies by role, region, and data type, and guardrails that flag or block deviations from learned patterns. Protecto takes a runtime enforcement context on every call and shapes the content to match. Two users can call the same agent and get two different, correct answers.
Protegrity protects the data estate. Protecto decides what each AI interaction is allowed to reveal. Many enterprises run both.
Add to your stack
Add Protecto where your AI workflow needs runtime control
Protegrity covers the data layer. Protecto adds runtime control on top of it, through an API.
Four enforcement points
Agent builders call Protecto before an LLM call, at document retrieval, around other MCP servers and tools, and before a response returns. Each call carries the user, agent, and task context.
Fits the platforms you already run
Published reference architectures cover secure RAG on Snowflake Cortex and on Databricks Mosaic. Automation Anywhere embeds Protecto Vault to protect its 5,000+ enterprise customers.
Deploys where your data lives
SaaS, on-prem, private cloud, or air-gapped, with data residency support. Multi-tenant by design, with API-driven tenant and user creation, high availability, and horizontal scaling.
API-first, no migration
No data migration. No platform replacement. A Fortune 100 technology company deployed within days, and a health analytics company finished a POC in under a week.
Why AI teams choose Protecto
Built for the data path AI teams actually ship
Entity masking built for AI accuracy
Protecto Vault handles PII, PHI, and PCI detection with context-preserving tokenization. Standard redaction breaks AI reasoning. Vault maintains accuracy by mapping the same entity to the same token across documents, prompts, and multi-turn workflows. Controlled unmasking returns original values when policy permits.
Context-Based Access Control (CBAC)
Sensitive business context does not fit into named entity lists. CBAC detects, removes, or rewrites sensitive content based on the current user, agent role, and task context — covering discount terms, legal strategy, and confidential narratives.
Protection across every AI layer
Protecto enforces policy at every point in the AI data flow: prompt input, RAG retrieval, MCP and tool boundaries, agent memory, and model responses. Protecto governs it, the layer Protegrity was not built to reach.
Secure document retrieval at scale
GPTGuard MCP is Protecto's secure retrieval layer for agent builders. It ingests documents, applies Vault and CBAC during ingestion, and returns only policy-compliant chunks. 13M+ long-form texts processed per day. 50M+ records handled.
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.
- POC completed in under one week for a health analytics customer.
- Deployed within days at a Fortune 100 technology company running multi-agent architectures.
- 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.
- Outperformed Protegrity in a head-to-head POC for an enterprise bank.
- 5,000+ companies protected through one embedded integration with Automation Anywhere.
Comparison
Protecto vs Protegrity capability matrix
For AI agents and production LLM systems, the core question is not only where sensitive data is stored. It is where sensitive data is processed, retrieved, passed, remembered, and returned.
| Capability | Protecto | Protegrity |
|---|---|---|
| Architecture | ||
| Protects sensitive data in databases and data warehouses | ✓ | ✓ |
| Native in-platform protectors (cloud data platforms, databases, mainframes) | ✓ | ✓ |
| Protects data in AI workflows at runtime | ✓ | Partial: Semantic Guardrails (flag or block) |
| Structured and unstructured data support | ✓ | ✓ |
| API-first integration | ✓ | ✓ Container and API |
| AI layer protection | ||
| Protects data in LLM prompts and responses | ✓ | ✓ Semantic Guardrails |
| Retrieval-time enforcement for RAG | ✓ GPTGuard MCP | ✕ |
| Protects data at MCP and agent boundaries | ✓ | ✕ |
| Per-user, per-agent, per-task runtime enforcement | ✓ | ✕ |
| Data protection | ||
| Context-preserving masking for AI accuracy | ✓ | ✕ |
| PII, PHI, PCI entity detection | ✓ | ✓ |
| Controlled unmasking and original value recovery | ✓ Per interaction | ✓ By policy |
| Context security | ||
| Context-Based Access Control (CBAC) | ✓ | ✕ |
| Protects sensitive business context in paragraphs | ✓ Remove, rewrite, split | Partial: flags or blocks confidential details |
| Compliance | ||
| SOC 2 Type II | ✓ | ✓ |
| ISO 27001 | ✓ | ✓ |
| HIPAA BAA | ✓ | ✓ |
| GDPR, CCPA, PCI DSS, DPDP support | ✓ | ✓ |
| Deployment | ||
| SaaS and cloud | ✓ | ✓ |
| On-premises | ✓ | ✓ |
| Private cloud | ✓ | ✓ Hybrid |
| Air-gapped | ✓ | ✕ |
Coming from Protegrity?
Add Protecto to your AI stack. No data migration. No platform replacement.
Protecto plugs into your existing AI workflow through an API. Recent deployments went live in days.
In this page
Building AI agents with sensitive data?
Protegrity protects the database layer. Protecto controls the AI interaction, from where data sits to where AI uses it. No migration. No platform replacement. API-first and production-ready in days.
- SOC2 Certified
- HIPAA Compliant
- GDPR Ready
- ISO 27001