How two privacy solutions compare across detection, tokenization, agentic AI, and enterprise governance
OpenAI Privacy Filter and Protecto Vault both address privacy protection for AI applications — but they operate at fundamentally different layers of the architecture.
OpenAI Privacy Filter is a specialized transformer-based privacy detection model designed to identify sensitive entities in text and enable redaction workflows.
Protecto Vault is an enterprise AI privacy platform combining privacy detection, tokenization, governance, policy enforcement, and secure data handling for AI applications and AI agents.
Organizations looking for a privacy detection component may find OpenAI Privacy Filter sufficient. Organizations building production AI systems — especially agentic applications — often require entity-aware policy enforcement, tokenization, access controls, auditing, and compliance workflows, all provided by Protecto Vault.
Raw Data → Privacy Filter → Redacted Data → LLM / AI App
Raw Data → Privacy Detection → Tokenization → Secure Vault → LLM / AI Agent → Policy Enforcement → Authorized Detokenization → End User
| Capability | OpenAI Privacy Filter | Protecto Vault |
|---|---|---|
| Primary Purpose | Privacy entity detection & redaction | End-to-end AI privacy platform |
| Deployment Model | Open-weight model | Enterprise platform |
| Open Source | ✓ Yes | ✕ No |
| PII Detection | ✓ Yes | ✓ Yes |
| Context-Aware Detection | ✓ Yes | ✓ Yes |
| Structured Data Support | ! Limited | ✓ Yes |
| Unstructured Data Support | ✓ Yes | ✓ Yes |
| Agentic Privacy Detection | ✕ No | ✓ Yes |
| Fine-Grained Entity Classification | ! Limited | ✓ Extensive |
| Tokenization | ✕ No | ✓ Yes |
| Reversible Detokenization | ✕ No | ✓ Yes |
| Secure Vault Storage | ✕ No | ✓ Yes |
| Policy-Based Access Control | ✕ No | ✓ Yes |
| Audit Logging | ✕ No | ✓ Yes |
| Compliance Workflows | ✕ No | ✓ Yes |
| AI Agent Protection | ✕ No | ✓ Yes |
| Custom Entity Support | ! Through model tuning | ✓ Through platform customization & tuning |
| Enterprise Governance | ✕ No | ✓ Yes |
Detection & Redaction
The Privacy Filter model focuses on detecting sensitive entities within text. Given a sample input like "John Smith lives at 123 Main Street. Email: john@example.com" — it identifies:
The primary objective is accurate identification and redaction of sensitive information.
Agentic Privacy Framework
Protecto Vault uses an agentic privacy detection framework designed for enterprise AI. Beyond detection, it classifies entities into business-relevant categories that drive privacy policies and agent behaviors.
Entity types from Protecto's fine-grained taxonomy:
This granularity enables different privacy policies per entity type. For example:
Such distinctions are critical in agentic AI environments where privacy decisions influence tool access and autonomous actions.
Text Detection Only
The model is designed to identify privacy entities in text. It does not manage tool access, agent permissions, retrieval controls, workflow governance, or data lifecycle management. These capabilities must be implemented separately by the development team.
Full Agent Lifecycle
Protecto Vault is designed for AI agents and autonomous workflows. Privacy controls can be applied at every stage:
Privacy-Focused Entities
Provides privacy-focused entity recognition, optimized for privacy detection use cases.
Enterprise Taxonomy
Provides a broader enterprise privacy taxonomy enabling highly specific governance policies per entity class.
Redaction
Supports redaction workflows. The original value is not retained by the model — once redacted, it cannot be recovered.
John Smith becomes [REDACTED]
Downstream systems cannot access the original value through the model.
Reversible Tokenization
Supports reversible tokenization. The original value is securely stored and can be recovered only through authorized access — enabling AI systems to operate on protected data while preserving business functionality.
John Smith becomes <PER>00Du wcTao</PER>
Authorized agents can detokenize values on demand — unauthorized agents cannot.
Model Fine-Tuning
Organizations can fine-tune the model, add new entity types, and extend privacy detection capabilities. This requires model training and deployment expertise from engineering teams.
Platform Customization
Organizations can define custom entity types, configure privacy policies, create organization-specific classifications, and apply governance rules. Custom entities become part of the broader privacy and governance framework — no model training required.
Detection Only
Provides privacy detection capabilities but does not include governance controls. Organizations must separately implement:
Integrated Governance
Provides integrated governance features out of the box — no separate implementation required:
OpenAI Privacy Filter is best viewed as a privacy detection model — it identifies sensitive entities for redaction and masking workflows. It is a capable, open-weight tool for teams who need detection and are comfortable building the surrounding infrastructure themselves.
Protecto Vault is a comprehensive AI privacy platform combining agentic privacy detection, fine-grained entity classification, tokenization, governance, access control, and compliance capabilities in a single system.
While both solutions support custom entity types, Protecto Vault uses those entities throughout the AI lifecycle to drive privacy policies, agent behavior, and enterprise governance. The two offerings are complementary rather than direct substitutes: OpenAI Privacy Filter addresses detection; Protecto Vault addresses enterprise AI privacy management end-to-end.