User access is not task access. Old ETL tools were built to move data into fixed tables and schemas. Old access control was built for files, folders, apps, and databases.
AI works differently. It reads raw content, pulls from mixed data, and answers in real time. It needs a layer that structures data for AI and enforces the right access at runtime.
A single document can mix medical notes, billing details, legal text, fraud signals, and internal comments, often in the same paragraph.
Old systems classify the whole document and block it. Block too much and AI loses context. Allow too much and sensitive data leaks into prompts, retrieval, and agent actions.
AI agents call tools, chain actions, and pull data across systems. Once data enters a prompt or payload, your existing access controls lose visibility. The data is already exposed, and nothing is watching it. CBAC enforces at the moment the agent asks: who’s asking, for what task, and what they’re allowed to see. Static roles can’t anticipate this. User access is not task access.
Example: the same account note is accessed by three agents and one tool call, with no fixed schema or fields.
Raw note before any policy runs. Every entity is visible.
→ This is what enters the model before CBAC evaluates the request.
| Feature | Metadata / source path controls | Protecto CBAC |
|---|---|---|
| Works on | File location, source, metadata, partial internal content | Content inside documents |
| Granularity | Document level | Section or sentence level |
| Understands meaning | Limited, mostly classification | Yes |
| Runtime enforcement | Limited | Yes |
| AI usefulness | Often hides too much | Keeps more usable context |
“Protecto enabled us to deliver data security, privacy, and compliance for our AI features at scale across 5,000+ enterprise clients, strengthening customer trust in our AI automation solutions.”
How to give each user, agent, and tool its own view of the same data, with a checklist for production.
How Protecto Vault detects, masks, and controls access to sensitive data across agents, AI applications, RAG pipelines, MCPs, data lakes, and enterprise systems.
How a health analytics company built a data product on sensitive medical text while keeping patient data private.
Why a static role cannot decide what an AI agent may see, and how runtime access control uses the user, agent, task, and destination.
No. You define policies in Protecto once and edit them as you need. You bind each policy to users and applications in your own AD or access-control system, and Protecto enforces it at execution time through an authorization key on every API call.
Yes, through Privacy Gateway. You keep each user’s masking and unmasking access in AD or Okta groups, and Gateway issues a Protecto auth key with that access at login. If you use Protecto Vault directly, it’s a headless API, so your team can build the integration around it.
Entity-based data such as names, IDs, card numbers, PII, and PHI, with 50+ built-in entity types plus custom entities. CBAC also controls context-based data that has no fixed pattern, such as pricing terms, fraud signals, HR notes, and legal strategy.
As SaaS, on-prem, in a private cloud, or air-gapped, so your documents and policies can stay inside your environment.
Yes. Protecto logs who asked, for what task, and which values each reader received, so security and compliance teams can review every decision. Book a demo to see the log on your own documents.
A practical guide to privacy controls for AI assistants, RAG, and agents. One workflow traced from request to CRM update, with what each destination may see.
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Features that safeguard your data across agents, AI applications, RAG pipelines, MCPs, data lakes, and enterprise systems.
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