Sensitive data can leak through the documents your AI reads, the tools it calls and the answers it writes. Protecto protects it at each step, and your AI keeps working.
SOC 2 Type II · ISO 27001 · HIPAA, BAA available
Bank Muscat let employees use Gemini on SharePoint content with sensitive data protected
Recall on sensitive data, incl. Arabic
Precision on the same data
To a working proof of concept
A support rep asks the company’s AI assistant to summarize a customer complaint. The rep types nothing sensitive. The assistant searches the knowledge base, pulls an account number and a note about a missed payment from the CRM, and sends it all to the model. The answer repeats the account number, and the agent saves it to memory.
Your DLP tool raises no alert. No file moved and no email went out. The data traveled as plain sentences inside API calls.
After the user types, agents add retrieved documents, tool results and memory. A check on user input sees none of that.
DLP watches email, files and endpoints. AI traffic is free text inside API calls, and a discount term or legal note has no pattern to match.
Redaction strips the context a model needs, and a placeholder that reaches a tool can make the lookup fail. Then the AI feature stalls.
Each path needs its own control point. Your application or gateway calls Protecto at each one, and you set what every recipient receives.
Users paste customer details, and apps add records to the request. Protecto masks the data before the model call.
Search returns a paragraph this user was never cleared to read. GPTGuard MCP returns only the chunks your policy permits.
An agent sends a customer ID to an API, and the result comes back full of personal data. Protecto protects both directions.
A connected server receives more data than the task needs. Protecto masks data before the call and again on the output.
One agent hands its full context to the next. Protecto applies your handoff policy before the data leaves.
A model repeats a detail in its answer, or an agent stores it for later sessions. Protecto checks the answer before it goes back, and memory holds tokens.
Check your own AI against these six places: 24 yes-or-no questions.
DLP still guards email, endpoints and file transfers. Protecto covers where it falls short: data moving to models and agents.
| What matters | Traditional DLP | Protecto |
|---|---|---|
| Where it works | Email, files, endpoints, web uploads | User input, retrieved context, tool calls, agent memory, answers |
| How it finds data | Known patterns on known channels | Reads values in context across 50+ entity types, plus your custom entities |
| What happens next | Usually blocks or allows the whole request | Masks the value with a consistent token, so the AI keeps working |
| When someone needs the real value | Not built to return usable answers to authorized users | Reveals it by role or policy, and logs it |
| Business context with no pattern | Rules have nothing to match | Context-Based Access Control (CBAC) applies your policy to discount terms, legal notes and HR context |
Typical behavior of pattern-based DLP. Products differ, so test on your own AI traffic.
See this on a workflow of your own.
Your application or gateway calls Protecto as data moves. Sensitive values are masked before they travel on, and you set what each recipient receives.
On-premises · Private cloud · Air-gapped · SaaS. When Protecto runs in your environment, token mappings, keys and logs stay on your servers.
Finding sensitive data is the first step. Keeping the AI useful and showing what happened gets the project approved.
DeepSight tells a name from a place with the same spelling, across 50+ entity types and seven tested languages.
Consistent, format-preserving tokens let the model follow who is who. Bank Muscat retained 85% semantic accuracy.
Approved tools and people get real values. Everyone else keeps seeing tokens.
Audit logs cover masking and unmasking, and reports show where sensitive data flowed.
“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.”
It keeps sensitive data from reaching people, models or systems that should not have it as data moves through an AI application. It covers user input, retrieved documents, tool calls, agent memory and model answers.
In what users type, documents the AI retrieves, data sent to and returned from tools and MCP servers, messages between agents, stored memory, logs and the final answer. PII masking for AI shows how one value is protected along that path.
No. DLP keeps guarding email, endpoints and file transfers. Protecto covers where it falls short: AI traffic, context and unstructured data moving to models and agents.
It can, when placeholders carry no meaning. Protecto uses consistent, format-preserving tokens, so the model can still follow the text. Bank Muscat retained 85% semantic accuracy after masking, on mixed Arabic and English data.
Yes. You decide which people and tools see real values, and Protecto reveals them by policy and logs it. Everyone else keeps seeing tokens. Protecto Gateway does this for authorized tool calls automatically. Without the Gateway, your application handles that step using Protecto’s APIs.
Yes. Context-Based Access Control (CBAC) applies your policy to passages such as discount terms, legal strategy and HR context, where pattern rules have nothing to match.
Not on its own. Protecto is API-first and is called by your applications, gateways and agents. For chat, Protecto Privacy Gateway protects data in chat interfaces you host yourself.
Protecto is API-first. You call it from LangChain, LlamaIndex or your own code, before a model, retrieval or tool call and again before the answer goes back, with any model provider. Bank Muscat reached a working proof of concept in four weeks.
On-premises or in your private cloud, air-gapped if you need it. A SaaS option is also available. When Protecto runs in your environment, token mappings, keys and logs stay on your servers, and external models only receive tokens.
Teams use Protecto to support GDPR, HIPAA and CCPA programs. It masks sensitive data before it moves and produces reports on how your data policies were enforced. See AI data privacy and compliance.
Bring an AI workflow, with test data if you prefer. We will trace where sensitive data moves and show what each recipient receives.
This datasheet outlines features that safeguard your data and enable accurate, secure Gen AI applications.
By submitting you agree to Protecto’s privacy policy.
24 yes or no questions across the six places sensitive data leaks in agentic AI, from model requests to tool calls and agent memory.
By submitting you agree to Protecto’s privacy policy.
