DLP blocks sensitive data, breaking AI tasks and tool calls. Protecto is built for agentic workflows, preserving context across documents, conversations and tool calls while protecting sensitive values.
SOC 2 Type II · ISO 27001 · HIPAA, BAA available
Inside your company
Your AI workflow · Protecto
The model sees <PERSON>k3d9f</PERSON> never Dana Ruiz
Answer comes back with real values for allowed users
Endpoints and USB
Web uploads
SaaS sharing
Traditional DLP watches the exits
Block
Warn
Redact
The model gets tokens. Authorized users and tools get real values.
Partly. Traditional DLP blocks sensitive data from leaving through email, endpoints, browsers and SaaS apps, including prompts sent to AI tools. But AI agents need enterprise data to complete tasks, and that data often includes sensitive information. Blocking it can break the workflow.
Protecto is built to protect sensitive data while keeping it usable for AI. It preserves context across documents, conversations and tool calls without exposing sensitive values to the model. Authorized users and tools receive real values only where policy allows.
Keep DLP for
Blocking sensitive data from leaving through email, endpoints, browsers and SaaS apps.
Add Protecto for
Protecting sensitive data in AI use cases while keeping workflows running.
Automation Anywhere protects sensitive data in its AI features across 5,000+ live enterprise tenants, with a policy per tenant.
Live enterprise tenants at Automation Anywhere
To deploy at a Fortune 100 company
Long texts a day, at a SaaS company
A support agent asks the company’s AI assistant to draft a reply. The prompt names two customers and a card number.
DLP can block it, warn the agent or redact the values. Each choice trades safety against a useful answer. Protecto gives each value its own token, so the draft comes back right and the data never reaches the model.
Support agentDraft a reply to Dana Ruiz about the chargeback on card 4111 1111 1111 1111. Her brother Sam Ruiz also wrote in.
Real data reaches the model?
Useful draft?
Real names in the reply?
AI needs enterprise data to complete tasks. Blocking that data or stripping away its meaning breaks workflows. Protection must preserve the context AI needs.
Replace every customer name with “PERSON,” and AI loses track of who did what. Protecto keeps customers distinct and their information connected across documents and conversations, without exposing real identities.
An agent needs customer information to resolve a ticket and share specific details with a billing tool. Block that information, and the workflow stops before the task is complete.
An agent reads a ticket, retrieves customer records and calls a tool. Sensitive data needs protection at every step, with real values available only to authorized users and tools.
AI workflows mix languages, documents, conversations and structured tool inputs. Sensitive information does not always match a fixed pattern. Detecting it requires understanding what the information means in context.
DLP stops sensitive data from leaving the company. Protecto protects sensitive data in AI workflows and keeps it usable, so tasks and tool calls still finish.
You don’t replace anything. Protecto works with any LLM and sits in the path your AI app or gateway already uses.
What it does: swaps sensitive values for consistent tokens, then returns real values to the users, agents and tools your policy allows.
What it does: spots sensitive data leaving the company and blocks it, warns the user, redacts it or raises an alert.
DLP stops unauthorized data sharing. Protecto protects sensitive data while keeping AI workflows running.
| Capability | Protecto | Traditional DLP |
|---|---|---|
| Primary purpose | Protect sensitive data while enabling AI workflows | Stop unauthorized data sharing |
| Where it fits | AI workflows, document processing, agents, RAG, gateways, MCP and tool calls | Email, endpoints, browsers and SaaS apps |
| Sensitive data handling | Replaces sensitive values with consistent tokens so work continues | Blocks, warns or redacts |
| Preserving context | Keeps identities distinct and information connected across documents and conversations | Generic redaction removes distinctions between people and values |
| Completing tasks | Provides authorized tools with the real values needed to finish the task | Blocking required data stops tasks; redaction removes details tools need |
| Beyond PII | Identifies business-sensitive information in context, such as discount details and confidential leadership communications | Uses keywords, labels, fingerprints and classifiers to identify confidential content |
| RAG and retrieval | Masks sensitive data before indexing and applies policy at retrieval | Coverage depends on integrations with the retrieval system |
| Agents, MCP and tools | Protects sensitive values across tool calls and restores them where authorized | Coverage depends on monitored channels and integrations |
| Who sees real values | Controls which users and tools receive real values | Controls data sharing through monitored channels |
| Model responses | Restores permitted values while keeping other values protected | Supported products inspect responses for sensitive data |
| Data retention | Lets customers set how long Protecto retains sensitive data and when it is deleted | Workflow data retention depends on the surrounding applications and services |
| Audit trail | Records requests and reveals of sensitive values | Records policy violations, alerts and enforcement actions |
| Deployment | SaaS, private cloud, on-premises and air-gapped environments | Endpoint agents, network controls, cloud services and SaaS integrations |
Based on public documentation for widely used DLP products and their AI add-ons, reviewed November 2026. Products differ, so check the one you use.
DLP and Protecto solve different problems. Pick what you need and see which one handles it.
What do you need to do? Pick one.
DLP tells you a prompt holds sensitive data, then stops or strips it. Keeping that data usable for AI is left to your team.
Code that gives each value its own token so the model can tell people apart.
A store for mappings, with backup and key management, so real values can come back.
The same customer needs the same token across sessions, documents and tools.
Who sees real values, by user, agent and task, and how your apps pass that context.
Hooks at every step of an agent run, including tool inputs and tool results.
A record of who saw which real value, and why.
“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.”
AI DLP, or AI data loss prevention, means controls that keep sensitive data from leaking through AI tools, copilots and agents. Most DLP products add it by checking prompts sent to AI sites and copilots, then blocking, warning or redacting. Protecto is an AI DLP built for agentic workflows: it protects sensitive data and keeps it usable, so AI tasks and tool calls still finish.
No. DLP still does its job on email, endpoints, USB, web uploads and SaaS sharing. It falls short in AI workflows, where agents need the data to finish tasks and blocking it breaks the workflow. Add Protecto there and keep your DLP.
No. Protecto works alongside your DLP. DLP stops sensitive data leaving the company. Protecto lets AI apps, agents and tools work with that data through consistent tokens and returns real values only to allowed users.
Purview can stop Microsoft 365 Copilot from processing prompts that hold sensitive data, and its Edge inline protection can block typed prompts to sites such as ChatGPT and Gemini. Both stop the prompt; neither swaps values for tokens and brings them back. For your own apps, Purview’s APIs return allow or block, and your app has to act on it.
Those products watch email, endpoints, web traffic and cloud apps, and several now check prompts sent to AI tools. Netskope and Zscaler can redact prompts in their AI gateways, and Palo Alto Networks Prisma AIRS can mask values with X characters, all one way. Protecto keeps each value as a consistent token in AI workflows and restores real values by policy.
It blocks the prompt, warns the user or redacts the value, depending on policy. A warning can be clicked through, so the data still leaves. Redaction keeps the data out but leaves the model with text it can’t reason about.
Mostly no. DLP sees traffic at the endpoint, browser or network. Retrieval, agent steps and tool calls inside your app often never cross those points, and DLP doesn’t follow data from one agent step to the next. Protecto runs in that path and masks data at each step.
Masked values. Protecto doesn’t stop the request. The model works with tokens, and the answer shows real values only to the users, agents and tools your policy allows. Everyone else sees the tokens.
As SaaS, in your private cloud, on-premises or air-gapped. It works with any LLM and sits in the path your AI app or gateway already uses, so your DLP keeps running as it is.
Bring a prompt, document or agent run, and see how Protecto handles it next to your DLP.
