Generic guardrails check for unsafe content and requests. Protecto protects sensitive data across AI workflows, preserving context and revealing real values only to authorized users and tools.
SOC 2 Type II · ISO 27001 · HIPAA, BAA available
AI agents need enterprise data to complete tasks, and that data often includes sensitive information. Guardrails can block or mask it. But blocking stops the task, and generic masking breaks the context AI needs.
Protecto protects sensitive values while keeping information connected across documents, conversations and tool calls. The model works with consistent tokens. Authorized users and tools receive real values only where policy allows.
Keep guardrails for
Detecting prompt injection, harmful content and prohibited requests.
Add Protecto for
Protecting sensitive data while keeping AI workflows running.
A Fortune 100 company protected data across multi-agent AI and deployed within days.
Live enterprise tenants at Automation Anywhere
To deploy at a Fortune 100 company
Long texts a day, at a SaaS company
Most AI apps need a guardrail. Whether they also need Protecto depends on what the AI does with sensitive data.
A security company got to market over 12 months faster with Protecto.
A support rep asks an AI assistant to sum up a refund case. Two people are involved, and the answer depends on who did what.
A guardrail either blocks the request or gives every name the same placeholder. The data is safe, but the support rep gets no useful answer. Protecto gives each person their own consistent token, so the summary comes back right.
Text sent to the model
Maya approved Ravi’s refund, then Maya emailed Ravi.
They do different jobs in the same AI workflow. Here is what each one covers.
| Capability | Protecto | Typical LLM guardrail |
|---|---|---|
| Main job | Protect sensitive data while keeping AI workflows running | Detect prompt injection, harmful content and prohibited requests |
| Prompt injection and jailbreaks | Handled by your guardrail | Core feature |
| Harmful content and prohibited requests | Handled by your guardrail | Core feature |
| What happens to sensitive data | Replaced with consistent tokens, and the task keeps running | The request is blocked, or values become placeholders such as {NAME} |
| Context for the model | Kept across documents, conversations and tool calls | Blocking stops the task. Generic masking breaks the context |
| Telling people apart | Each value gets its own token, the same one every time | Often one placeholder per entity type |
| Restoring real values | By policy, through Protecto Vault | Usually no. One open-source scanner swaps values back from an in-memory store |
| Unmasking by user, AI agent and task | Yes, your application passes who is asking and for what task | No |
| Confidential business information | Context-Based Access Control | Topic or keyword filters that block the request |
| Detection | Agentic detection and classification engine, 50+ entity types plus custom entities | Pattern matching, or open-source detectors such as Presidio |
| Prompts and responses | Masks prompts, restores values by policy, any LLM | Checks both |
| RAG and retrieval | Masks before indexing, applies policy at retrieval | Checks retrieved text when your app sends it |
| AI agents and tool calls | A tool gets a real value only when policy allows it | Varies. Some PII filters don’t check tool inputs or results |
| MCP servers | Controls which MCP servers get which real data | Varies by product |
| Where it runs | SaaS, private cloud, on-premises, air-gapped | Cloud service or open-source library |
| Audit logs | Logs each request and reveal | Logs check results |
| Compliance evidence | SOC 2 Type II, ISO 27001. Signs a BAA when Protecto hosts, manages or can access your instance | Depends on the vendor |
Based on public documentation for widely used LLM guardrails, reviewed November 2026. Products differ, so check the one you use.
A guardrail and Protecto solve different problems. Pick what you need and see which one handles it.
What do you need to do? Pick one.
A guardrail tells you a prompt holds sensitive data. Keeping the task running with that data 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 person needs the same token across sessions, retrieved documents and tools.
Who sees real values, by user, AI agent and task, and how your apps pass that context.
Hooks at every step of an AI agent run, including tool inputs and tool results.
A record of who saw which real value, and why.
Each one does a different job in the same request. The guardrail detects prompt injection, harmful content and prohibited requests. Protecto protects the sensitive values, both ways.
You keep your guardrail and your models. Protecto works with any LLM and any guardrail, in the same path your app or AI gateway already uses.
Your app
A support rep asks for a case summary.
Sum up Maya Shah’s refund for Ravi Kumar
Protecto
Swaps each sensitive value for its own consistent token.
<PERSON>agjwd</PERSON> … <PERSON>hqzpt</PERSON>
Guardrail
Checks the request for prompt injection, jailbreaks and off-topic asks.
Any LLM
Answers with tokens and never sees the real names.
Any LLM
Writes the summary with the same tokens.
<PERSON>agjwd</PERSON> approved the refund
Guardrail
Checks the answer for harmful or off-policy content.
Protecto
Restores real values only for the users your policy allows.
Your app
The support rep sees the real names. A user without access sees them masked.
Maya Shah approved the refund
Several open-source guardrails find PII with Presidio. An independent benchmark by DataXpert and UT Dallas, published by Protecto, tested it on default settings.
Phone false positives
11 Protecto, 502 Presidio
Credit card false positives
0 Protecto, 232 Presidio
SSN false positives
0 Protecto, 55 Presidio
How to read this. 3,000 synthetic samples scored at the entity level, March 2025. Presidio was analyzer 2.2.351 with the default en_core_web_sm model. Guardrails that use Presidio may run other models or settings, and Presidio has released new versions since this study. Test on your own data.
“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 guardrails, or LLM guardrails, are checks that run on the prompt before it reaches a model and on the answer before it reaches the user. They look for prompt injection, jailbreaks, harmful content, off-topic requests and, in many products, personal data. Our guide to LLM guardrails covers the types in more depth.
Not on their own. Generic guardrails detect sensitive data, then block the request or replace each value with a placeholder such as {NAME}. Blocking stops the task, and generic masking breaks the context the model needs. Protecto keeps the context with consistent tokens, and authorized users and tools receive real values only where policy allows.
No. Protecto is data security for AI. It finds sensitive data, replaces it with consistent tokens, and returns real values to the users, AI agents and tools your policy allows. It does not check prompts for injection, jailbreaks or harmful content, so keep a guardrail for those.
Yes. Protecto works next to any guardrail, including Amazon Bedrock Guardrails, Google Model Armor, NVIDIA NeMo Guardrails and Guardrails AI. The guardrail keeps doing its safety checks, and Protecto handles the sensitive values in the same request. Protecto also works with any LLM, so you don’t change models to add it.
LLM Guard (now part of Palo Alto Networks) and F5 AI Guardrails (formerly CalypsoAI) are AI security tools. They scan prompts and answers and can redact personal data. Protecto adds what happens next: consistent tokens, real values for allowed users, and policy by user, AI agent and task.
Usually not. Most guardrail PII filters mask one way. One open-source scanner can swap values back, but only from an in-memory store that lasts as long as the running process. Protecto Vault keeps the mapping and restores values by policy, with every reveal logged.
It varies by product. Some PII filters only check the prompt and the final answer, not tool inputs or tool results. Protecto masks data at each step of an AI agent run, and a tool gets a real value only when policy allows it.
Run Protecto first. It masks the prompt, so the guardrail and the model only ever see tokens. On the way back, the guardrail checks the answer, then Protecto restores real values for the users your policy allows.
It depends on the detector inside the guardrail. In the DataXpert and UT Dallas benchmark, Protecto had a higher F1 score than Microsoft Presidio on default settings, the detector inside several open-source guardrails. Test both on your own data.
Bring a prompt, document or AI agent run, and see how Protecto handles it on your own data.
A third-party study compares how accurately Protecto, Microsoft Presidio, and AWS Comprehend find personal and health data in unstructured text.
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