Stop sensitive data from leaving in an AI prompt.
Customizable redaction detects sensitive and proprietary information and replaces it with placeholders before a prompt ever leaves your system. Your staff keep using AI; customer and member data stays put.
- Detection runs on the employee's device
- Sensitive data is replaced with placeholders
- Runs before a model call or internet search
- Rules you define, per assistant and per model
- Inline reminders that point to your AI use policy
Redaction happens before anything is sent.
DLP runs before data is sent to a model and before an internet search is performed, so the protection does not depend on what a third-party provider does with the prompt.
Detection
Finds names, account numbers, links, and other sensitive or proprietary information in the prompt.
Redaction
Replaces sensitive information with placeholders, so the model still gets the context it needs to help.
On-device
Sensitive data is caught on the employee's device, before it leaves your system.
Customizable pre-filtering
You define what counts as sensitive, in line with your own data classification and policies.
Per-model rules
Set redaction rules per assistant and per model, with stricter rules for External models than for those inside your tenant.
In-context guidance
Inline reminders point staff to your AI use policy at the moment of risk, so every redaction is also a lesson.
Safer prompting without slowing your staff down.
The details your staff paste in without thinking.
Your member service reps summarizing calls, loan officers drafting credit memos, analysts triaging complaints: each of these can put personal data into a prompt. MultiModel catches it first.
- Customer and employee names and personal identifiers
- Account numbers, IDs, and proprietary references
- Internal links and URLs that reveal private systems
- Custom patterns you define for your institution
Choose which data categories are detected when DLP is enabled for a provider. Unchecked categories are ignored and not redacted.
Identifies sensitive information based on meaning and context.
Identifies sensitive information by matching specific formats, structures, and known patterns.
Words or phrases that will never be redacted, even if they match a detection rule. Case sensitive. e.g. your company name, product terms, or any text that should always pass through.
Words or phrases that will always be redacted, regardless of other detection rules.
See exactly what was redacted, and where.
Every redaction is recorded next to the prompt it came from, so your compliance team can confirm DLP worked without anyone having seen the original values.
- The redacted prompt, as the model received it
- User, model, time, and data types caught
- Searchable for audits and exams
3:46 PM What is my cash ordering procedure? mreyes@amfinancial.com Branch Policy Q&ASonnet 5 Internal No
1:08 PM Can you check the status on bank account US_BANK_NUMBER_1? PERSON_2's Social Security number is US_SSN_1. kfoster@amfinancial.com ChatGPT w/ Web SearchGPT-5.4 External Yes
7:43 PM What do I do if I get a counterfeit bill? mreyes@amfinancial.com Teller AIRAG Internal No
7:42 PM Review this HELOC exception against our lending policy. mreyes@amfinancial.com Loan AdvisorRAG Internal No
4:15 PM Draft a friendly reminder to a member about a missed auto loan payment. jellis@amfinancial.com Member LettersSonnet 5 Internal No
11:02 AM Summarize the latest NCUA guidance on third-party AI vendors. kfoster@amfinancial.com ChatGPT w/ Web SearchGPT-5.4 External No
Where DLP matters most in banking
Customer and member support
Draft replies and summarize conversations without sending account details to a model.
Customer supportComplaint triage
Sort and route complaints while names and identifiers stay out of the prompt.
Triaging complaintsLoan application pre-review
Summarize loan files with account numbers and IDs redacted first.
Loan applicationsAI data loss prevention: FAQ
Straight answers for CIOs, CISOs, and compliance officers evaluating AI.
Talk to usWhat sensitive data does MultiModel detect?
Out of the box, MultiModel looks for customer and employee names and personal identifiers, account numbers and other IDs, proprietary references, and internal links or URLs that reveal private systems. You can add your own patterns, so the definition of sensitive data matches your institution's data classification.
Where does redaction happen?
On the employee's device. Sensitive information is detected and replaced with placeholders before the prompt is sent to any model, and before any internet search is performed. The original data does not leave your system in the prompt.
Can we apply different rules to different AI models?
Yes. Redaction rules can be set per assistant and per model. A common approach is to apply stricter rules to External models than to models running inside your own tenant. See AI guardrails for how Internal and External models are separated.
How do your staff know why something was redacted?
MultiModel shows inline reminders at the moment of risk that point staff to your AI use policy. That turns each redaction into a short piece of training. If you don't have a policy yet, start with our AI use policy template for banks and credit unions.
Does DLP replace our existing data loss prevention tools?
MultiModel's DLP is built for AI prompts specifically: it inspects what your staff type into AI assistants before it is sent. It sits alongside the controls you already run on email, endpoints, and file sharing.
See redaction run on your own prompts.
Bring examples of what your staff type into AI tools. We'll show you what MultiModel detects, how it is redacted, and how you set the rules.