Protecto runs inside your bank, finds customer data headed to AI and applies your policy: models get tokens, approved roles see real values, every release is logged.
Animated example. A relationship manager asks the bank's AI assistant to summarize a loan file that contains a customer's Arabic name and IBAN. Protecto detects both values, replaces them with tokens, and sends only tokens to the AI model outside the country. The model answers in tokens. Protecto restores the real values for the relationship manager and logs the release. For a contact center agent, the IBAN stays partly hidden.
Recall on sensitive data, incl. Arabic
Precision, so little is over-masked
Semantic accuracy retained
To a working proof of concept
A bank in the Gulf wants its relationship managers to use an assistant built on a frontier model, such as Gemini, GPT or Claude. The model runs in a data center outside the country. Every request would carry customer names, account numbers and payment history.
The bank’s regulator expects customer data to stay at home, so the project sits in review. This is the data residency problem: the best AI is somewhere the data is not allowed to go.
The data stays home, but answers get weaker, and your team pays to run and tune the model.
The regulator is satisfied. Your teams fall behind banks that ship AI on the work that matters.
DLP still guards email, endpoints and file transfers, but it was not built to follow data into prompts, retrieval and agent tools. Redaction turns a loan file into “[REDACTED] missed a payment on [REDACTED]”, and the model has to guess.
Sector rules for banks are often stricter than the national privacy law. Here is the short version for four markets.
Summaries, not legal advice. Confirm your position with counsel and your regulator.
Protecto runs in your private cloud or data center. Data passes through Protecto before it reaches a model, a search index or a tool.
On-premises · Private cloud · Air-gapped · SaaS. When Protecto runs in your environment, token mappings, keys and logs stay on your servers.
Bank Muscat wanted Gemini for its teams. Its regulator said customer data stays in the country. Its records mixed Arabic and English, with transliterated names and Arabic numerals, which is hard for pattern-based tools.
Protecto ran inside the bank’s private cloud, found the sensitive values in both languages and sent the model tokens only. The proof of concept was working in four weeks.
Blunt redaction breaks the answer. Protecto swaps sensitive values for tokens that keep the data’s shape, and controls who sees the real ones.
The same customer gets the same token in every message and document, so the model can follow one account through a loan file.
A tokenized IBAN still looks like an IBAN and a date stays a date, so downstream checks and tools keep working.
Detection is officially tested in seven languages, including Arabic and English, and handles transliterated names and Arabic numerals.
Controlled unmasking returns real values only to the roles your policy names, and logs each release.
Summarize a client’s file, draft a follow-up and prepare a meeting. Data: names, account and loan details, notes.
Draft replies to complaints and requests in Arabic or English. Data: names, phone numbers, card and account numbers.
Summarize applications and supporting documents for an analyst. Data: national IDs, income, payment history.
Let staff ask questions over internal documents through a search index that holds only tokens. Data: customer details inside those files.
Possibly. Tokenization is pseudonymization, not anonymization: while the mapping exists, a token can be linked back to a person. Whether sending tokens abroad counts as a transfer is a question for your counsel and regulator. Protecto gives you the control: you decide which recipients get tokens, which get real values, and which get nothing.
When Protecto runs in your environment (on-premises, private cloud or air-gapped), detection, token mappings, keys and logs stay on your servers. External models only receive tokens.
Keep your DLP. It still guards email, endpoints and file transfers. Protecto covers the AI path that DLP was not built for: data going to models, search indexes, agents and tools. It finds sensitive values in free text, including Arabic, and applies your policy for each recipient instead of blocking the request.
Your policy, where Protecto runs, where token mappings and keys are kept, and a log of which AI received which customer data and who saw real values. When Protecto runs in your environment, those logs stay on your servers.
Protecto is model agnostic. Use models from OpenAI, Anthropic or Google, or a model you host in-country, behind the same policy.
Yes. Detection is officially tested in seven languages: English, German, French, Spanish, Dutch, Arabic and Japanese. In the Bank Muscat case study, on mixed Arabic and English data, Protecto reached 99% recall and 96% precision.
Consistent, format-preserving tokens keep the structure the model needs. Bank Muscat retained 85% semantic accuracy after masking.
Bank Muscat had a working proof of concept in four weeks. Bring a sample of your own data and we will scope yours on the first call.
This page is general information, not legal advice.
Share a sample of customer text in Arabic, English or both. We will run it through Protecto and walk you through what each model and tool would receive.
Bank Muscat wanted employees to use Gemini on everyday banking work. See how Protecto kept customer and financial data inside the bank. 4 pages.
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A path-by-path map of what may leave the Kingdom, and on what footing.
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Identify the applicable privacy regime and protect data across AI workflows.
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Identify the applicable privacy regime and protect data across AI workflows.
By submitting you agree to Protecto’s privacy policy.
Preserve useful AI context while addressing permissions, sensitive data, and overseas access.
By submitting you agree to Protecto’s privacy policy.
