Sensitive data gets anonymized before your prompts and files leave your environment and restored in the response, so frontier models only ever see placeholders.
Sensitive values in your prompts are swapped before the prompt leaves. The model works on structure, not secrets. NodeShift puts the real values back inside your walls, so the answer is accurate and nothing sensitive ever left.
Without Anonymization
Raw data leaves. Audit nightmare.
"Draft email to John Smith, confirming AED 2.5M wire from AE07 0331 2345 6789 …"
With NodeShift
Only tokens cross. Audit-ready.
"Draft email to [NAME_1], confirming [AMOUNT_1] wire from [IBAN_1] …"
What the user types
"Draft a customer reply explaining why we declined the loan application from Aisha Al Mansoori (Emirates ID 784-1985-1234567-8). Reference application AL-2026-09142 dated April 14, and our internal credit memo CM-09442. Use a polite, regulation-compliant tone."
What the external model sees
"Draft a customer reply explaining why we declined the loan application from [NAME_1] ( [NATIONAL_ID_1] ). Reference application [REFERENCE_1] dated [DATE_1], and our internal credit memo [REFERENCE_2]. Use a polite, regulation-compliant tone."
What the user gets back
"Dear Ms. Aisha Al Mansoori, Thank you for your application AL-2026-09142 dated 14 April. After careful review, including the analysis in credit memo CM-09442, we regret to inform you…"
The anonymization engine runs entirely inside your perimeter, including the local detection LLM that does the work
A purpose-built detection LLM running locally in your environment scans every outbound prompt. Sub-100ms latency.
Every detected entity is replaced with a deterministic placeholder. The original-to-token mapping is sealed inside your perimeter.
The anonymized prompt is sent to Claude, ChatGPT, Gemini, or any of 140+ supported models. Structure and intent — never raw values.
The model's response returns to your perimeter. The sealed mapping restores the original values — reassembled inside your environment.
Both the anonymization and de-anonymization events are recorded as immutable audit entries. Then — and only then — is the response delivered.
Critical guarantee. Raw sensitive data never crosses your perimeter boundary. Even when the prompt requires an external model, only tokens are transmitted. Even when the response needs full context, restoration happens inside your environment.
The detection LLM combines contextual recognition with regex pattern matching, region-aware identifier validation, and user defined classification rules.
Identity attributes that map to GCC privacy regimes — UAE PDPL, KSA PDPL, Qatar PDPPL.
Detected
Banking and payment identifiers, validated by check-digit and format rules where applicable.
Technical secrets that should never appear in any external prompt — critical for Compliant Vibe Coding.
Proprietary content classified by your information security policy.
NodeShift ships with a default catalog covering PII, national IDs, financial identifiers, credentials, and internal document patterns. Your security team can change any of it.
NodeShift records every detection, every masking action, and every restoration as an immutable event so your compliance team can prove to a regulator exactly what was protected, when, and how.
Click any row to see exactly which spans were detected, which token replaced them, and which rule fired.
Export anonymization activity by user, by department, by date range, by regulation.
Logs are append-only and cryptographically chained. Configurable retention per regulatory requirement.
NodeShift implements the controls your regulators expect and gives you the evidence to prove it.
Draft replies, summaries, and explanations that reference real customer data — without that data ever reaching the external model. Names, account numbers, and identifiers are tokenized before transmission and restored on return.
Developers use Claude, ChatGPT, and frontier coding models on real codebases. API keys, internal hostnames, and credentials are detected and permanently masked — never restored, never transmitted, never logged in plaintext.
Process supervisory reports, contracts, and regulated submissions through external models without their sensitive contents leaving your environment. Entities are tokenized; structure is analyzed; the output comes back fully restored.
Run AI workflows across multiple GCC jurisdictions — each with different residency rules — using one anonymization layer that enforces the strictest applicable regime per request.
As seen on
The ideal way for organisations young and old to ease their way into the decentralized cloud at their own pace.
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