TrustPrompt - 2026 Pricing, Features, Reviews & Alternatives

TrustPrompt is a browser extension for Chrome and Edge that performs on-device detection and redaction of personally identifiable information before prompts are transmitted to public large language model services. It supports nine distinct web applications, including major conversational AI platforms as well as emerging generative research tools. All analysis and transformation of prompt text occurs locally, with no external servers or cloud services involved in detection. The architecture prevents any unredacted sensitive data from leaving the device, addressing compliance requirements for sectors such as healthcare and legal practice.

Detection relies on a dual-layer system combining deterministic pattern matching and transformer-based named entity recognition. The first layer uses forty checksum-validated regular expressions to identify structured data elements such as international bank account numbers with per-country checksum validation, payment card numbers verified by a Luhn algorithm, national identification numbers, passport identifiers, API keys, bearer tokens, and encoded private keys. The second layer employs a transformer model with five hundred sixty million parameters to recognize unstructured entities including personal names, postal and street addresses, organizational names, and specialized clinical or demographic attributes across fifty-four entity categories in twelve languages. Execution is optimized for local hardware by attempting a high-precision GPU path before falling back to lower quantization or a WebAssembly alternative, ensuring consistent performance independent of network connectivity.

Redaction is integrated directly into the user interface by intercepting the native send operation, applying built-in rules and custom watchlist terms defined by users or administrators, and conducting on-device model classification with adjustable confidence thresholds. A lightweight classifier refines results by suppressing learned false positives, and a preview interface presents findings for optional review prior to transmission. Identified data spans are replaced with typed placeholders such as [FIRST_NAME_one] or [IBAN_two] to preserve structural context, with placeholder mappings maintained only within the session and discarded upon tab closure. The extension operates in a default fail-closed mode to block any transmission if a redaction step encounters an error. Only minimal browser permissions are required, no telemetry or error reporting is conducted, and an optional enterprise audit webhook can transmit metadata without exposing actual prompt content or detected values.

Enterprise deployment is managed through standard policy frameworks such as Windows Group Policy, Microsoft Intune, and Jamf, with policy objects serving as the sole entitlement mechanism and no external license server or activation call. Administrators configure detection categories, set confidence thresholds, define fail mode behavior, create proprietary regular expression rules for unique identifiers, specify audit endpoints, and select the preferred execution backend. End users retain control over a master toggle, custom watchlist terms, false-positive dismissals, and resetting the on-device learning model. After installation, the extension functions fully offline, embedding the detection model and weights within the package to remove any need for external connectivity during operation.