Built for teams who can't ban AI and can't ignore it either.
PromptSec applies the same browser-level detection to how each industry actually gets exposed — client code pasted into a chat tool at an IT services firm, patient identifiers at a hospital, card numbers at a bank. Blocking AI tools outright just pushes usage onto personal devices you can't see; the realistic goal is to make the safe path the easy one.
By use case
Shadow AI discovery
Most organisations can't say which AI tools their staff actually use, how often, or with what data. Because interception happens at the browser, the first week of telemetry answers that — including tools that never show up in a CASB catalogue.
Available todaySecret and credential leak prevention
AWS keys, bearer tokens, and private key blocks pasted into a prompt get caught and can be blocked before the request is sent. This is the highest-severity, lowest-ambiguity case there is — nobody has a legitimate reason to paste a private key into a chatbot.
Available todayAI governance evidence
A per-decision record of what was detected, which policy version applied, and what action followed — the evidence an auditor actually asks for. The chain is tamper-evident: editing or deleting an entry breaks it, visibly.
Available todayBy industry
Where the pressure comes from, and honestly how well we meet it today.
Banking & financial services
- The pressure
- RBI cybersecurity guidelines and SEBI obligations, against staff who find AI genuinely useful for drafting and analysis.
- Where we fit
- Card numbers are Luhn-validated today, and the policy engine can hard-block them outright. Every decision lands in a hash-chained audit trail.
IT services & product engineering
- The pressure
- Client code under NDA, pasted into an AI tool to debug it — often the single largest source of accidental disclosure.
- Where we fit
- Source-code detection and secret detection run on every prompt, and a warn action teaches the boundary instead of just blocking outright.
Healthcare
- The pressure
- Patient identifiers showing up in prompts asking for a summary or a second opinion.
- Where we fit
- Contact identifiers and structured government IDs are caught today. Health-specific entity recognition needs the NER layer, which isn't shipped yet — worth knowing before you evaluate us on it.
Public sector & regulated bodies
- The pressure
- Citizen data, and a low tolerance for any of it leaving the country or the organization.
- Where we fit
- On-device detection means prompt content need not leave the machine at all, and Aadhaar numbers are checksum-validated and caught today.
We're not certified against any of these frameworks today, and nothing here should be read as a compliance guarantee. What PromptSec provides is a technical control and the evidence trail that backs one up. How we handle data.
Find out what your team is
already sending to AI.
The first week of telemetry usually answers that better than any policy document. No credit card, no commitment — you reach the founding team directly.
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