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FOR SAAS TEAMS

Your churn risk is a breach you learned about last.

SaaS teams find out about leaked exports and credential-stuffing runs from angry customers. Canaries flip that: plant decoys in the places attackers touch first, and get the alert — with evidence — before the support tickets arrive.

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SIX PLACES TO PLANT A CANARY

Where SaaS products actually leak.

01

Know when a stuffing list includes you

CanaryVaults seeds synthetic canary credentials to public leak surfaces on a staggered schedule. The moment one is tried against a login — yours or anyone's — you know your users are on a circulating list, with the attempt's infrastructure captured as evidence.

Decoy identities →
02

Wire your customer exports

Every CRM export, contact dump, and contract annex gets one decoy identity that exists nowhere else. When a file leaks — an employee laptop, a vendor, a misconfigured bucket — the decoy tells you which file, with full headers on first contact.

Per-file attribution →
03

Trap-fact the docs behind your AI features

If you ship an AI assistant over customer or internal documents, trap facts prove when that content surfaces somewhere it should not — a competitor's bot, a leaked knowledge base — even after paraphrase.

RAG trap facts →
04

Put a firewall in front of your LLM endpoints

Your users paste untrusted content into your AI features every day. CanaryShield inspects each request through rules, a semantic analyzer, and a sanitizer — adopted by changing one base URL in your OpenAI-compatible client.

Prompt-injection firewall →
05

Let attackers rehearse on a decoy

Deploy a honeypot assistant that looks like your production support bot. Probes get classified by intent — jailbreak, extraction, probing — and fingerprinted, so you see the playbook before it reaches the real endpoint.

Decoy assistants →
06

Read one briefing a week, not a thousand alerts

The weekly intelligence briefing rolls every surface into one evidence-linked digest: new hits, attacker fingerprints, and campaign rollups — including credential-stuffing campaigns identified from canary credential reuse.

All surfaces →

BUILT ON THE SAME STACK

We run on Supabase and Next.js — the stack we protect is the stack we ship on.

CanaryVaults itself is a SaaS product on Supabase auth and Postgres. The deception patterns on this page — decoy rows in exports, canary credentials near the auth flow, sealed logs for service-role automation — are patterns we apply to our own deployment, not theory. Nothing here requires agents on your infrastructure: canaries are planted in your data and in front of your endpoints, so they work the same on Supabase, RDS, or anything else.

IS THIS FOR YOU

A fit if any of these sound familiar.

✓You run a multi-tenant product where one leaked export is a breach-notification event.✓Login abuse shows up in your support queue before it shows up in your dashboards.✓You ship LLM features and cannot see what users paste into them.✓Security is a hat someone wears, not a team — evidence-rich alerts matter more than dashboards.

GO DEEPER

Field notes from the team.

Why AI security alerts need evidence attached from the start

The design principle behind every alert on this page.

Proof beats suspicion when private documents leak through AI

What happens when your docs feed someone else's model.

Why prompt injection needs its own firewall

Why moderation-after-the-fact fails for SaaS AI features.

Plant your first canary before the next export leaks.

Free tier, no credit card. Five minutes to your first alert.

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Deception-based AI security. Decoys, trap facts, honeypots, prompt defense, and tamper-evident audit trails — one workspace.

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