AI systems fail quietly
Prompt injection, leaked answers, and unsafe agent actions often show up after a customer already saw the output. The platform exists to move detection earlier.
About
CanaryVaults is building practical AI security for real operators
The goal is not to make AI security feel abstract. The goal is to help teams catch attacks earlier, understand what happened, and prove it later.
Why the company exists
CanaryVaults started from a simple observation: most AI incidents become obvious only after a customer, attacker, or regulator already has the important context.
Security teams need proof, not vibes
A suspicious answer or broken workflow is not enough on its own. CanaryVaults is built around evidence, timestamps, and incident context that hold up under review.
Modern AI risk is fragmented
Inbox traps, RAG leakage, prompt defense, honeypots, and audit records are usually spread across separate tools. CanaryVaults brings those surfaces into one operating model.
Product principles
What the platform is trying to optimize for
- Make every product surface narrow enough that operators immediately understand what risk it covers.
- Prefer believable detection signals over dashboards that look busy but do not prove anything.
- Treat auditability as part of product behavior, not an afterthought added for enterprise sales.
- Keep the system legible for small teams so AI security work does not require a large platform team.
Operating beliefs
What shapes product decisions
Security should be explainable
Operators need pages, alerts, and controls that explain the why behind each decision. Clear language matters as much as raw detection depth.
Proof must stay portable
Evidence should survive leadership reviews, customer escalations, and compliance checks. That is why the platform leans on hashes, exports, and linked event history.
One login should not mean one giant product blob
The platform is intentionally split into clear modules so teams can adopt only the surface they need without losing the benefit of shared auth and alerts.
How the platform is structured
Each module solves a different class of AI risk, but they share a common model for auth, alerting, evidence, and operator workflow.
Tripwires for exposed identities and inboxes
Plant monitored identities where stolen data is likely to surface, then alert the moment an attacker touches them.
Leak evidence for private-document AI
Inject believable trap facts into sensitive knowledge so leaked answers can be proven instead of guessed.
Prompt defense before model execution
Inspect, classify, and sanitize hostile prompt traffic before it reaches a live LLM or agent flow.
Attacker observation without exposing the real stack
Deploy decoy AI surfaces that capture probing, jailbreak attempts, and extraction behavior for later analysis.
Tamper-evident records for agent behavior
Record what an agent did, why it happened, and what evidence exists if someone challenges the outcome later.
Runtime oversight for OpenClaw-style agents
Track agent heartbeat, skill changes, and risky runtime drift so operational teams can intervene sooner.
What to read next
If you are evaluating the platform, the best next step is to review how the products fit together, how trust surfaces are described, and how support flows are organized.