Hardshell - Unlock Data Where It Lies. Make It Useful for AI.

How it Works

Where does your enterprise AI leak data?

Models memorize sensitive content from training data, and RAG pipelines surface documents users were never cleared to see. Pick an attack scenario to watch each pathway leak, and see what changes when Hardshell sits between your data and the model.

Platform

The secure foundation between your data and your AI

Your data stays in your environment. RAG applications, model training, and fine-tuning run on top of a secure data foundation, and every workflow inherits the same measurement, monitoring, and hardening.

Tested with real attacks

Live-Fire Exposure Testing

Hardshell's platform gives you vulnerability scores backed by demonstrated attacks and the evidence behind each one. Point it at any system that puts your data behind a model: a chatbot, a support agent, internal search.

Watch every retrieval

RAG Telemetry and Detection

Add one package to your RAG application and Hardshell sees how your corpus is actually being retrieved. Which chunks each query returned, for whom, and whether the pattern looks like extraction. Detection runs across requests, so distributed exfiltration surfaces too. Telemetry is keyed to opaque IDs, so document content and PII never leave your environment.

Dataset Hardening

When testing finds exposure, Hardshell hardens the data itself before any model touches it. Build-time transformations reduce what an attacker can extract, and runtime defenses prevent access when anomalous behavior is flagged.

Utility Verification

Hardening only counts if the system still works. Hardshell continuously evaluates answer quality against your own benchmarks and flags regressions as your corpus grows, so security never quietly costs you performance.

Multiple layers of a data security system for AI

Model-agnostic, store-agnostic

Works Where Your Data Lives

Native integration for major AI platforms plus a standard interface for self-hosted and open-weight models. Built on OpenTelemetry, so the same telemetry stream mirrors into the observability stack you already run. Beyond RAG, the same foundation secures tabular model training with attack-tested hardening.

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