SOLUTIONS

Prompt Injection

Data Leakage & PII

Employee AI Use

Agentic AI Risk

Brand Protection

SOLUTIONS

Data Leakage & PII

Of all the ways AI can hurt you, this is the one that reaches a regulator's inbox and the morning headlines at once. Customer records pasted into a model you don't control. A RAG assistant handing someone a file they were never cleared to see. And unlike most AI risks, this one already has decades of regulation aimed straight at it.

Our AI is going to leak something it shouldn't have.

We inspect what your AI takes in, sends back, and pulls from your own data. The moment the wrong data heads somewhere it shouldn't, we stop it.

WHAT IT IS

Three ways your data leaks

Data leakage is when sensitive information ends up somewhere it shouldn't: outside your control, or with someone who was never meant to see it. With AI, it happens three ways.

Inbound Ingestion

An employee pastes customer records or source code into a public chatbot. Or a pipeline feeds the same into a model or vector store you don't control.

Inbound Ingestion

An employee pastes customer records or source code into a public chatbot. Or a pipeline feeds the same into a model or vector store you don't control.

Outbound Exposure

The model surfaces private data straight to a user: something it remembered from training, or something it was manipulated into revealing.

Outbound Exposure

The model surfaces private data straight to a user: something it remembered from training, or something it was manipulated into revealing.

Broken Access Control

A RAG assistant or agent fetches an internal document and returns it to someone who was never cleared to see it.

Broken Access Control

A RAG assistant or agent fetches an internal document and returns it to someone who was never cleared to see it.

REAL INCIDENTS

It's already happening

Data leakage isn't hypothetical. The incidents are public, they're well-documented, and they're getting more frequent.

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Engineers pasting source code into a public chatbot.

Models surfacing their training data.

A RAG assistant returning the wrong tenant's data.

THE STAKES

The consequences don't stay technical

Regulatory exposure.

Data leakage brings massive regulatory penalties (GDPR, HIPAA, EU AI Act). When fines hit millions, saying 'our AI did it' is no defense.

Contractual exposure.

Sending unauthorized data to third-party models breaches customer contracts, turning a technical leak into a legal dispute.

Trust collapse.

Reputational fallout is irreversible. Losing customer trust and employee engagement destroys your AI’s credibility instantly.

THE FIX

How we stop all three

Data leakage isn't one problem with one fix. It happens three ways: data you send out, data the model gives away, and data the wrong person pulls. We cover each with its own guard.

Data you send out

Context Guard

Keeps sensitive data from leaving your boundary for a model or service you don't control.

Workflow

A person or a pipeline sends data toward an outside model: an email, source code, a document.

Context Guard intercepts the content before it reaches any external or third-party model.

The fine-tuned classification engine identifies PII, PHI, financial secrets, and credentials.

Sensitive data is automatically classified, masked, or redacted.

Data the model gives away

AI Guard

Inspects the prompt going in and the response coming back, so the model doesn't hand a user data it was never meant to reveal.

Workflow

A user submits a prompt, and the AI Guard inspects it for sensitive data before it hits the LLM.

The AI processes the query and generates a response payload.

AI Guard intercepts the response before it renders on the user's screen.

Any accidentally surfaced training data or internal credentials are completely masked or blocked.

Data the wrong person pulls

RAG Guard

Ensures AI assistants cannot fetch or return documents from unauthorized knowledge bases.

Workflow

A user asks a RAG-powered AI assistant to retrieve information from internal company data.

RAG Guard evaluates the user's access permissions.

The RAG Guard restricts the retrieval engine to only search authorized datasets and vector stores.

Unauthorized records are strictly isolated, preventing the AI from displaying documents the user shouldn't see.

RESOURCES

What we're learning and sharing.

Research reports, threat intelligence, deployment playbooks, and the occasional blunt opinion on where the AI security category is going.

See All

Research

Adversarial Multi-Turn Prompt Injections

Guide

AI Security Glossary

Research

Decoding the Latest AI Exploits