Protection Guard

Prediction Guard provides secure, private LLMs with safeguards for enterprise-grade AI accuracy and reliability.

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Protection Guard's Top Features

Secure, private LLM environments

Scalable model endpoints

Security checks for new vulnerabilities

Privacy filters for PII masking

Output validations to prevent hallucinations

Compliance with HIPAA and BAA

High AI accuracy and reliability

Robust safeguards

Seamlessly integrated infrastructure

Reduced AI budget expenditures

Frequently asked questions about Protection Guard

Prediction Guard offers enterprise-grade AI solutions with secure, private large language models (LLMs) that are highly accurate and surrounded by safeguards.

Prediction Guard's LLMs are secure due to several features, including security checks, privacy filters, output validations, and compliant data protections.

The service includes security checks for new vulnerabilities, output validations to prevent erroneous or harmful predictions, and privacy filters to protect sensitive information.

Industries requiring high AI accuracy and strict data protection can benefit, such as healthcare, finance, and legal sectors.

Privacy filters are designed to mask or replace personally identifiable information (PII) in model inputs, ensuring data privacy and compliance.

Output validations are procedures to prevent the model from delivering incorrect or toxic outputs, thus maintaining high reliability and accuracy.

Yes, Prediction Guard ensures compliant data protections, including adherence to HIPAA regulations and the possibility of implementing a Business Associate Agreement (BAA).

By providing scalable model endpoints and robust safeguards, Prediction Guard reduces the 75% of AI budget typically spent on preventing malfunctions and setting up infrastructure.

You can get started by requesting a demo or reading the comprehensive documentation available on their website.

Prediction Guard is trusted by several industry-leading organizations, though specific names are not mentioned on the upcoming site content.