Protecto provides AI-powered data protection by automatically identifying and masking sensitive information such as PII and PHI, ensuring compliance with regulations like GDPR and HIPAA. The platform enhances the security of GenAI applications while maintaining model accuracy, allowing businesses to leverage AI without compromising data privacy.
Funding
$5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations face challenges in securing sensitive data, such as Personally Identifiable Information (PII) and Protected Health Information (PHI), when leveraging Generative AI (GenAI) applications. Traditional data masking techniques can reduce the accuracy of AI models, hindering their effectiveness and limiting their potential. Compliance with data privacy regulations like GDPR and HIPAA adds further complexity to the secure deployment of AI.
Solution
Protecto offers an AI-powered data protection platform designed to automatically identify and mask sensitive information, ensuring compliance without compromising the accuracy of GenAI models. The platform employs intelligent tokenization and other advanced data masking techniques that preserve data utility and context, allowing AI models to perform optimally while safeguarding privacy. Protecto's solution provides comprehensive data security across prompts, responses, and context data, enabling businesses to leverage AI responsibly and securely. The platform offers flexible API options for seamless integration into existing systems, along with audit trails and reports to meet compliance requirements.
Target Audience
Protecto targets enterprises across various industries, including healthcare, finance, and technology, that are deploying GenAI applications and require robust data protection and compliance solutions.
Features
- AI-powered sensitive data scanning for accurate identification of PII, PHI, and other sensitive data.
- Custom pattern identification to define and identify unique data patterns specific to business needs.
- Accuracy-preserving data masking techniques tailored to specific security and privacy requirements.
- Entropy-based tokenization methods like Anonymization and Pseudonymization for secure data tokenization.
- Attribute & role-based controls to implement granular access controls based on user roles and data attributes.
- Flexible API options (sync and async) for seamless integration with preprocessing, context data, prompts, or responses.
- Audit trails & reports on scan, mask, and unmask activities to meet compliance needs.
- On-Premises or SaaS deployment options with multitenancy support.