Pioneering Contextual Data Redaction for Conversational AI

Safeguard sensitive data with advanced intelligence. We’re transforming data protection with our innovative Contextual Data Redaction technology.

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How does Wald’s PrivateGPT work?

Wald’s PrivateGPT i.e. Contextual Data Redaction is an advanced approach to protecting sensitive information in conversational AI systems. Unlike traditional methods, our technology understands the context and semantics of conversations, providing dynamic and intelligent protection.

Here’s how Wald’s Contextual Redaction works:

Why Choose Contextual Redaction over Traditional Methods?

Smart Redaction at Work: Practical Examples

“Can you explain the role of [Product Manager] in our company? How does it differ from [Project Manager]?”

A contextual system would recognize these as job titles, not specific individuals.

“What are the key features of [Version 2.0] compared to [Version 1.5] of our software?”

These version numbers would likely be left unredacted by a contextual system.

“How can we improve communication between [Team Alpha] and [Team Beta]?”

A contextual system would understand these as team names, not confidential information.

“What’s the best way to onboard new employees to [System X] and [Platform Y]?”

These generic system names would typically not be considered sensitive by a contextual redaction system.

Here's how Wald protects you...

Intelligent Context Analysis

Our advanced Natural Language Processing (NLP) engine comprehends the nuances of language, identifying sensitive information that traditional systems might overlook.

Adaptive Learning for Evolving Threats

Powered by our machine learning core, we adapt to new data patterns and potential vulnerabilities, keeping you ahead of emerging threats.

Comprehensive Data Relationships

By leveraging a Knowledge Graph, we map relationships between diverse pieces of information, enabling context-aware decisions about what requires protection.

Mathematically Guaranteed Privacy

Our Cryptographic Privacy Layer ensures that any sensitive information we store on behalf of our customers is kept encrypted with keys that they own, such that even we can’t access this data. This ensures that even if our servers are breached, the customer information stays safe.

Anonymized Learning on Sanitized Data

We ensure that any improvements to our sanitization models and fine-tuning occur only on anonymized and cleansed data—the same sanitized content sent to external LLMs. Since we never have access to sensitive data, there's no risk of it being used for training purposes.