PrivacyPal
The Browser Extension for AI Governance & Security.
92 followers
The Browser Extension for AI Governance & Security.
92 followers
Secure your organization’s AI posture without breaking the user experience. We use Privacy Twins—not redaction—to replace sensitive data with synthetic context, ensuring LLMs give 100% accurate results. Includes full audit logs and governance tools to manage Shadow AI.
PrivacyPal
PrivacyPal
@fmerian Give it a look!
All the best with the launch! Really curious to know how the synthetic data swap maintains semantic meaning. Since this is a browser extension, is there also a way to enforce it on a user's browser?
PrivacyPal
@mustassim
We maintain original data statistical accuracy, based on the type of data detected, e.g. Names or locations maintain key traits like gender or geographic proximity.
Regarding forcing or requiring the extension in the browser organizations that manage their users browsers are able to do this, their IT admin would need to facilitate it. Individual users will have to install the extension themselves.
PrivacyPal
We’ve been developing security solutions and listening to clients for over 5 years. PrivacyPal wasn't created for the sake of shipping a tool; it was born out of necessity.
Our enterprise clients were practically begging for Guardrails on their AI training models. While we built a sophisticated solution for those complex enterprise needs, we realized the everyday professional needed protection too.
That’s why we built this extension—it’s the power of our enterprise security, simplified for the user who just wants to use ChatGPT safely.
I’d love to hear your feedback!
The synthetic replacement angle here is a clever way to meet security folks where they are without completely wrecking the day-to-day UX for people who just want to use AI tools.
One pattern I keep seeing is security teams wanting strict redaction while ICs need rich enough context for the model to stay useful, and those two incentives often collide.
How do you decide what level of semantic fidelity to preserve in the synthetic context so that the LLM stays accurate but you’re not accidentally leaking more structure than a security team would be comfortable with?
PrivacyPal
@devin_owen Hi Devin, thank you so much for taking the time to chat with us!
We decided this based on two fronts: security teams care about regulatory frameworks (fined based on information), and we have an option where the security team can define dictionary terms that should not be leaked into llms. We also offer a self-hosted solution for enterprises that enables data sovereignty.
A bit about the generation of Privacy Twins—statistically accurate representations of the information that satisfy security requirements while keeping the context rich for the LLM.
Unlike redaction, which creates "holes" in data, Privacy Twins maintain the semantic weight and relationships within the prompt.
Context Preservation: In a medical scenario, we don’t swap a diagnosis (like "Stage 4 Cancer") for something inaccurate. We keep the clinical significance intact because that is what the model needs to stay accurate.
Regulatory Compliance: While the "Privacy twin" mirrors the logic of the original data, it replaces sensitive identifiers (names, DOB, SSN) with synthetic equivalents that follow HIPAA, PII, COPPA, and FINRA standards.
To handle proprietary secrets—like project codenames or internal company terms—we provide an Administrative Portal.
Custom Logic: Security teams can define specific terms or company-sensitive data to be transformed into synthetic equivalents.
Please feel free to share more reviews and feedback on the product with our team.
PrivacyPal
@chrismessinaGive it peak 😉 🎨
Privacy Twins is the right approach here. Redacted prompts lose context and the LLM hallucinates around the gaps. For structured data like tables or JSON where field relationships matter, the swap has to preserve schema integrity or downstream processing breaks.
PrivacyPal
@piroune_balachandran Great question! Yes, it maintains schema integrity throughout the conversation, regardless of how many topics you switch between.
Congrats on the launch! This is a really smart take on the Shadow AI problem, swapping sensitive data instead of redacting it feels like a big usability win. How Privacy Twins handle edge cases like highly domain-specific data, like IDs, logs, or semi-structured text. Does the model still preserve accuracy in those scenarios?