PrivacyPal
Harness the world’s best AI, privately.
88 followers
Harness the world’s best AI, privately.
88 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!
@shayra_antia Congratulations on the launch. Shadow AI is a huge problem already. I operate in third party risk management space and already seeing a lot vendors not dealing with this issue.
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.
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.
No disruption to user experience is a core promised benefit. In real workflows, is this data replacement completely transparent and unnoticeable to end users? Will it cause significant latency in request response times or introduce extra operational steps?
PrivacyPal
@long_wang4 Hi Long, there is absolutely no disruption to the user experience; in fact, seamless protection is a core benefit of our platform.
The process is invisible to the end user unless they choose to see it. We have a Shield Protection toggle at the bottom of the screen that you can pull up. This allows you to see the "Privacy Twin" in real-time, showing you the full LLM response and exactly how the information was protected before being finalized.
Please give it a try and download it!
Here is the Chrome extension link:
https://chromewebstore.google.com/detail/privacypal/jejpbafongjgfknkpphahpfkcmaebbbn
For IT departments, ease of deployment is critical. Is the integration process with enterprises’ existing identity authentication systems, AI application platforms and data sources seamless—or does it require complex configuration or system
PrivacyPal
@qwang_dazee Hi Qi Wang, thanks for the question!
The setup is designed to be seamless, featuring a guided interface and a simple one-line installation process. We natively support Google and Microsoft identity systems, and we are fully compatible with other OAuth and SAML identity providers for custom enterprise needs.
The provided audit logs and governance tools are highly valuable for managing Shadow AI. Are the details in these logs sufficiently comprehensive and intuitive to enable administrators to quickly identify risks and gain clear visibility into how data is being utilized?
PrivacyPal
@ironliuyi
Yes — we provide ISO-aligned audit logging designed to meet common regulatory expectations (including ISO, PCI, and HIPAA).
At a high level, our audit logs give organizations clear visibility into who accessed data, what activity occurred, and when, with controls in place to support security reviews, compliance reporting, and incident investigation — without exposing sensitive content itself.
The goal is accountability and traceability, while keeping customer data protected and minimally exposed.
Balancing security, user experience and cost is always a key enterprise consideration. Compared with traditional data desensitization or isolation solutions, does the improved efficiency and enhanced security it delivers feel cost-effective?