Frequently Asked Questions

Should you worry that your data and IP – research data, training manuals, professional journals, medical data, meeting recordings – has been swallowed up by AI or misused internally? Yes. Compliance is a huge issue where AI is rapidly becoming a system of record.

And you’re losing money, brand protection, and market reputation as a result with lower customer engagement, regulatory fines, and consent orders that add friction to operations. That’s where Parsifer comes in. Our platform equips data owners – CIOs, Compliance Officers, and IT professionals – with a radically innovative solution to identify the use of their data by AIs, create audit trails, and provide evidence to enforce their policies, and monetize their IP.

1. What’s Parsifer about?

Parsifer equips data and IP owners to thrive in a world of AI risk. And by risk, we mean the widespread theft of copyrighted IP by AIs rampaging across the Internet. We also mean the unauthorized use of data by employees, hackers, and exploits such as bots and scrappers. Our solution is a revolutionary yet easy-to-use digital service for data governance that identifies how your information is being used by an AI model or agents, both internally and externally.

We offer three things to our customers:

1. A unique, proprietary Identify, Track, & Trace tool that monitors data usage & AI output

2. Easy-to-follow analytics and an audit trail to enforce data rights, policies, and spot security gaps

3. Compliance means data governance, system orchestration, the memory of what happened:
   Parsifer protects artifacts and timelines.

Parsifer is an empowering technology, providing IP creators with hard data on how their work is reflected in AI systems.

2. Is the situation that urgent regarding AI exploitation of data?

AI systems – specifically, Large Language Models (LLMs) – have been built, or trained, on digital content from the Internet. Put bluntly: LLMs have pretty much swallowed up all forms of digital content, including IP under copyright and behind paywalls. And moreover, the pressure from executives is on to operationalize AI at all levels of the workforce.

Many AI companies still, remarkably, claim they do not violate copyright laws in acquiring their training content, either buying or licensing it, or relying on the fair use principle. Believe that if you like (but don’t). They also claim that they can’t track the data they use or pinpoint its source. It’s highly probable your work has been swallowed, either taken in directly by an LLM or acquired from a pirate website somewhere offshore.

Parsifer provides verifiable insight – hard evidence – to determine whether your work has been uploaded into an LLM – or multiple LLMs. The solution also works across the wider internet to check competitors and websites for pirated data.

3. Why is Parsifer important for data-driven enterprises?

It’s pretty simple: the unauthorized use or appropriation of your data degrades your ability to monetize it.

To make matters worse, the AI processing of your work – stripping it of identifying markers and chopping it up in ways not intended – often results in misrepresenting your original intent, causing reputational and brand damage, not to mention annoying the hell out of you.

This is basically a multi-billion dollar revenue grab by AI companies in violation of your rights and ability to make a living from your data and IP.

4. You appear to see American innovation in AI as the enemy?

AI has many great uses and millions of people and businesses are engaging today with LLM models to improve productivity, to learn, to make their lives better. Like many technologies, AI is a tool, neither good nor bad, but the way it’s used can be either.

Parsifer believes that data stewards and owners have the right to determine how their information is used, and that AI systems must respect that right. Most AI systems don’t provide transparency with respect to the data they use or acknowledge their sources in response to prompts. These are troubling indicators of what’s to come.

By now we have all seen how tech innovation has impacted the music industry and journalism, completely disrupting their revenue models. So, it’s natural – no, it’s essential – to be concerned with what’s happening today in the AI sector.

Consider the white papers for AI regulation recently submitted to the U.S. government by the most prominent AI companies. Draped in the logic of national security, AI companies argue that copyright laws should evolve to ensure that AI systems have more or less free access to all content scraped off the Internet, with very little consideration to how this will affect the economic realities of content creators.

It's as if your data exists only for AI commercialization with zero respect for the voices and workers that created that information over so many generations. In contrast, we at Parsifer believe data owners should retain agency and be fairly compensated for their work.

5. Parsifer is a curious name, what’s behind it?

Parsifer is a combination of Parsing and Inference, which speaks to how the technology works and how tech people think. We parse arguments or data, say, breaking down ideas or concepts into components for analytical purposes, which is where inference comes in, the interpretative act in an evaluation process. More fun to consider is that our name was inspired by the medieval tale of Parsifal, the knight searching for the Holy Grail. Parsifal is basically the OG Knight in Shining Armor, a protector. That’s kind of how we view our purpose in serving the broader community, providing them with new solutions to protect and monetize their work; restoring trust in the system.

6.    How does Parsifer work?

The Parsifer solution, accessed using an SaaS platform, starts by analyzing a sample of your work to generate a digital, semantic “fingerprint” of its characteristics: the unique DNA of the data. We take that fingerprint and create a prompt to query an LLM or a broader system (archive, internet, website), all towards generating a second fingerprint from the response of that query. The resulting second fingerprint either matches yours or doesn’t. Think of this process as a digital equivalent of dusting a crime scene for fingerprints. Parsifer uses a four-step process to determine whether an AI system is using specific source data:

1. Source data is fed into Parsifer, from which it generates a semantic fingerprint (FP#1) featuring metadata qualities that represent the source information.

2. A prompt is then submitted to an LLM or data repository using the metadata elements from that fingerprint.

3. The response data is fed into Parsifer from which it generates a second semantic fingerprint (FP#2).

4. The two fingerprints, FP#1 and FP#2 are compared, yielding a similarity score and probability assessment — the analytics — that the specific source content has been used and to what degree.