@DataFundProject

In the digital age, freedom begins with owning personal data. Connect with us on Linkedin: https://xitter.cf/t.co/vnT2uLR57I

 Europe
datafund.io
Joined August 2017
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314Media
Data provenance is becoming a key parameter in the GenAI landscape. Lawmakers are demanding it and the cost of non-compliance is extremely high. Luckily, we've built a toolkit for building an immutable and auditable data trail. No 3rd party needed and standard-agnostic. 👇👇👇
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1/8 Could you prove what your AI agent did yesterday?Not what it told you it did. What it actually did.We're handing agents our code, our money, our customers. Staying in control starts with that answer. 🧵
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5/8 An open call. @OpenAI, you said: keep agent transcripts in write-once storage. @AnthropicAI, your CEO said: give outside evaluators real access. We agree. Now do it. Records your agents can't touch, checked by auditors, not taken on trust. RT so this reaches them.
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6/8 Agents need storage they can't rewrite. Record what the agent does, outside the agent. Store it where nobody can change it, the agent included. Let an auditor check it.
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In a recent article, WEF highlighted the growing need for strong data provenance systems as synthetic data rises. weforum.org/stories/2025/10/…
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The tools are early but the architecture makes provenance capture simple and reliable. Check out the POC demo below. We'd love your feedback, especially if you're an agent builder, researcher or practitioner.
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More info: ⚪️github.com/datafund/swarm_pr… ⚪️github.com/ethersphere/SWIPs…

GitHub - datafund/swarm_provenance_MCP: Model Context Protocol (MCP) server for AI agents to manage...

Model Context Protocol (MCP) server for AI agents to manage Swarm postage stamps and store provenance data on decentralized storage - datafund/swarm_provenance_MCP

github.com
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Data provenance is important because it tells us how the world is vs. how somebody wants us to see it. It tell us if the source was legitimate, if the capture was authentic or if the data reflected reality.
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If we cannot verify origin, authenticity or context, data becomes a liability. And as AI agents move into research, finance, science and public systems, they will act on inputs no one can trace. We need provenance captured at the source.
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This is why we built our provenance toolkit and MCP POC at Datafund. It lets agents capture and store provenance at creation and write it to a decentralised backend for public scrutiny. Try the POC and tell us what is missing.
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We're pleased to welcome @crtahlin to our Decentralized AI panel discussion! 🤩 Data flows are the pillar of the emerging AI economy. It is important to think about how AI agents can work with data and how trust can be established in decentralized AI. 👇 luma.com/8ewy3a20
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A recent paper (arxiv.org/pdf/2404.12691) highlights that a growing issue with provenance tools is their piecemeal nature. 🧩 Dev teams can’t reliably assess safety, legality or copyright across thousands of mixed datasets. Creators, on the other hand, can’t track their content.
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AI needs a provenance infrastructure that unifies ethics, law and technical aspects. That's what we're building for with our latest provenance POC. 👇👇👇
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Discussion before the discussion @web3privacy panel on cypherpunk values meeting encryption with @zeligf @ethswarm
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1️⃣ Agents are becoming the new knowledge workers, and we need to understand how they know what they know. 🧠🤖 The best AI models sit at ~50% of human baseline for agentic work. Most are even lower. But they are grinding toward human-level capability. 📈
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5️⃣ Which sources influenced its outputs and to what degree? Who has the power to influence the information it relies on? In all fields built on knowledge work, traceability of evidence = credibility. 📜✔️ This goes double for AI in high-stakes domains. 🏥💶⚖️🏛️
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6️⃣ Provenance allows us to build agentic workflows that are transparent, auditable and grounded in verifiable evidence. 🔗🛠️ That’s what we’re building with our latest provenance toolkit. 🚀 📽️ Check out the demo in the post below. 👇👇👇
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