Data Provenance

According to IBM, data provenance is “the historical record of data that details data’s origins by capturing its metadata as it moves through various processes and transformations.”⁠1 Provenance safeguards are broadly relevant across AI systems, since any model or system built on unverified or manipulated data risks producing harmful outputs. For LLMs and RAG systems in particular, trust filters and content scoring help ensure that only vetted documents are used for generation, reducing the risk of prompt poisoning from public content. High retrieval quality is also essential for ensuring satisfactory and reliable outputs.

AI startup Clean Lab notes that data origin tracking typically begins at the point of data ingestion, where information is collected from various sources, such as databases, APIs, or external systems.⁠2 Multiple signals can be used to score the trustworthiness of sources and documents, including

  • classical retrieval metrics (e.g., normalized discounted cumulative gain, fidelity)
  • authoritative metrics (e.g., citation count, institutional affiliation)
  • bias or semantic signals (e.g., contextual fit, topical match).

Computer Forensics Lab, a United Kingdom–based digital forensics firm, recommends several methods for validating the authenticity and integrity of digital documents.⁠3 These include

  • examination of document metadata
  • authorship attribution and origin
  • digital signatures
  • document hashing
  • certificates and public keys
  • document watermarks
  • digital forensic analysis software
  • forgery detection
  • content analysis.

According to AI statistical software firm Number Analytics, verification involves confirming the authenticity and integrity of digital evidence.⁠4 Number Analytics suggests using hash values to verify data integrity, validating data through multiple sources, and using digital signatures for authentication.

Commercial Off-the-Shelf Solutions and Simple Recommendations

  • Cleanlab’s Trustworthy Language Model with integration into Pinecone⁠5
  • Amazon Bedrock⁠6
  • Microsoft Azure RAG evaluators⁠7
  • CycloneDX Machine Learning Bill of Materials⁠8
  • CamFLow⁠9
  • Linux Provenance Modules⁠10

How Does This Relate to the Rest of the Guide or Other Threats That the User Cares About?

  • Data Origin Tracking: D.DQIA-1, IE.MLCM-1
  • BIML: Model Manipulation
  • OWASP:
    • LLM01: Prompt Injection
    • LLM03: Training Data Poisoning
    • LLM03:2023: Inadequate Sandboxing
    • ML02: Data Poisoning Attack

Other Sources of Information About This Topic

  • “Data Origin Tracking” (Alooba)⁠11
  • “Building Trust in LLM Answers” (Medium)⁠12
  • “What Is Data Provenance?” (IBM)⁠13
  • “Trust Issues?” (AICompetence.org)⁠14
  • “Document Verification” (Cellbunq)⁠15
  • “Digital Document Forensics — How to Tell a Digital Document Is Genuine?” (Computer Forensics Lab)⁠16
  • “Verification in Digital Forensics” (Number Analytics)⁠17
  • “How to Evaluate Retrieval Augmented Generation (RAG) Systems” (Medium)⁠18
  • “When Documents Deceive” (Journal of the American Society for Information Science and Technology)⁠19
  • “TrustDataFilter” (arXiv)⁠20
  • “Evidence‑Backed Fact Checking Using RAG and Few‑Shot In‑Context Learning with LLMs” (arXiv)⁠21
  • “Exploring Trust and Transparency in Retrieval‑Augmented Generation for Domain Experts” (CHI EA ’25: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems)⁠22
  • “ClaimTrust” (arXiv)⁠23

Notes

  1. Mucci, “What Is Data Provenance.” Return to content ⤴
  2. Cleanlab, “Datasets.” Return to content ⤴
  3. Naghdi, “Digital Document Forensics — How to Tell a Digital Document Is Genuine.” Return to content ⤴
  4. Lee, “Verification in Digital Forensics.” Return to content ⤴
  5. Cleanlab, “Trustworthy Language Model (TLM) — Quickstart”; Turk, “Building a Reliable, Curated, and Accurate RAG System with Cleanlab and Pinecone.” Return to content ⤴
  6. Kandakji and Catana, “Evaluate the Reliability of Retrieval‑Augmented Generation Applications Using Amazon Bedrock.” Return to content ⤴
  7. Microsoft, “Retrieval‑Augmented Generation (RAG) Evaluators.” Return to content ⤴
  8. CycloneDX, “Machine Learning Bill of Materials (ML‑BOM).” Return to content ⤴
  9. Pasquier et al., “Information Flow Audit for PaaS Clouds.” Return to content ⤴
  10. Bates et al., “Trustworthy Whole‑System Provenance for the Linux Kernel.” Return to content ⤴
  11. Alooba, “Data Origin Tracking.” Return to content ⤴
  12. SO Development, “Building Trust in LLM Answers.” Return to content ⤴
  13. Mucci, “What Is Data Provenance.” Return to content ⤴
  14. RoX818, “Trust Issues?.” Return to content ⤴
  15. Boyd, “Document Verification.” Return to content ⤴
  16. Naghdi, “Digital Document Forensics — How to Tell a Digital Document Is Genuine.” Return to content ⤴
  17. Lee, “Verification in Digital Forensics.” Return to content ⤴
  18. RidgeRun.ai, “How to Evaluate Retrieval Augmented Generation (RAG) Systems.” Return to content ⤴
  19. Lynch, “When Documents Deceive.” Return to content ⤴
  20. Zhang et al., “TrustDataFilter.” Return to content ⤴
  21. Singhal et al., “Evidence‑Backed Fact Checking Using RAG and Few‑Shot In‑Context Learning with LLMs.” Return to content ⤴
  22. Ravi and Sindhgatta, “Exploring Trust and Transparency in Retrieval‑Augmented Generation for Domain Experts.” Return to content ⤴
  23. Qian, Li, and Wang, “ClaimTrust.” Return to content ⤴