📘The Data Provenance Gap: The Compliance Risks of Unknown Data Origins in AI

📄Executive Summary

Artificial intelligence (AI) systems rely heavily on large-scale, diverse datasets to function effectively. However, the origin, context, and integrity of this data are frequently unknown or undocumented; a condition this paper identifies as the data provenance gap (Longpre et al., 2024). This gap poses critical challenges to compliance, trust, and accountability, and the deployment of ethical AI (Stanham, 2025).

This article aims to raise awareness of the data provenance gap (Longpre et al., 2025) as a growing strategic and regulatory risk in the global AI ecosystem. It underscores how organizations are unclear about the origin of data before collecting and processing it. These organizations are facing increased exposure to data privacy and protection violations, legal and regulatory penalties, and reputational harm. The loss or lack of documentation of data origin often results from practices such as large-scale aggregation, web scraping (Truong et al., 2019), third-party acquisitions, and inadequate data governance protocols.

The consequences of poor data provenance are profound and systemic (Longpre et al., 2024). Organizations may be unable to honor data subject rights, such as deletion or access requests. They risk violating cross-border data transfer restrictions (LaCasse, 2024). They may unknowingly embed bias (Souza et al., 2019) into AI models that perpetuate discrimination. Additionally, the lack of traceability can render due diligence and accountability mechanisms (Stanham, 2025). It can render them ineffective, especially in regulated industries like healthcare, finance, and law enforcement.

This article explores the scope of this challenge and proposes governance frameworks to bridge the provenance gap. It provides:

1. A glossary of key terminology that is essential to understanding data provenance within the AI lifecycle.

   
   

2. An overview of global AI governance, data privacy, and data protection laws and regulations that seek to address data provenance-related obligations.

   
   

3. A breakdown of AI-specific scenarios where untraceable data can lead to compliance failures.

   
   

4. Case studies on sector-specific risks have shown that provenance lapses can cause significant legal and ethical harm.

   
   

5. Emerging technological solutions such as data passports (MacDonald, 2023), immutable lineage (Nguyen et al., 2019) chains, and provenance-by-design systems.

   
   

6. Strategic guidance for civil society, regulators, data controllers, data processors, and vendors on closing the provenance gap.

   
   

This analysis advocates for treating data provenance not as a back-office compliance issue but as a central pillar of responsible AI strategy. Legal, regulatory, and societal expectations around data use continue to rise. The need to address the data provenance gap becomes essential for ethical, explainable, responsible, and trustworthy AI systems.

📚Introduction

The transformative potential of AI depends on data. This data can be voluminous, diverse, and often scraped, shared, or bought from a multitude of sources. From training large language models to optimizing predictive algorithms, data is the foundational input that powers every stage of the AI lifecycle. However, as these data sets scale in size and complexity, a critical issue emerges. This issue amplifies the inability to verify the origin or collection process of specific data. This phenomenon is known as the data provenance gap (Longpre et al., 2025).

The problem is more than a technical oversight. It is a structural vulnerability with far-reaching implications. When data provenance is lost or undocumented, organizations are unable to validate consent, fulfill deletion or access rights, detect data lineage, or assess model bias (Souza et al., 2019). Such lapses not only violate global data privacy and protection laws like California’s Consumer Privacy Act (CCPA) as amended by the California Consumer Privacy Rights Act (CPRA), Brazil’s Lei Geral de Proteção de Dados (LGPD), China’s Personal Information Protection Law (PIPL), and India’s Digital Personal Data Protection Act (DPDPA), and similar laws and regulations. Additionally, they may jeopardize public trust in AI systems.

While the concept of data provenance is well-established in fields such as academic research, logistics, and supply chain management, it has not yet been systematically incorporated into all AI governance frameworks (Longpre et al., 2024). This article argues that this oversight must be addressed urgently. The central purpose of this article is to explore the origins, risks, and solutions related to this issue (Longpre et al., 2024) within the AI ecosystem. It aims to:

1. Analyze provenance challenges in high-risk sectors such as healthcare, finance, and public safety.

   
   

2. Define what data provenance means in the context of AI governance, data privacy, data protection, legal, and regulatory compliance.

   
   

3. Examine how the loss of data provenance creates ethical, legal, operational, and regulatory risks.

   
   

4. Propose governance, policy, and technology-based strategies to rebuild traceability and accountability in AI systems.

   
   

By framing provenance not as an afterthought but as a cornerstone of responsible AI, this paper seeks to shift the conversation from reactive compliance to proactive governance. To ground this analysis, the following section outlines the key terms necessary to understand how data provenance functions across data lifecycles, regulatory environments, and AI model architectures.

🧾Key Terms

To fully understand the scope and impact of the data provenance gap (Longpre et al., 2025) in AI, it is essential to establish a shared vocabulary. Figure 1 below visually summarizes key terms explored in this section:

Figure 1: Key Terms

The following terms represent foundational concepts used throughout the paper. These definitions help clarify how data flows through AI systems, the obligations tied to its origin, and the implications when such information is missing or incomplete. This glossary also provides the basis for discussing legal compliance, technical solutions, and governance strategies in later sections.

1. Data Lineage: The documented pathway data follows from its point of collection through all transformations, transfers, storage locations, and uses. Data lineage is critical for tracing accountability, understanding how models are built, and demonstrating compliance with audit requirements (Nightfall AI, n.d.; Stanham, 2025).

   
   

2. Data Passport: Technology that fully encrypts every data point in a cloud environment and infrastructure (Marr, 2020). They help mitigate the risks associated with data breaches and security incidents. They are designed to ensure continuity of provenance across systems and organizational boundaries.

   
   

3. Data Provenance: The complete historical record of a data element, including its source, how it was collected, its legal basis (such as consent or contract), and any changes made to it over time (Mucci, 2024). Provenance enables accountability, lawful reuse of data, traceability, and transparency in AI development and deployment (Freeman, 2024; Longpre et al., 2025; Longpre et al., 2024; Nightfall AI Team, n.d.; Osarenren, 2024; Stanham, 2025).

   
   

4. Embedded Data: Personal or sensitive information that becomes encoded within AI model weights or internal parameters during training (Qualtrics, n.d.). This makes such data difficult to identify, extract, or delete, raising complex legal and ethical challenges.

   
   

5. Metadata: Descriptive information that characterizes a data file or field, including details such as creation timestamp, source location, data type, and processing actions (Ford, 2025). Metadata is essential for auditing, classification, and establishing provenance.

   
   

6. Personal Data: Any data that relates to “an identified or identifiable natural person, including names, identification number, location data, an online identifier, or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of the natural person (Intersoft Consulting, 2025a). This definition is consistent with many global data privacy and data protection laws and regulations.

   
   

7. Provenance Gap: A condition in which the origin, consent status, or processing record of a dataset, or any part of it, is missing, inaccessible, or unverifiable. Provenance gaps may result from web scraping (Nguyen et al., 2019), data aggregation, third-party acquisitions, or poor documentation practices (Longpre, 2025). They often create compliance, legal, and regulatory blind spots.

   
   

These concepts serve as the groundwork for the discussion that follows. In the next section, the article examines how and why provenance becomes lost in AI systems. It also discusses the resulting consequences for governance, privacy, and trust.

🔍What is the Data Provenance Gap?

As AI systems become increasingly reliant on vast and complex datasets, a critical challenge has emerged. Organizations often cannot trace where their data comes from, under what legal basis it was collected, or how it has been processed over time. This phenomenon, referred to in this paper as the data provenance gap (Longpre et al., 2025), represents a growing blind spot. It exists at the intersection of AI governance, data privacy and protection compliance, and operational accountability (Stanham, 2025).

This gap (Longpre et al., 2025) arises when there is a loss of transparency regarding a dataset’s origin, consent status, or transformation history. This impairs an organization’s ability to:

1. Identify the source of a data point (e.g., individual, website, device).

   
   

2. Confirm the legal basis for its collection and subsequent processing.

   
   

3. Demonstrate valid and granular consent (when required by law).

   
   

4. Verify the data’s accuracy, relevance, and currency.

   
   

5. Document the chain of custody or modifications over time.

   
   

In AI training pipelines, this gap may form at multiple stages due to both technical and organizational weaknesses. Common causes include:

1. Loss of metadata (IBM, n.d.) during data aggregation, formatting, or preprocessing, where fields related to consent, source, or jurisdiction are stripped or overwritten.

   
   

2. Third-party data acquisition, especially from brokers or open datasets, often lacks documentation about consent and collection context.

   
   

3. Web scraping of publicly available content at scale, without implementing adequate systems to record permissions, terms of use, or website-specific restrictions.

   
   

4. Organizational changes, such as mergers or acquisitions, often involve the absorption of legacy systems and historical data without accompanying provenance records or processing logs.

   
   

The consequences of this gap are not theoretical; they affect real-world compliance, model accuracy, ethical integrity, and business risk. AI systems trained on data of unknown origin may embed bias, expose companies to data privacy and protection violations, and erode public trust (Souza et al., 2019).

Moreover, data provenance gaps make it difficult, if not impossible, for organizations to comply with emerging legal obligations around data minimization, accountability (Stanham, 2025), data subject rights, and algorithmic transparency. Global data privacy and protection frameworks are quickly evolving, and AI-specific regulations are coming into force. Consequently, this blind spot is quickly becoming a high-priority concern for both regulators and regulated entities.

The following section explores how this gap directly intersects with statutory and regulatory frameworks worldwide. It highlights the operational, financial, and reputational risks of failing to trace the origin and processing history of data.

⚖️Legal and Compliance Risks

As AI systems increasingly process personal and sensitive data, the lack of data provenance creates significant legal, regulatory, and contractual vulnerabilities. Legal and regulatory environments are demanding greater transparency and accountability (Stanham, 2025). They also demand lawfulness in data processing. Those organizations that cannot trace the origin or legal basis of their data may be exposed to enforcement actions, civil litigation, and reputational damage. This section outlines how this issue intersects existing data protection and AI-specific laws worldwide. These risks are grouped alphabetically for clarity and global relevance:

1.   AI Governance Laws and Sector Regulations:

• Emerging AI-specific legal frameworks, including the EU Artificial Intelligence Act (EU AI Act), China’s Administrative Measures on Generative AI Services, India’s Digital India Act (Chin, 2025), Japan, Singapore, and South Korea, require detailed documentation of datasets used to train high-risk or foundational AI systems.

• Providers must demonstrate how data was collected, what categories it includes, and under what conditions it was obtained. This is true particularly for systems used in employment, healthcare, education, and law enforcement.

• Without reliable provenance, organizations may be unable to meet documentation, auditability, or transparency obligations embedded in these laws.

  
  

2.   Cross-Border Data Transfers and Localization Requirements: Many jurisdictions enforce strict rules regarding international data transfers, such as:

• China’s PIPL enforces localization and transfer impact assessments.

• EU’s GDPR requires adequacy or appropriate safeguards for transfers outside the EEA.

• India’s DPDPA enables future notification-based localization frameworks.

• Russia’s Federal Law on Personal Data requires initial collection and storage of personal data on servers located within Russian territory (DLA Piper, 2025).

If data provenance is lost, organizations cannot determine where data was collected or whether it is subject to localization laws. Furthermore, it makes lawful transfers impossible and risks unauthorized or extraterritorial processing.

3.   Documentation and Accountability Requirements: Several data privacy and protection laws and regulations around the world mandate that data controllers and data processors maintain records of data collection, purpose, legal basis, and processing history. Key frameworks include:

• Australia Privacy Act (undergoing reform)

• Brazil LGPD

• California’s CCPA and CPRA

• China’s PIPL

• EU GDPR

• India’s DPDPA

• Kingdom of Saudi Arabia’s Personal Data Protection Law

• South Africa’s Protection of Personal Information Act (POPIA)

• UK GDPR, UK DPA, and UK Data Access and Use Act

Under these laws and regulations:

• If a data subject issues an access or deletion request, and the organization cannot identify the source or confirm consent, it may be found noncompliant.

• Organizations must demonstrate lawful processing through documented data lifecycles.

  
  

4.   Individual Rights and Remedies: Global data privacy and protection laws and regulations increasingly empower individuals with rights to access, delete, correct, or restrict processing of their data.

Suppose these gaps prevent organizations from isolating or verifying data related to a specific individual. In that case, they risk infringing on these rights and may face legal and regulatory fines or civil claims. The legal and compliance landscape is becoming more stringent, with regulatory agencies emphasizing data traceability and explainability in the age of AI. Data provenance is no longer optional. It is foundational to demonstrate lawful data practices, especially for systems that impact rights, freedoms, and safety.

To better illustrate how provenance obligations differ globally,

Table 1 compares key jurisdictions by their legal frameworks, requirements, and enforcement posture:

Table 1: Global Data Provenance Obligations (Jurisdictional Comparison)

Jurisdiction	Law(s) & Status	Data Provenance Obligations	Enforcement Notes
🇧🇷 Brazil	LGPD (in force)	Emphasizes purpose limitation, lawful basis, and individual data rights	National ANPD enforces through administrative action
🇨🇳 China	PIPL (in force)	Mandates localization, documented consent, cross-border transfer impact assessments	Long-arm scope; strict penalties for violations
🇪🇺 European Union	GDPR (in force)	Requires origin documentation, legal basis, consent tracking, and full traceability	Fines up to 4% global turnover; strict compliance oversight
🇮🇳 India	DPDPA 2023 (awaiting enforcement)	Requires consent documentation, notice, access, and erasure compliance	Implementation rules pending
🇷🇺 Russia	Federal Data Law (in force)	Enforces local storage, data origin control, and restrictions on cross-border transfers	Strong localization framework
🇿🇦 South Africa	POPIA (in force)	Requires recordkeeping, security safeguards, and collection purpose transparency	Enforced by the Information Regulator
🇸🇬 Singapore	PDPA (with 2020 amendments)	Requires breach notification, purpose limitation, and personal data lifecycle tracking	Personal Data Protection Commission enforces
🇺🇸 United States – California	CCPA/CPRA (in force)	Implies provenance to fulfill opt-out, access, and deletion rights; sensitive data tracing	Civil actions + CPPA oversight

Source Note: Compiled from current versions of national data protection and AI governance frameworks, including:

1. EU GDPR (gdpr-info.eu)

2. China PIPL (NPC Observer)

3. India DPDPA 2023 (meity.gov.in)

4. Brazil LGPD (anpd.gov.br)

5. South Africa POPIA (inforegulator.org.za)

6. Singapore PDPA (pdpc.gov.sg)

7. Russia Federal Law 152-FZ (consultant.ru)

8. California CCPA/CPRA (oag.ca.gov/privacy, cppa.ca.gov)

These variations underscore the importance of embedding traceability tools and compliance protocols early in the AI development lifecycle, especially for systems deployed across borders.

🧠AI-Specific Challenges

Even with legal and regulatory frameworks in place, AI introduces distinct technical and operational complexities that compound the risks associated with the data provenance gap (Longpre et al., 2025). Unlike conventional data processing, AI systems transform data into statistical patterns, vectors, and weights. They often render the original inputs invisible or inseparable from the model architecture. This black-box nature of AI creates significant challenges in complying with provenance-related legal and ethical obligations. This section explores key AI-specific risks that arise when the origins of training data are uncertain, lost, or unverifiable.

1.   Bias Amplification: When datasets lack documented provenance, organizations may be unaware of embedded demographic imbalances, geographic skews, or outdated information. This can lead to:

• Challenges in bias (Souza et al., 2019) mitigation arise because auditing tools often rely on knowing the data source or context to assess fairness.

• Model outputs that disadvantage protected groups, especially in hiring, lending, or healthcare contexts.

• Reinforcement of historical bias (Souza et al., 2019), such as racial, gender, or socioeconomic disparities.

  
  

2. Embedded Data: AI models trained on personal or sensitive data may “memorize” portions of that data, embedding it directly into the model weights. This presents serious risks:

• Data subjects’ information may reappear in outputs or be reconstructed through inference attacks.

• Model retraining may be required, at significant technical and financial cost, to remove data that should never have been included.

• EU GDPR’s Article 17, “Right to Erasure (‘Right to be Forgotten’) (Intersoft Consulting, 2025b), becomes impractical if provenance is lost, as the controller cannot identify which data points were used or where they reside within the model.

3. Model Accountability and Explainability

• AI governance frameworks increasingly demand transparency around how a model was developed, what data was used, and whether lawful collection and processing occurred.

• Courts, regulators, or oversight bodies may request evidence of responsible sourcing during litigation or audits.

• EU AI Act (Future of Life Institute, 2025) and the Organization for Economic Cooperation and Development (OECD) AI Principles (OECD, 2024) require documentation of data inputs for high-risk systems.

• Without data provenance, organizations may be unable to demonstrate due diligence, legal compliance, or ethical safety. This oversight could potentially result in product bans, fines, or loss of public trust.

  
  

These technical realities make it clear: solving the provenance gap is not just a data governance issue. It is a prerequisite for safe, lawful, and sustainable AI. As AI systems are deployed in increasingly sensitive environments, the risks tied to untraceable data are magnified. The following section examines how the gap (Longpre et al., 2025) creates unique vulnerabilities in domains like healthcare, finance, and law enforcement, where errors, bias (Souza et al., 2019), or violations can lead to severe consequences for individuals and institutions alike.

🚨High-Risk Sectors

The problem (Longpre et al., 2025) is not a uniform risk. It becomes acutely dangerous when AI systems are deployed in domains that involve fundamental rights, safety, or economic well-being. In these high-risk sectors, the inability to trace data origin can lead to discrimination, legal violations, regulatory enforcement, and irreparable harm to individuals.

This section outlines how the gap creates unique vulnerabilities in specific regulated environments. These examples illustrate why addressing provenance is not only a matter of compliance but also one of public interest, safety, and institutional integrity:

1.   Financial AI: In the financial sector, AI tools are widely used for credit scoring, fraud detection, risk modeling, and transaction monitoring. However, when these tools rely on datasets of unclear or unverifiable origin, several risks arise:

• Audit failures or regulatory scrutiny can result in financial institutions being required to demonstrate that decisions are fair, explainable, and based on lawful data.

• Non-compliance with financial privacy laws, such as the EU’s Payment Service Directive 2 (Adyen, 2023), the U.S. Gramm-Leach-Bliley Act (American Bankers Association, 2025), or national banking regulations.

• Use of prohibited or discriminatory data, such as race or ZIP code proxies, is especially problematic when the provenance of training data is unknown.

  
  

2.   Healthcare AI: Healthcare AI systems may include diagnostic tools, patient triage models, and predictive analytics. They often rely on sensitive personal health data, such as protected health information or electronic protected health information as defined by the Health Insurance Portability and Accountability Act (HIPAA) of 1996, as amended. Data provenance gaps in this context can have the following life-threatening consequences:

·         Breach of consent laws, such as HIPAA, the EU GDPR, or South Africa's POPIA, if patient data was used without valid consent or anonymization.

·         Erroneous diagnoses or treatment plans, especially if training data comes from non-representative or undocumented sources.

·         Litigation or regulatory penalties, particularly if AI-driven decisions contribute to medical errors or health inequities.

3.    Law Enforcement and Public Safety AI: Law enforcement agencies are increasingly adopting AI for predictive policing, surveillance, facial recognition, and forensic analysis. The absence of provenance in such systems can directly violate civil liberties:

• Inaccurate or biased facial recognition outputs disproportionately affect communities of color or marginalized populations (Souza et al., 2019).

• Legal challenges against police departments or vendors for deploying unaccountable, opaque AI systems.

• Use of scraped biometric data without consent, which may breach constitutional protections, such as the U.S. Fourth Amendment (United States Courts, 2025), or individual privacy rights protected by the Charter of Fundamental Rights of the EU’s Articles 7 and 8 (European Union, 2012).

  
  

The data provenance gap does not affect all industries equally. Its consequences are particularly acute in regulated, high-impact sectors where AI systems influence rights, health, safety, and financial well-being. In these domains, poor traceability of data origin can amplify bias, lead to unlawful processing, and expose organizations to severe legal, operational, and reputational risks.

Table 2 below maps key AI use cases, provenance-related risks, and associated compliance threats across selected sectors. It is designed to help stakeholders understand and prioritize sector-specific vulnerabilities where data provenance lapses can be most damaging.

Table 2: Sectoral Impact of the Data Provenance Gap on AI Systems

Sector	AI Use Cases	Provenance Risks	Compliance Threats
Education	Student analytics, adaptive learning systems	Profiling, consent gaps in minors’ data	GDPR, DPDPA, and potential discrimination claims
Employment	Resume screening, hiring algorithms	Discrimination based on race/gender proxies, undocumented model inputs	GDPR, CCPA/CPRA litigation or enforcement
Finance	Credit scoring, fraud detection, AML/KYC	Undetected bias, illegal use of protected attributes, and audit failure	GLBA, GDPR, PSD2; regulatory audits and fines
Healthcare	Diagnostic tools, triage models, analytics	Use of data without consent, misdiagnoses, and privacy breaches	GDPR, HIPAA, POPIA violations; patient harm liability
Law Enforcement	Predictive policing, facial recognition, surveillance	Racial bias, rights violations, and the use of unverified biometric data	Constitutional challenges, EU Charter Articles 7 & 8, US 4th Amendment issues

Source Note: This matrix is adapted from the article’s original analysis of high-risk sectors and aligns with global legal frameworks referenced in the text. Legal references and provenance-related risks are based on:

1. EU General Data Protection Regulation (GDPR) – gdpr-info.eu

2. California Consumer Privacy Act / Rights Act (CCPA/CPRA) – cppa.ca.gov

3. Brazil’s Lei Geral de Proteção de Dados (LGPD) – anpd.gov.br

4. China’s Personal Information Protection Law (PIPL) – npcobserver.com

5. India’s Digital Personal Data Protection Act (DPDPA 2023) – meity.gov.in

6. South Africa’s Protection of Personal Information Act (POPIA) – inforegulator.org.za

7. U.S. Health Insurance Portability and Accountability Act (HIPAA) – hhs.gov/hipaa

8. EU Charter of Fundamental Rights, Articles 7 & 8 – eur-lex.europa.eu

All references are consistent with those cited in The Data Provenance Gap: The Compliance Risks of Unknown Data Origins in AI (2025) and supporting academic and regulatory sources included therein.

These sector-specific risks underscore a critical takeaway: the data provenance gap is not a peripheral concern. It is central to ethical, lawful, and accountable AI deployment. Addressing this gap requires more than awareness; it demands action. The following section outlines concrete governance strategies that developers, organizations, and regulators can implement to restore traceability, reduce compliance risk, and reinforce public trust in AI systems.

🛠️Closing the Gap: Governance Solutions

Addressing the gap (Longpre et al., 2025) is both a technical and organizational challenge, but it is solvable. Effective governance strategies can restore traceability, reduce compliance risk, and enhance public confidence in AI systems. Rather than relying on ad hoc documentation or reactive audits, organizations should adopt structured, proactive mechanisms that embed provenance tracking into the AI lifecycle by design. This section presents a set of governance solutions that can be tailored to different sectors, regulatory environments, and technical architectures. These solutions are listed alphabetically to emphasize their modular nature and applicability across jurisdictions and system types:

1.   Automated Data Provenance Checks: Automated tools can be integrated into data ingestion pipelines to detect missing, incomplete, or suspicious provenance information:

• Enforce validation protocols at the point of collecting or importing.

• Flag incomplete lineage, such as missing consent records or unclear data sources.

• Trigger remediation or exclusion protocols for non-compliant data points.

These tools act as gatekeepers and reduce reliance on manual audits, especially on a large scale.

2.   Immutable Data Lineage Tracking: Using blockchain, cryptographic signatures, or secure logs, organizations can create tamper-evident records of:

• Custody transfers (e.g., between vendors or departments).

• Data origin (e.g., source system, individual, jurisdiction),

• Processing actions (e.g., transformations, filtering, enrichment),

This approach creates verifiable, unalterable chains of custody that are particularly valuable in high-risk or highly regulated environments.

3.   International Standards Alignment: Global interoperability requires shared frameworks. Organizations should support and implement efforts like:

• Cross-border harmonization, especially for multinationals handling data under multiple legal regimes (Longpre et al., 2024).

• International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 5259-1 (2024) and other international standards currently in development or use for trustworthy AI (ISO/IEC, 2024; Spoczynski et al., 2025).

• ISO/IEC 42001 (ISO, 2023), OECD AI Principles (OECD, 2024), the EU AI Act (Future of Life Institute, 2025), the NIST AI Risk Management Framework (NIST, 2023), and other frameworks offer best practices that emphasize traceability.

These standards foster consistency and reduce fragmentation in how provenance is defined and enforced.

4.   Mandatory Data Passports: Data passports are structured with metadata (IBM, n.d.) records that travel with a dataset throughout its lifecycle. They include fields for:

• Consent and legal basis,

• Jurisdiction and processing history,

• Source and collection methods.

Mandatory adoption of data passports (Monte Carlo Data, 2023) ensures that data provenance is preserved across systems, vendors, and borders. This is especially critical within complex AI supply chains.

5.   Supplier and Vendor Audits: Given the frequency of third-party data integration, organizations must demand:

• Audit logs or certification mechanisms confirm adherence to AI governance, data privacy, and data protection compliance requirements.

• Evidence of user consent or contractual authority.

• Proof of lawful data sourcing.

  
  

Vendor management frameworks should include clear contractual obligations and escalation procedures if provenance cannot be demonstrated. These governance solutions can be implemented individually or combined into a comprehensive provenance assurance strategy. What matters most is that organizations move from reactive risk management to proactive, traceable, and auditable AI governance.

Addressing this critical issue requires more than ad hoc fixes or reactive audits. Organizations need structured, proactive governance approaches that embed provenance into every stage of the AI lifecycle. Figure 2 provides practical tools for regulators, data controllers, processors, and vendors to assess readiness and operationalize provenance-by-design strategies.

Figure 2: Checklist for Closing the Data Provenance Gap

Source Note: Adapted from The Data Provenance Gap: The Compliance Risks of Unknown Data Origins in AI (2025), with references to ISO/IEC 5259-1:2024, ISO/IEC 42001:2023, OECD AI Principles, and the NIST AI Risk Management Framework (2023).

These governance tools and workflows underscore that closing the gap is not only a technical necessity but also a compliance imperative. However, establishing provenance safeguards is just one part of the broader framework for trustworthy AI. The following section explores key takeaways and cross-sector implications, highlighting how organizations, regulators, and vendors can operationalize these measures to ensure AI systems remain lawful, ethical, and resilient.

📌Key Takeaways

The data provenance gap (Longpre et al., 2025) is not a theoretical or future risk. It is an urgent and present governance challenge. As this paper has shown, provenance issues lie at the heart of many legal, ethical, and operational concerns in AI deployment.

Organizations are developing models in-house, sourcing data externally, or deploying third-party systems. The following key takeaways synthesize the paper’s core insights and highlight what every stakeholder should know. Policymakers, technologists, auditors, or executives should prioritize closing the gap and mitigating risk. These are listed alphabetically for clarity and emphasis:

1.   AI Amplifies Provenance Complexity: The scale and heterogeneity of data required to train modern AI systems make provenance harder to track. This occurs mainly when data flows across jurisdictions, vendors, and preprocessing stages. This amplifies the risk of untraceable data entering mission-critical systems without oversight (Longpre et al., 2024).

2.   Compliance Requires Data Provenance: Laws and regulations such as Brazil’s LGPD, China’s PIPL, the EU AI Act, the EU GDPR, and India’s DPDPA, explicitly require accountability, transparency, and a demonstrable legal basis for data processing. Without clear provenance, organizations cannot meet requirements like deletion, access, lawful basis, or impact assessments.

3.   Governance Tools Are Available: A range of governance mechanisms already exist to mitigate provenance risks. They include automated verification tools, cryptographic lineage tracking, data passports (Monte Carlo Data, 2023), and third-party audits. Adoption of these tools can reduce exposure and demonstrate proactive compliance.

4.   Proactive Measures Are More Cost-Effective: Organizations that invest in provenance safeguards now are likely to face lower remediation costs, stronger reputational resilience, and higher regulatory trustworthiness. They should be proactive and not wait for enforcement or litigation to occur.

5.   Provenance Gaps Are Multi-Dimensional Risks: Beyond legal exposure, provenance gaps undermine operational reliability, ethical integrity, and stakeholder confidence. In sectors such as healthcare, finance, and law enforcement, poor provenance can translate directly into discrimination, harm, or rights violations.

6.   Sector-Specific Vulnerabilities Must Be Prioritized: AI systems deployed in sensitive or regulated industries face the most acute consequences of provenance failure. Mitigating these risks requires targeted governance approaches that account for sectoral norms, liabilities, and regulatory expectations.

Figure 3 provides a visual summary of the key takeaways from this analysis. It highlights six critical insights into closing the data provenance gap, from the growing complexity introduced by AI to sector-specific vulnerabilities that must be prioritized.

Figure 3: Key Takeaways: Closing the Data Provenance Gap

Source Note: Adapted from The Data Provenance Gap: The Compliance Risks of Unknown Data Origins in AI (2025), with references to ISO/IEC 5259-1:2024, ISO/IEC 42001:2023, OECD AI Principles, and the NIST AI Risk Management Framework (2023).

These key takeaways highlight that closing the data provenance gap is both a compliance requirement and a strategic necessity. The implications extend beyond individual organizations to regulators, vendors, and entire sectors. Building accountability, trust, and resilience in AI depends on how effectively stakeholders implement these safeguards. The Conclusion reflects on the broader significance of these findings and outlines why addressing the provenance gap is essential for the future of lawful, ethical, and trustworthy AI.

🧭Conclusion

The gap (Longpre et al., 2025) is more than a regulatory concern or technical oversight. It is one of the defining challenges of the AI era. AI will continue to become deeply embedded in the decisions that shape health, finance, mobility, security, and opportunity. The stakes of building trustworthy systems are escalating. Data provenance is the invisible thread that holds the integrity of these systems together. Individuals and organizations must know where data comes from, how it was obtained, and whether it was lawfully and ethically processed.

This article has shown that when provenance is lost, so too is the ability to ensure fairness, accountability (Stanham, 2025), and legality. The consequences extend beyond fines or compliance audits. They include algorithmic discrimination, systemic bias (Souza et al., 2019), and public backlash that can derail entire technologies or sectors. When the origin of the data is unknown, trust is unearned. We must remember that trust is the currency on which the AI economy will rise or fall.

However, the tools, standards, and governance models to solve this problem already exist. What is missing is not feasibility, but urgency, leadership, and commitment. Organizations that embed data provenance into their data pipelines, contractual frameworks, and AI development processes today are not just hedging against risk. They are investing in resilience, transparency, and long-term viability.

The future of AI will be shaped not only by what systems can do, but by whether they can be trusted to do it lawfully, ethically, and reliably. The courts will continue to sharpen their scrutiny, regulators will harden their rules, and society will raise its expectations. Data provenance will no longer be a niche issue; it will be a frontline requirement.

This is the moment to act, not because regulation demands it, but because responsibility requires it. This gap is solvable. The question is whether we will close it before trust, rights, and accountability become the collateral damage of inaction (Stanham, 2025). Resolving this issue requires more than technical solutions (Longpre et al., 2025). It demands a cultural and structural shift across the entire AI ecosystem. Regulators, organizations, technology vendors, civil society, and consumers must all rethink their roles. They must ensure that data-driven algorithmic decisions are lawful, traceable, and trustworthy.

❓Key Questions for Stakeholders

The following questions are designed to challenge assumptions, provoke institutional introspection, and guide future action. Organized by category, these questions are intended to challenge assumptions, guide compliance strategies, and prompt coordinated action across civil society, organizations, regulators, and technology vendors. To move from analysis to action, each stakeholder group must reflect on its role in addressing the data provenance gap.

Table 3 synthesizes guiding questions designed to challenge assumptions, provoke discussion, and support the development of effective governance strategies. These questions are not prescriptive. They serve as a framework for reflection, collaboration, and accountability across civil society, organizations, regulators, and technology vendors.

Table 3: Key Questions for Stakeholders

Stakeholder	Guiding Questions
Civil Society & Consumers	1.    Should individuals have a right not only to access their data but also to trace its origin, transformation, and use? 2.    How can public pressure and digital literacy drive greater transparency around AI data sourcing? 3.    What accountability mechanisms should exist when organizations cannot demonstrate lawful or ethical data collection?
Organizations (Controllers & Processors)	1.    Do we maintain complete and current data lineage records for all datasets used in model development, training, and deployment? 2.    How do we evaluate the legal basis and ethical acceptability of datasets acquired from brokers, aggregators, or open repositories? 3.    What internal protocols and technical systems ensure that data provenance is preserved from ingestion through AI lifecycle stages? 4.    Are our compliance, data science, and procurement teams aligned on provenance expectations and responsibilities?
Regulators & Policymakers	1.    Should data provenance documentation be a prerequisite for the certification, registration, or deployment of high-risk AI systems? 2.    How can regulatory frameworks ensure that provenance is preserved across complex data transactions, including mergers, acquisitions, or platform transitions? 3.    What level of granularity and duration should provenance records be required to meet (e.g., data subject-level vs. dataset-level)? 4.    What proportionate penalties should apply when an organization is found to have deployed AI systems trained on unverifiable or unlawfully sourced data?
Technology Vendors & Developers	1.    Can our data infrastructure automatically capture and update metadata (e.g., source, consent, jurisdiction) without degrading performance or scalability? 2.    How can we modularize AI pipelines to enable the substitution or isolation of questionable datasets without retraining entire models? 3.    Are our tools equipped to generate immutable audit trails or digital signatures for provenance verification at scale? 4.    What role should we play in creating industry standards for provenance-by-design systems and tools?

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