Postmortem Data Management Principles

• Postmortem Data Management principles are rigorous, multidimensional frameworks that define responsible handling of digital data and metadata after stakeholder inactivity.
• They integrate metadata and provenance tracking to support reproducibility, auditing, and secure archival in scientific and AI contexts.
• They address legal and ethical challenges through mechanisms like deletion rights, data inheritance, and harm prevention safeguards.

Postmortem data management principles define rigorous, multidimensional frameworks for handling digital data and metadata created by individuals and organizations after the original stakeholders are deceased or inactive. These principles are especially crucial in contemporary settings involving generative AI, scientific archives, and digital legacies, where vast, heterogeneous datasets persist across platforms and over extended timescales. Postmortem management encompasses data ownership, provenance, deletion, inheritance, purposeful reuse, and mitigation of downstream risks, requiring harmonization among technical, legal, and ethical domains.

1. Core Definitions: Metadata, Provenance, and Postmortem Data

Metadata is formally described as structured information about data, encompassing both logical (semantic) and physical (operational) attributes. In scientific applications, metadata records contain unique identifiers, formats, timestamps, versions, quality metrics, and can be organized hierarchically—primary metadata refers to raw observations while secondary metadata captures transformation or summary details (Deelman et al., 2010).

Provenance refers to the detailed history of derivation for data products, systematically documenting all inputs, transformations, algorithms, parameters, environmental factors, and modifications along the lifecycle. Provenance supports reproducibility, auditability, and quality control. Graph-based representations, typically directed acyclic graphs (DAGs), map the relationships between data artifacts and processes, annotated with causal mechanisms (e.g., "wasGeneratedBy", "used").

Postmortem data constitutes all digital artifacts—data, metadata, and process documentation—persisting beyond the active involvement or lifetime of original data subjects. Its management concerns legal, technical, and ethical stewardship in various contexts, ranging from AI model training datasets to scientific archives.

2. Lifecycle Integration and Contextual Importance

Postmortem data management is situated within a comprehensive data lifecycle consisting of discovery, selection, processing, dissemination, and archiving (Deelman et al., 2010). Both metadata and provenance underpin each phase:

• Discovery: Metadata catalogs facilitate efficient indexing and retrieval.
• Selection/Analysis: Provenance supports traceability when choosing analytical modules or datasets.
• Processing: Automated provenance capture systems log transformations and parameter changes.
• Archiving/Replica Management: Metadata and provenance jointly support distributed storage, replication, and downstream forensic analysis.

Once a data product reaches end-of-life (postmortem), the integrity of scientific or digital legacy requires that provenance graphs, metadata, and process documentation stay accessible and interpretable. This enables retrospective auditing (“which inputs, processes, and software versions led to this result?”) and ensures reproducibility, troubleshooting, or safe reprocessing.

3. Postmortem Data Rights: Principles in the Generative AI Era

Contemporary research identifies three foundational principles for postmortem data in applications involving large-scale AI, foundation models, and agentic systems (Jarin et al., 9 Sep 2025):

1. Right to Be Forgotten / Deletion: Deceased individuals (or their designated legacy contacts) must have the ability to request permanent removal of their data from all storage locations, caches, and retrieval indices. In the AI context, data deletion also requires model unlearning—modifying the AI model such that its outputs are statistically indistinguishable from a model trained without the deceased’s data:

$$\forall Q,\; M_{\text{original}}(Q) - M_{\text{unlearned}}(Q) \approx 0$$

This ensures that downstream model behaviors do not continue to reflect information derived from deleted individuals.

1. Data Inheritance and Ownership: Postmortem data can become a digital legacy. Data ownership should be transferrable to heirs via secure channels and possibly monetized without exposure of raw data. Mechanisms may involve digital wills implemented using attribute-based encryption, controlling fine-grained access or economic rights.
2. Purpose Limits and Harm Prevention: Where users have consented (in life) to donate postmortem data for research or other uses, explicit agreements must limit data usage to well-defined purposes. Safeguards—including privacy-by-design, data minimization, differential privacy

$$\Pr[M(D) \in S] \leq e^{\varepsilon} \Pr[M(D') \in S] + \delta$$

(for neighboring datasets $D, D'$), as well as watermarking and canary injection—protect dignity and mitigate risk of harm to descendants.

These principles address gaps in current laws (GDPR, CCPA, LGPD), which provide limited or no explicit protections for deceased persons’ data.

4. Management Approaches and Operationalization

Management of postmortem data leverages established metadata and provenance frameworks from scientific and digital environments (Deelman et al., 2010), alongside emerging technical protocols for AI data deletion and inheritance (Jarin et al., 9 Sep 2025).

• Metadata Catalogs: Store logical and operational attributes, supporting extensibility for domain-specific schemas. Technologies include relational databases (PostgreSQL, MySQL), XML databases, RDF triple stores with ontologies (RDF Schema, OWL).
• Provenance Tracking: Automatic capture within workflow management systems (e.g., Pegasus, VisTrails, PASOA), utilizing DAGs to model data-process dependencies. Open Provenance Model (OPM) standardizes inter-system interoperability.
• Integration with Digital Platforms: Solutions capable of interfacing with major technology platforms enable enforcement of user-defined postmortem wishes (deletion, inheritance, selective sharing) and execution of data-related digital wills.
• Auditing and Verification: Regular independent audits, using frameworks such as MUSE, validate the efficacy of deletion or inheritance protocols—an essential requirement for compliance with regulatory or contractual obligations.

Existing practices, such as social media “Legacy Contact” options or “Inactive Account Manager” settings, operate largely at the account level and lack comprehensive model/data-level guarantees (Reeves et al., 1 Jul 2024), suggesting a need for further technical and policy innovation.

5. Stakeholder Preferences and Trust Models

Empirical research among Australian users reveals heterogeneous preferences for posthumous data management (Reeves et al., 1 Jul 2024):

• Approximately 36.3% prefer full access transfer to a single trusted individual; 34.9% prefer that no one be given access.
• Over 90% support being able to preconfigure their posthumous data disposition via provider settings.
• Trust is greatest for close associates or self-administered third-party software solutions; social media companies are consistently rated low on both trust and convenience.

Demographic correlates—higher internet activity, parental status, greater education—strengthen desires for control, but age, gender, and marital status are not significant predictors.

Decision models for postmortem data disposition can be abstracted as:

$D = f(U, T, P)$

where $D$ is the final data disposition, $U$ user preferences, $T$ trust in the designated entity, and $P$ platform policies.

Existing third-party data management prototypes face sustainability challenges, suggesting ongoing research needs in business model development and platform interoperability.

6. Applications, Case Studies, and Sectoral Relevance

The principles of metadata, provenance, and postmortem management are deployed across critical scientific and digital domains (Deelman et al., 2010):

Domain	Data Management System	Postmortem Significance
Astronomy	FITS + Metadata Headers	Enables retrospective spatial, calibration, and provenance queries for legacy datasets
Climate Modeling	Earth System Grid + Metadata catalogs	Supports auditable dataset discovery and criteria-based retrieval after project completion
Geospatial/Oceanography	SSDS + Provenance Graphs	Provides full traceability from sensors to visualization, key for archival quality control
Neuroscience	Montage + PASOA/Pegasus	Connects derived brain atlases with originating workflows for reproducibility

In generative AI, training datasets containing the data of deceased individuals require model unlearning and active monitoring—not only deletion at the raw data level but also active verification that model parameters and outputs are purged of legacy influence (Jarin et al., 9 Sep 2025).

7. Regulatory and Technical Challenges, Future Directions

Current privacy laws and vendor practices do not robustly cover postmortem data. The recommended roadmap comprises:

• Updates to privacy regulations, explicitly addressing postmortem rights, deletion, inheritance, and transparency obligations.
• Mandates for platforms to disclose postmortem processing policies, deletion timelines, and digital legacy mechanisms.
• Widespread adoption of privacy-by-design frameworks, watermarking, differential privacy, and independent audits.
• Development of technically sound, user-centric third-party management solutions, with sustainable models and seamless integration capabilities.

A plausible implication is that future solutions must dynamically combine user choice, regulatory enforcement, platform compliance, and technical means to operationalize digital data rights beyond death, especially in the context of AI training and inference.

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Postmortem data management principles now intersect technical, legal, and ethical fields, requiring rigorous operationalization to guarantee digital legacy, privacy, and reproducibility. Their application spans not only scientific environments but also generative AI and consumer platforms, with clear demands for composable, auditable, user-controlled systems that extend data protection beyond individual lifetimes.