Chain of Custody is a chronological, auditable trail that documents the sequence of custody, control, transfer, and analysis of a digital artifact, ensuring its integrity and non-repudiation. It establishes a verifiable provenance record, proving that a specific artifact—such as a trained model, a dataset, or a software build—has not been altered, tampered with, or substituted during its lifecycle.
Glossary
Chain of Custody

What is Chain of Custody?
A chronological, auditable trail that documents the sequence of custody, control, transfer, and analysis of a digital artifact, ensuring its integrity and non-repudiation.
In AI supply chain security, this process relies on cryptographic mechanisms like code signing, transparency logs, and in-toto attestations to create an immutable record of every action performed. By linking each step to a verifiable identity and a specific artifact hash, a chain of custody assures DevSecOps engineers that a model deployed to production is the exact, untampered artifact that passed testing and validation.
Core Properties of a Digital Chain of Custody
A robust digital chain of custody is not merely a log; it is a cryptographically enforced, tamper-evident architecture that guarantees the integrity and non-repudiation of an artifact throughout its lifecycle.
Tamper-Evident Sequencing
Establishes an immutable chronological order using cryptographic hashing and Merkle tree structures. Each entry in the chain contains a hash of the previous entry, creating a mathematical guarantee that any retroactive alteration to a single record would invalidate the entire subsequent chain.
- Mechanism: SHA-256 or SHA-3 hashing algorithms
- Benefit: Instant detection of unauthorized modification
- Example: A model's weight checkpoint hash is recorded before and after a fine-tuning run, proving the exact artifact that was transferred.
Complete Metadata Capture
Documents the full context of an event, not just the action. This includes environmental fingerprints, tooling versions, and dependency graphs to satisfy the requirements of frameworks like SLSA and in-toto.
- Captured Data: Timestamps, geolocation, build system parameters, SBOM
- Standard: W3C PROV model for provenance data
- Example: A model card is automatically attached to an artifact, recording the exact Docker image digest and hyperparameters used during training.
Frequently Asked Questions
Essential questions about establishing and maintaining a verifiable chain of custody for AI model artifacts, training data, and deployment pipelines.
A chain of custody in AI supply chain security is a chronological, auditable trail that documents the sequence of custody, control, transfer, and analysis of a digital artifact—such as a model checkpoint, training dataset, or container image—ensuring its integrity and non-repudiation throughout the machine learning lifecycle. This record cryptographically proves that an artifact has not been tampered with from its point of origin to its current state. In the context of AI pipelines, the chain of custody captures every transformation event: who trained the model, which dataset version was used, what hyperparameters were applied, who validated the output, and who authorized deployment. Each custody transfer is timestamped and signed, creating an immutable provenance trail. This is critical for regulatory compliance under frameworks like the EU AI Act, where organizations must demonstrate exactly how a high-risk model was built and who was responsible at each stage. The chain of custody integrates with Software Bill of Materials (SBOM) records and in-toto attestations to provide end-to-end verifiability across the entire AI supply chain.
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Master the foundational elements of digital artifact integrity and supply chain security that underpin a robust Chain of Custody.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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