Inferensys

Glossary

Non-Repudiation

A security property providing undeniable proof of the origin and integrity of data, ensuring the entity that signed an artifact cannot later deny having done so.
Data scientist building training data pipeline on laptop, data preprocessing visible, technical workspace.
CRYPTOGRAPHIC ACCOUNTABILITY

What is Non-Repudiation?

Non-repudiation is a security property providing undeniable proof of data origin and integrity, preventing an entity from credibly denying authorship or receipt of a digital artifact.

Non-repudiation is a core information security objective that cryptographically binds an action or artifact to its originating identity. Unlike simple authentication, which verifies a claimed identity at a point in time, non-repudiation provides forensic proof that a specific party generated, signed, or approved a specific piece of data. This is achieved through digital signatures and a robust Public Key Infrastructure (PKI) , ensuring the signer cannot later repudiate their involvement.

In the context of tamper-proof model registries, non-repudiation is critical for auditability. When a model artifact is pushed to a registry, a signing mechanism like Cosign generates a cryptographic signature linked to the developer's workload identity. This signature, recorded in a transparency log such as Rekor, creates an immutable, verifiable assertion of provenance, guaranteeing that a specific, audited model is the one deployed to production.

UNDENIABLE PROOF

Core Properties of Non-Repudiation

Non-repudiation is a composite security property built on several cryptographic primitives. These core properties collectively ensure that an entity cannot credibly deny having signed or originated a specific digital artifact.

02

Proof of Integrity

Guarantees that the artifact has not been altered after the signature was applied. The process relies on a cryptographic hash function (like SHA-256) to generate a unique, fixed-size digest of the artifact's contents. This digest is then included in the signed payload. Any subsequent modification to the artifact—even a single bit—will produce a completely different hash, immediately invalidating the signature and signaling tampering.

SHA-256
Standard Digest Algorithm
04

Verifiable Identity Binding

Connects the cryptographic signing key to a real-world, auditable identity. This moves beyond simple public-key cryptography to answer: who is the signer? Methods include:

  • X.509 Certificates: Binding a public key to a verified organization.
  • SPIFFE IDs: Binding a key to a specific workload in a distributed system.
  • OIDC Tokens: Used in keyless signing to bind an ephemeral key to a federated identity (e.g., a GitHub Actions workflow run).
06

Auditable Evidence Trail

The aggregation of all proofs—signature, integrity hash, timestamp, and identity binding—into a persistent, verifiable record. This bundle of evidence, often stored as an in-toto attestation or within a Rekor log entry, serves as the legal and technical proof required to settle disputes. It allows a third-party auditor to independently verify the entire chain of custody without trusting the signer or the verifier.

NON-REPUDIATION

Frequently Asked Questions

Clear answers to the most common questions about cryptographic non-repudiation, its mechanisms, and its critical role in tamper-proof model registries and AI supply chain security.

Non-repudiation is a security property that provides undeniable proof of the origin and integrity of data, ensuring that the entity that signed an artifact cannot later deny having done so. It works by binding a digital signature—created with a private key—to a specific artifact, such as a model weight file or an SBOM. This signature can be independently verified by any third party using the corresponding public key. The mechanism relies on asymmetric cryptography, where only the signer possesses the private key, making the signature computationally infeasible to forge. When combined with a transparency log like Rekor, the act of signing is publicly recorded, adding a temporal anchor that prevents retroactive repudiation even if the signing key is later compromised.

Prasad Kumkar

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.