Artifact review is the process of inspecting a model file, source code, or software package before it is approved for use. In AI environments, the artifact may include model weights, training code, configuration files, dependency manifests, or deployment bundles. The goal is to confirm that the object is authentic, safe, and suitable for the intended environment.
This matters because compromised artifacts can introduce hidden backdoors, malicious code, unsafe dependencies, or unintended behavior into production systems. Security teams use artifact review to check provenance, scan for malware, verify signatures and hashes, examine dependencies, and compare the item against policy. In real defenses, it helps reduce supply-chain risk by stopping untrusted models or packages before they reach deployment. In attacks, adversaries may try to disguise poisoned models or tampered packages as legitimate releases, so review acts as a critical trust gate.