Synthetic forensics: the field and its practice

The science of synthetic evidence

Synthetic forensics is the systematic identification, preservation, examination, reconstruction, and interpretation of digital evidence involving artificial intelligence systems, synthetic media, AI-generated artifacts, and human-AI interactions.

The definition includes evidence of AI use as well as generated content. A retained conversation, application database, or service receipt may be authentic evidence even when it describes a synthetic object. Define the proposition before assigning the word authentic.

Build on digital-forensic practice

Traditional digital forensics supplies acquisition, preservation, examination, correlation, and reporting foundations. AI-related work adds changing cloud deployments, generated content, branching interactions, tool actions, and model uncertainty. These conditions require careful source and version records; they do not remove ordinary evidentiary obligations.

Examine objects, interactions, and actions

Media examination asks about the acquired image, video, or audio and its transformations. Artifact examination asks what applications and devices retained. Interaction examination follows prompts, uploads, retrieved material, responses, and revisions. AI Agent Forensics follows tasks, permissions, tool use, delegation, and independently observed system effects.

Keep provenance, authenticity, and attribution distinct

Provenance records history; authenticity tests a stated claim about an item; attribution associates it with a source at a named level. A valid signature, a model label, and a matching account identifier can support different propositions. None should silently become proof of a real-world event or a particular person’s intent.

Reconstruct supported histories

Use source identifiers, raw timestamps, branches, and external effects to build a traceable history. Retain competing hypotheses when several sequences fit. Missing records, delayed jobs, and conflicting clocks belong in the reconstruction rather than in a footnote after a confident timeline.

Preserve integrity and handling history

Keep original and derived objects separate. Record acquisition scope, hashes, custody events, transformations, and tool versions. Byte integrity supports comparison of objects; it does not establish the truth of their contents. See chain of custody for a handling record that preserves those distinctions.

Test alternatives and express uncertainty

Compare the preferred explanation with ordinary editing, copied content, incomplete logging, unknown generators, and unsupported parsing where relevant. Seek independent corroboration. A detector score or several correlated scores cannot replace an explained conclusion; use confidence statements tied to the actual proposition.

Make the method reproducible

Retain inputs, conditions, controls, repetitions, errors, and derivation steps. Repeatability in one version is narrower than validity across environments. Public research should disclose redaction and access constraints, material AI involvement, and any failure to replicate a consequential observation.

Respect evidentiary and human context

A reliable technical observation can leave responsibility, belief, or legal significance unresolved. The evidentiary research area explains technical documentation boundaries; this guide is not legal advice. Human source-monitoring questions require their own appropriate research design rather than inference from one transcript.

Research challenges and where to continue

Changing models, incomplete cloud histories, transformed media, source-label loss, and inaccessible validation data remain practical challenges. Explore the ten research areas, follow the evaluation method, and consult the roadmap for questions that remain open.