What is synthetic forensics?

Working definition

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.

Why the field exists

AI systems can create plausible media and take actions across applications. Their outputs often travel through editing, export, and sharing before an examiner sees them. A final file may answer fewer questions than the application, account, interaction, and external system records around it. The discipline’s purpose is to connect those sources through defensible methods.

What counts as synthetic evidence?

“Synthetic evidence” can mean a generated object, a record of an AI interaction, or a trace of an AI-assisted workflow. It does not mean that the evidence itself is fabricated or unreliable. State which meaning applies. The term also does not grant special certainty: AI-related material remains subject to ordinary concerns about acquisition, integrity, provenance, completeness, and interpretation.

Relationship to neighboring disciplines

Typical evidence sources

Start with conversations and attachments, application databases and browser storage, agent tool records, original media, content provenance, account exports, network logs, and model configuration. Which sources exist depends on platform, version, retention, access, and collection timing. The evidence taxonomy describes these classes without claiming that every application creates them.

Typical investigative questions

• What was created, altered, or acted upon, and in which environment?
• Can a conversation or agent task be reconstructed from source records?
• What connects an output to a particular workflow, account, or model configuration?
• Which provenance assertions validate, under which trust assumptions?
• What remains unresolved because data is missing, inaccessible, or unvalidated?

Principles that constrain conclusions

• Detection ≠ provenance. A statistical classification and a documented origin answer different questions.
• Attribution ≠ proof. A plausible source association needs independent support and stated limits.
• A prompt ≠ the complete interaction. Context, revisions, branches, uploads, tools, and system state can matter.
• Generated content ≠ all available evidence. The output is one part of a wider digital evidence environment.
• Missing provenance ≠ non-synthetic origin. Records may never have existed, may be inaccessible, or may have been stripped.
• Model attribution ≠ certain identification. Output style alone rarely identifies a model reliably.
• Automated scores ≠ forensic conclusions. A tool result must be interpreted within its validation conditions.
• User input ≠ necessarily user intent. An interaction record does not, by itself, explain motive or understanding.

Limits and current examination challenges

Historical model state, inaccessible cloud records, incomplete logging, and transformed media can limit reconstruction. A detector’s performance on a benchmark does not establish performance on every case object. The same interface action can leave different traces across versions.

Agent delegation, multimodal workflows, provenance interoperability, and changing model deployments require versioned observations and independent replication. This project publishes a working taxonomy and proposed methodology to make such observations comparable; it does not certify practitioners or claim to establish an international standard.

Where an examiner should start

Define the authorized question, inventory acquired evidence, preserve volatile sources when justified, and record unavailable sources. Then use the seven-field overview, methods, and published SFIF workflow. Consult related terminology and citation guidance when referring to this definition.