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An audit-ready record follows every AI-assisted game asset from generation to release. Give each asset a stable ID, preserve the untouched output and its hash, record the tool and model version, timestamp, operator, prompts or source inputs where permitted, repository or build location, human edits and approvals, rights evidence, similarity checks, and final release decision. Keep the record versioned in access-controlled or tamper-evident storage so a reviewer can reconstruct what happened without relying on memory.
What an auditor needs to prove
A useful file is more than a statement that “AI was used.” It is a chain of custody. A reviewer should be able to start with an asset in a shipped build, locate its immutable record, retrieve the original output, see how people changed or selected it, verify the applicable license and provider terms, inspect code or image similarity results, and identify who approved its release.
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Apply the same discipline to a texture, character portrait, sound cue, shader, script, tool-generated code change, marketing image, and any derivative exported from them. Link the asset to the scene, feature, repository path, commit, build, and release version in which it appears. If an output was rejected, retain enough evidence to show that it did not ship and why.
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| State | What it means operationally | Evidence to retain | Rights question |
|---|---|---|---|
| Human-authored | A person created the expressive material or code without generative output contributing to the final form. | Source files, commits, concept notes, reviews, and ordinary build records. | Apply the normal human-authorship and third-party-license analysis. |
| AI-assisted | A person remains the primary expressive author while a system helps brainstorm, autocomplete, translate, refactor, clean, or otherwise support the work. | Tool and model version, relevant instructions, accepted suggestions, human edits, reviewer approval, and the final hash. | Document the human contribution and check the provider’s terms for the intended use and license. |
| AI-generated with human selection, arrangement, or modification | The system supplies substantial material, but people select outputs, combine them, paint over them, pose them, edit them, or arrange them into the game. | All candidate outputs that materially influenced the result, selection rationale, before-and-after files, edit history, composition records, and approvals. | Separate machine-determined elements from protectable human decisions; jurisdiction-specific advice may be needed. |
| Fully machine-determined | The expressive result is determined by the system with no meaningful human selection or modification beyond a routine request. | Original output, prompt or instruction, configuration, model version, hash, and disposition. | Do not assume the output is copyright-protected or exclusively owned; record the uncertainty and any contractual restrictions. |
These labels are not a substitute for legal advice. They make the factual record legible and prevent a later reviewer from treating every use of an AI tool as the same kind of authorship.
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The asset-level record schema
Create one record for every shipped asset or logically grouped code change. A relational database, signed manifest, or versioned form can work; consistency and traceability matter more than the software brand. The following fields cover the questions an auditor normally asks.
| Record area | Capture | Why it matters |
|---|---|---|
| Identity and linkage | Stable asset ID; asset name; file path; repository and branch; scene, feature, or package; commit; build number; release version; successor or replacement asset. | Connects an abstract record to exactly what shipped and prevents duplicate or orphaned entries. |
| Generation event | Provider; model and model version; application or plug-in version; account or workspace; operator; UTC timestamp; prompt or instruction; input references; safety or content-filter settings. | Terms and model behavior can change. A dated event shows which conditions applied. |
| Original evidence | Unmodified download or generation output; cryptographic hash; MIME type; dimensions or programming language; export or download path; creation metadata. | Lets a reviewer verify that later edits did not silently replace the evidence. |
| Human contribution | Paint-overs, code rewrites, rejected suggestions, selection and arrangement decisions, level or scene composition, reviewer, approval date, and a change log. | Shows where human creativity, judgment, testing, and accountability entered the final work. |
| Rights and license evidence | Provider terms archived with their effective date; input licenses; third-party notices; output-use restrictions; contract approvals; required attributions. | Supports the intended distribution model and exposes restrictions before launch. |
| Similarity and provenance checks | Code scanner, image-similarity review, database and version, findings, false-positive decisions, remediation, C2PA manifest or verification result, and metadata-preservation test. | Demonstrates that recognizable third-party material and provenance failures were considered. |
| Security and quality | Functional tests; vulnerability review; privacy review; known limitations; rejected-output log; rollback plan; incident references. | AI provenance is only one part of release risk. |
| Release and retention | Build inclusion; marketing use; target jurisdictions; disclosure or label decision; record owner; retention deadline; legal hold; deletion event; successor asset. | Shows who authorized publication and what happened to the record afterward. |
Build the chain of custody step by step
- Inventory the scope. Export a list of assets and code changes that used a generative system, including discarded outputs that influenced a final composite or implementation. Assign IDs before release rather than trying to reconstruct them after launch.
- Capture the generation event. Record the provider, exact model and tool versions, workspace or account, operator, UTC time, instruction, referenced files, seed or configuration when available, and safety settings. If prompts contain personal data, secrets, or confidential material, store a redacted audit copy and restrict the original to authorized personnel.
- Freeze the original. Save the unmodified output in an evidence store and calculate a cryptographic hash such as SHA-256. Record file type, dimensions or language, and the download or export path. Never overwrite this file with a cleaned or resized version.
- Link every derivative. Give each resize, render, conversion, paint-over, branch, patch, or export its own hash and parent ID. For code, connect the record to the commit and build. For art, connect source files to textures, atlases, engine imports, and marketing derivatives.
- Describe human decisions. Write what a person changed, selected, rejected, combined, or designed. For code, identify architecture decisions, refactoring, tests, and manual rewrites. For art, retain sketches, layer history, masks, compositing, pose or layout choices, and before-and-after images where practical.
- Archive rights evidence. Save the provider terms that applied on the generation date, the relevant input licenses, notices, and any contract approval. A current web page is not enough if its wording later changes; retain a dated copy or approved internal capture according to your records policy.
- Run similarity and provenance checks. Record the code scanner and database version, image-similarity method, findings, false-positive reasoning, remediation, and final disposition. Verify any C2PA or equivalent manifest, but preserve it alongside—not instead of—the original and your internal chain of custody.
- Obtain human sign-off. Name the reviewer, approval date, scope of review, unresolved limitations, and conditions. Separate creative approval from security, privacy, and licensing approval when those are performed by different people.
- Connect the release decision. Record the build, platform, market, audience, marketing use, disclosure decision, owner, and rollback or replacement asset. If an export strips metadata, record the exact transformation and retain the pre-strip artifact.
- Maintain the record. Version the manifest, log corrections and deletion events, preserve legal holds, and keep successor links when an asset is replaced. A silent edit to an audit record is itself a control failure.
Illustrative manifest
The values below show the level of specificity expected; adapt the fields to your repository and privacy policy.
{
"asset_id": "ART-CHAR-00427",
"file_path": "Game/Characters/Mira/mira_face_02.png",
"repository": "git.example.invalid/game-client",
"commit": "8f31c2d",
"build": "1.4.0-rc3",
"release": "1.4.0",
"provenance_state": "AI-generated with human modification",
"provider": "Example Image Service",
"model": "ExampleVision 3.2",
"tool_version": "5.7.1",
"operator": "designer-17",
"generated_at_utc": "2026-09-14T10:23:41Z",
"prompt_record": "vault://prompts/ART-CHAR-00427-v1",
"input_references": ["brief://characters/mira-v6"],
"original_sha256": "7b4e3c...91aa",
"original_file": "evidence://originals/ART-CHAR-00427.png",
"human_changes": "Selected the eyes from output 3; repainted hair, lighting, and costume details; composited into the approved turnaround.",
"derivatives": ["evidence://derivatives/ART-CHAR-00427-turnaround.psd", "Game/Characters/Mira/mira_face_02.png"],
"c2pa_result": "manifest verified; metadata removed by engine import; pre-import file retained",
"similarity_review": "No unresolved match; reviewer note SR-2026-118",
"terms_archive": "legal://provider-terms/2026-09-14",
"reviewer": "art-lead-03",
"approved_at_utc": "2026-09-18T16:05:00Z",
"disclosure_decision": "No public label required for in-game asset in launch markets; marketing derivatives reviewed separately",
"retention_deadline": "2029-09-18",
"owner": "production-compliance"
}Controls for AI-generated and AI-assisted code
Record the tool in version control
Archive the provider terms that applied when the code was generated and put the model or tool version in the commit or asset manifest. Apache guidance recommends identifying contributions made with generative tooling; its suggested format is Generated-by: <Name of Tool> <Version> or Co-authored-by: <Name of Tool> <Version>. You can keep that token in internal metadata even if your public repository does not expose it.
For an autocomplete suggestion, record the accepted change and the human author who reviewed it. For a generated subsystem, retain the relevant instruction, generated draft, rewritten final code, tests, and architecture decision. A prompt by itself does not establish authorship or license rights.
Check licenses and recognizable code
Require a reviewer to look for copied snippets, distinctive third-party implementations, embedded credentials, and obligations triggered by a dependency. Record the scanner name, database or ruleset version, date, findings, false-positive decisions, remediation, and the final license notice. Confirm that the tool’s terms grant rights broad enough for the intended game license and distribution model, and that third-party material is used with permission and compliant terms.
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Map the work to the secure-development lifecycle
NIST SP 800-218A augments the Secure Software Development Framework 1.1 with generative-AI-specific practices across the software life cycle. Use it as a control map: connect an AI-assisted change to requirements, code review, testing, vulnerability management, release, monitoring, and incident response. Keep evidence that generated code passed the same tests and security gates as human-written code; do not create an “AI” exemption.
- Store prompts and outputs in a repository with access controls that prevent accidental disclosure of proprietary source.
- Run secret scanning, dependency analysis, static analysis, and unit or integration tests before approval.
- Document known hallucinations, unsafe APIs, performance limits, and the reviewer’s corrective actions.
- Link the final commit, build artifact, software bill of materials where used, and release notes to the asset record.
- Keep a rollback commit or replacement implementation for code that fails after release.
Controls for AI-generated art
Preserve the complete image history
Keep the first-generation file, prompt or instruction, seed and configuration when available, model and version, and every derivative. A flattened PNG is not a substitute for a layered source when the layers explain the human contribution. Record selection among candidates, compositing, paint-over, posing, masking, color changes, upscaling, and engine import.
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C2PA Content Credentials can attach origin and editing history in a machine-readable manifest. Verification is useful evidence, but it does not prove that an image is accurate, unedited, legally owned, or associated with a particular creator. Keep the original file and manifest in your own evidence store. Test each editor, asset-management system, and game-engine export: if metadata is stripped, record the tool, version, date, and resulting hash, then retain the pre-strip artifact.
Separate game assets from public-facing material
Apply a disclosure review to trailers, store art, screenshots, social posts, and other marketing outputs. A private in-game texture and a public deepfake can trigger different obligations and platform rules. Record the audience, market, content type, whether the material depicts a real person, the label or icon used, the reviewer, and the responsible owner.
Copyright and authorship evidence
The U.S. Copyright Office’s Part 2 announcement says its conclusions turn on “the centrality of human creativity to copyright” and warns that extending protection to material “whose expressive elements are determined by a machine” would undermine constitutional goals. In practice, preserve evidence of the human decisions that shape the final game asset: concept briefs, iterations, paint-overs, edits, level or scene composition, code architecture, refactoring, testing, and approvals.
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Do not present a prompt as the source of copyright ownership. When registration, licensing, or a dispute requires a distinction, identify the human-authored portions and the machine-determined portions and obtain jurisdiction-specific counsel. A rights matrix makes that distinction actionable:
| Question | Record |
|---|---|
| What did the system supply? | Original output, model, version, instruction, configuration, and hash. |
| What did people decide? | Selection, arrangement, edits, composition, code design, and rejected alternatives. |
| What third-party material appeared? | Similarity findings, licenses, notices, permissions, and remediation. |
| What will be claimed or licensed? | Human-authored scope, territory, term, media, and any exclusions identified by counsel. |
EU and cross-border compliance
Provider and deployer duties
If a studio places a general-purpose AI model on the EU market or acts in a role covered by the AI Act, map the provider’s documentation into the audit file. Commission guidance lists technical documentation, downstream documentation, a Union-copyright policy, and a public training-content summary, with documentation kept current through the model life cycle. The guidance uses an indicative compute threshold of 1023 floating-point operations together with broad generation capability when discussing certain general-purpose models; treat that figure as a regulatory criterion, not as a claim that every game tool reaches it.
Article 50 transparency records
The Commission says Article 50 transparency obligations apply from 2 August 2026. The underlying obligations are binding even though the related code of practice is voluntary. For each public-facing output, record:
- the market and audience;
- the content type and whether it is a deepfake or covered AI-generated text;
- the marking method, label wording, or icon;
- whether a human reviewed the result and what that review covered;
- the responsible business owner and publication date.
The Commission reported about 190 signatories to the AI-generated-content transparency code in July 2026. Participation in that code does not replace the legal analysis for your role, market, or content.
Use consistent cross-border controls
A UK or US studio shipping into the EU should map each release market rather than assuming one global label. HMRC software guidance offers a useful operational pattern: make AI enhancement and human review visible, maintain reliable source data, monitor continuously, version the system, update it promptly, and document model limitations. These practices help even where a particular game asset is outside a specific statutory label requirement.
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Retention, privacy, and security
There is no universal retention period for game AI records and no single disclosure rule covering all AI-generated code and art. Set deadlines through contracts, launch jurisdictions, platform terms, privacy requirements, legal holds, and counsel review. Put the deadline, owner, and deletion event in each record rather than relying on an unwritten team habit.
- Minimize sensitive inputs. Redact personal data, credentials, unreleased source, and confidential partner material from routine audit views. Keep restricted originals only when a documented purpose requires them.
- Control access. Separate production users, reviewers, legal staff, and vendors; log reads and changes; encrypt records in transit and at rest.
- Make evidence tamper-evident. Use immutable or append-only storage where feasible, sign manifests, and preserve hash values outside the working directory.
- Plan deletion. A privacy deletion request or contract expiry may require removing a prompt or source file. Record what was deleted, under whose authority, on what date, and which hash or redacted substitute remains.
- Preserve legal holds. Suspend routine deletion for disputes, investigations, or regulator requests and link the hold to affected asset IDs.
How to assemble an audit packet
Give the reviewer a read-only index rather than a dump of every project folder. Start with the release manifest, then provide a map to asset records, code commits, build artifacts, terms archives, scanner reports, approvals, and disclosure decisions.
- Export the list of shipped AI-related assets and code changes for the release.
- Verify that every item has a stable ID, hash, provenance state, owner, and build link.
- Sample records from code, art, rejected outputs, and marketing derivatives.
- Recompute hashes on the original files and compare them with the manifest.
- Follow one asset from the build back to the generation event and forward to its public release decision.
- Check that terms, scanner versions, model versions, reviewer identities, and dates are present.
- Document exceptions, missing evidence, compensating controls, and the person responsible for remediation.
A reviewer should be able to answer “which parts were made by AI?” without guessing. The answer may be a mixture of states: machine output in one layer, human composition in another, and entirely human code around both. Your manifest should expose that mixture.
Common failure modes and recovery
Only the final file was saved
Recover the generation date, tool and model version, operator, prompt or input references, and the earliest available export from logs, local caches, or provider history. Mark reconstructed fields as reconstructed, preserve their confidence and source, and require a reviewer to approve the exception. Do not backfill an invented prompt.
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Retain the pre-export file and manifest, hash both files, record the application and version that removed metadata, and link the exported asset to its parent. Do not claim that the shipped file carries credentials when it does not.
A scanner found a possible copied snippet
Quarantine the change, preserve the scanner result and database version, inspect the match, and record whether it is a false positive. If it is third-party code, replace it or document permission, license notices, attribution, and any source-disclosure obligation before release.
The provider changed its terms
Use the archived terms effective on the generation date for the historical decision, then assess whether a later update affects continued use, derivatives, support, or new releases. Escalate conflicts between contract language and product documentation to counsel.
An asset cannot be classified confidently
Use the most conservative factual description, list the missing evidence, and restrict the intended claim or distribution until counsel decides. Ambiguity is a recordable risk; silently labeling the work “human-made” is not a control.
Choosing provenance and review tools
| Tool category | Compare these capabilities | Questions to ask in procurement |
|---|---|---|
| Provenance system | Metadata durability; C2PA support; verification coverage; export-pipeline compatibility; preservation of original files and hashes. | Which editors and engines strip manifests? Can the system retain pre-strip artifacts, sign records, expose an API, and show a complete chain to a build? |
| Code scanner | Language and engine coverage; known-license database quality; private-code handling; false-positive workflow; SBOM or report export; source-control and CI integration. | What database version produced a finding? Can reviewers explain a false positive? Are proprietary repositories isolated from vendor training or discovery? |
| Advisory provider | Jurisdiction coverage; game and software licensing experience; evidence-retention practice; ability to review both code and art workflows. | Can the adviser distinguish human and machine-determined expression, evaluate provider terms, and support an audit without taking custody of unnecessary personal data? |
Do not select a tool solely because it displays a provenance badge. The useful test is whether your team can retrieve the original, verify the hash, understand the human contribution, and reproduce the release decision after a staff change or a platform export.
Release checklists
Before generation
- Confirm the provider and model are approved for the data and intended distribution.
- Define whether prompts may contain personal, confidential, or third-party material.
- Set the asset ID convention, storage location, access roles, and retention owner.
- Identify the target markets and whether public disclosure may apply.
For every output that may ship
- Capture the generation event and preserve the untouched output.
- Calculate and record hashes for the original and each material derivative.
- Classify the provenance state and describe human decisions.
- Archive terms, licenses, notices, scanner results, provenance checks, tests, and approvals.
- Link the final file or commit to a build and release version.
Before public release
- Review in-game, store, trailer, and social assets separately.
- Complete the market-specific disclosure and labeling decision.
- Confirm rollback, successor-asset, incident, and legal-hold procedures.
- Verify that the read-only audit index opens every linked record.
The durable rule is simple: preserve what the system produced, document what people changed and decided, prove which terms and checks applied, and connect the evidence to the exact build that shipped. That record is what turns an AI-use disclosure into an auditable compliance control.
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