Table of Contents
Executive Summary
Today, the VeritasChain Standards Organization (VSO) is pleased to announce the release of CAP (Content AI Profile) Basic Specification v0.1 — a domain-specific application profile within the Verifiable AI Provenance (VAP) Framework designed to address the unique challenges of AI-assisted content creation across gaming, film, animation, music, publishing, and related industries.
CAP provides a standardized methodology for recording cryptographically verifiable audit trails throughout AI content workflows, enabling organizations to demonstrate compliance with emerging regulations, defend against intellectual property claims, and build stakeholder trust through transparency.
Resources
Specification: CAP Basic Specification v0.1
Homepage: veritaschain.org/vap/cap
The Challenge: AI Content Creation Without Accountability
The integration of artificial intelligence into creative workflows has accelerated at an unprecedented pace. Generative AI tools now assist in character design, background art creation, music composition, voice synthesis, and narrative development across virtually every content vertical.
This transformation brings extraordinary productivity gains — but also unprecedented risks.
The Accountability Gap
When a game studio ships a title featuring AI-assisted character designs, they face a fundamental problem: how do they prove what they did — or didn't — use in their AI pipeline?
A major publisher receives a cease-and-desist letter claiming that a character in their latest release infringes on an independent artist's work. The letter alleges that the character was generated using AI trained on the artist's portfolio without authorization.
In this situation, the studio needs to demonstrate:
- What training data entered their AI pipeline
- Which models were trained or fine-tuned, and on what
- How specific outputs were generated
- The chain of custody from creation to publication
Without systematic provenance records, this demonstration is essentially impossible. This is the "devil's proof" problem: proving a negative is extraordinarily difficult without comprehensive, tamper-evident records.
Regulatory Momentum
The accountability gap is not merely a legal risk — it is increasingly a regulatory requirement.
- EU AI Act (Article 12) mandates that high-risk AI systems maintain logging capabilities that enable monitoring of operation and traceability of results
- MiFID II RTS 25 in financial services already requires algorithmic systems to maintain comprehensive audit trails
- Japan's Copyright Law Article 30-4 provides exemptions for AI training under certain conditions, but the scope of these exemptions is under active debate
- The Digital Services Act (DSA) and GDPR create additional obligations around content provenance and transparency
Industry-Specific Threat Landscape
Through extensive consultation with stakeholders, VSO has identified five primary threat categories:
| Threat ID | Category | Description |
|---|---|---|
| TH-1 | IP Dilution | Unauthorized reproduction of distinctive IP elements through AI generation |
| TH-2 | Reverse Flow | Assets shared with partners being used for unauthorized AI training |
| TH-3 | Confidential Leakage | Pre-release content being exposed through AI pipeline vulnerabilities |
| TH-4 | Deepfake/Likeness Abuse | Unauthorized synthesis of performer likenesses or voices |
| TH-5 | Brand/Style Mimicry | Systematic replication of distinctive brand aesthetics |
Introducing CAP: Content AI Profile
CAP is a domain profile within the VAP (Verifiable AI Provenance) Framework, providing a specialized specification for content creation workflows.
Design Philosophy
CAP is built on a fundamental principle:
「証拠を残す、AIを止めない」
"Leave evidence, don't stop AI"
CAP is not a content moderation system. It does not block AI usage, evaluate output quality, or make determinations about infringement. Instead, it provides the evidentiary infrastructure that enables organizations to:
- Demonstrate what occurred in their AI workflows
- Verify the integrity of their provenance records
- Defend against claims with cryptographic proof
- Comply with regulatory requirements through systematic documentation
Position Within VAP Framework
All VAP profiles share common cryptographic infrastructure:
- Hash Chain architecture for tamper-evident event linking
- Merkle Tree structures for efficient verification
- Ed25519 digital signatures for authenticity
- SHA-256 hashing for integrity
- UUID v7 for time-sortable event identification
Technical Architecture
The Four Core Events
CAP defines four fundamental event types that capture the complete lifecycle of AI content workflows:
INGEST — Asset Intake
Records when any asset enters the AI pipeline (training data, reference materials, style guides, fine-tuning datasets). Every ingested asset receives a cryptographic hash, creating a permanent record of what entered the system.
TRAIN — Model Training
Captures when AI models are trained or fine-tuned. Creates a permanent record linking specific training inputs to resulting model artifacts.
GEN — Content Generation
Records each generation operation, linking outputs to the models and contexts that produced them.
EXPORT — External Distribution
Tracks when assets leave the controlled environment, with explicit PermittedUse documentation for addressing "reverse flow" threats.
Hash Chain Integrity
All CAP events are linked through a hash chain structure. Each event contains the hash of the previous event, creating an append-only log where any modification is detectable.
The Power of Negative Proof
Perhaps CAP's most significant capability is enabling negative proof — the ability to demonstrate that something did not occur.
Traditional audit systems excel at proving positives: "User X performed Action Y at Time Z." But when facing allegations of unauthorized AI training, organizations need to prove a negative: "We did not train on the disputed asset."
How Negative Proof Works
- Comprehensive INGEST Logging: Every asset that enters the pipeline is recorded with its cryptographic hash
- Chain Integrity: The hash chain structure ensures no records can be inserted, modified, or deleted without detection
- Temporal Coverage: Timestamps establish the period covered by the audit trail
- Hash Comparison: The disputed asset's hash can be compared against all logged hashes
If the disputed asset's hash does not appear in the chain, and the chain's integrity is verified, this constitutes cryptographic proof of non-ingestion.
Limitations and Honest Caveats
- It proves non-ingestion of exact files — modified versions would have different hashes
- It requires comprehensive logging from the start — retroactive proof is impossible
- It depends on chain integrity — gaps in coverage weaken the proof
- It does not address semantic similarity — only cryptographic identity
Compliance Philosophy: Evidence Over Prohibition
CAP adopts a "Comply or Explain" approach rather than a prescriptive compliance model.
What CAP Explicitly Does NOT Do
- CAP does not prohibit AI usage — It records, not restricts
- CAP does not detect infringement — Legal determinations require human judgment
- CAP does not equate similarity with illegality — High similarity may result from independent creation
- CAP does not require model internals disclosure — Only inputs and outputs are logged
- CAP does not enable real-time content blocking — It is an audit system, not a filter
- CAP does not evaluate output quality — Aesthetic judgments are out of scope
Industry Applications
Gaming
Comprehensive INGEST logging of all training assets, RightsBasis tracking, ConfidentialityLevel for pre-release content protection, and EXPORT controls for asset sharing with partners.
Film and Animation
ConsentBasis tracking aligned with SAG-AFTRA requirements, multi-jurisdiction compliance documentation, clear audit trails for due diligence verification.
Music
Detailed consent tracking with revocation support, clear distinction between human and AI contributions, audit trails for royalty and credit allocation.
Publishing
TRAIN event logging for fine-tuned language models, clear documentation of human editorial intervention, evidence for plagiarism inquiries.
Getting Started
Implementation Levels
| Level | Name | Typical Use Case |
|---|---|---|
| L1 | Basic | Small studios, indie developers |
| L2 | Standard | Mid-size organizations |
| L3 | Enterprise | Large enterprises, regulated entities |
Resources
- Specification: CAP Basic Specification v0.1
- Homepage: veritaschain.org/vap/cap
- GitHub: github.com/veritaschain
- IETF Draft: draft-kamimura-scitt-vcp
Contact
- Enterprise: enterprise@veritaschain.org
- Technical: technical@veritaschain.org
- Developers: developers@veritaschain.org
Conclusion
The creative industries stand at an inflection point. AI is transforming content creation in ways that bring tremendous opportunity — and significant risk. The organizations that thrive will be those that embrace AI's capabilities while maintaining the accountability infrastructure that protects creators, rights holders, and the public.
CAP provides that infrastructure. It is not a barrier to innovation, but an enabler of responsible innovation. By recording what happens in AI pipelines with cryptographic integrity, CAP enables organizations to:
- Defend themselves against unfounded claims
- Demonstrate compliance with emerging regulations
- Build stakeholder trust through transparency
- Prepare for a future where AI provenance is not optional
We invite content organizations worldwide to evaluate CAP for their workflows. The specification is open, the tools are accessible, and the time to prepare is now.
The VeritasChain Standards Organization (VSO) is a standards body dedicated to developing open specifications for AI system accountability. Our mission is to build "a civilization that can learn before accidents occur" by establishing transparency infrastructure for AI systems before crises emerge.
"Verify, Don't Trust"
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