The increasing integration of artificial intelligence (AI) in creative and technical domains necessitates robust mechanisms for ensuring transparency, attribution, and compliance. The opacity of AI generated content presents significant challenges in legal compliance, ethical accountability, and industry regulation. The CLEAR Protocol (Compliance Logging for Ethical AI Attribution Registry) introduces a structured, verifiable framework to track, log, and authenticate AI contributions. By leveraging structured metadata, im- mutable timestamping via cryptographic verification, and aligning with established regulatory frameworks, the protocol offers a scalable solution for ensuring AI transparency across diverse industries, including publishing, media, software development, and legal services. This paper details the need for AI attribu- tion, explores the challenges surrounding AI assisted content, and presents the technical framework of the CLEAR Protocol, including its logging structure, timestamping methodology, verification mechanisms, and a comprehensive Google Cloud based implementation design.

The integration of AI into content generation has created a critical need for accountability in determining authorship and attribution [20]. AI systems, including large language models, generative visual tools, and automated coding assistants, have blurred the lines between human authored and AI assisted works. This raises significant concerns regarding copyright ownership, ethical use, and regulatory compliance [13]. In addition to copyright concerns, the US Patent and Trademark Office has also issued guidance on inventorship in cases involving AI assisted inventions [17], further highlighting the evolving legal landscape. While AI has expanded creative possibilities, its unregulated implementation has resulted in legal disputes and challenges in intellectual property law [2]. The rapid proliferation of AI generated content further complicates this landscape, demanding scalable solutions that provide transparent attribution and robust verification mechanisms.

This paper proposes establishing the CLEAR Protocol, a new open standard designed to facilitate verifiable logging of AI contributions. The protocol enforces a structured approach to AI content attribution through project registration, metadata logging, cryptographic timestamping, and compliance monitoring. By incorporat- ing secure data storage and external verification via established timestamping services, such as OpenTimestamps [15], the CLEAR Protocol aims to mitigate legal ambiguity, promote ethical AI use, and align with international regulatory frameworks, including the EU AI Act [7] and US Copyright Office guidelines [16]. The following sections examine key challenges in AI attribution, describe the technical implementation of the protocol, present a detailed Google Cloud centric architectural design, and evaluate its applications across industries.

The integration of AI into creative fields has been met with skepticism and resistance from artists, writers, and content creators [9]. Many within the creative industry perceive AI as a disruptive force that threatens their intellectual property, diminishes human authorship, and enables large scale replication of creative works without consent [3]; [4]. The rapid adoption of AI generated imagery, text, and music has fueled concerns about the potential replacement of human creators, with legal cases emerging over the unauthorized use of copyrighted materials in AI model training datasets [10]. The lack of attribution mechanisms has led to the proliferation of AI generated content indistinguishable from human work, further intensifying concerns regarding plagiarism, creative integrity, and the financial sustainability of original creators.

However, AI possesses the potential to be a powerful tool for creatives, enabling new forms of expression, streamlining workflows, and enhancing artistic output [1]; [3]. For AI to be widely adopted as an assistive tool, it must earn the trust of the communities it aims to serve. Establishing this trust requires clear attribution, transparency, and verifiable documentation of AI's role in the creative process.

The CLEAR Protocol directly addresses these concerns by providing a structured, transparent framework for attributing AI generated content. By ensuring that every AI assisted work is registered, logged, timestamped, and traceable, CLEAR offers a solution that respects the intellectual contributions of both human creators and AI. The protocol serves not only as a compliance tool for legal and regulatory requirements but also as a trust building mechanism between AI and the creative community. Through detailed metadata logging, cryptographic timestamping, and human modification tracking, CLEAR enables creators to maintain control over their work by ensuring that AI contributions are distinctly separated from human authored elements.

The protocol facilitates the demonstration of human originality by providing a verifiable history of mod- ifications, establishing proof of creative input beyond AI assistance. The system provides safeguards against unauthorized AI replication by documenting AI's role, deterring uncredited use of AI generated content. This encourages ethical AI adoption by fostering an ecosystem where AI tools are collaborative partners rather than replacements for human creators.

By implementing CLEAR, AI transparency shifts from a legal necessity to an ethical standard, reinforcing the value of human authorship in creative endeavors. The protocol ensures that AI remains a tool that serves creatives, rather than a force that undermines them.

The CLEAR Protocol, administered by a designated organization similar to ICANN or WHOIS, establishes a structured system for AI attribution through project registration, metadata logging, timestamping, and verification. The process begins with a creative or IP holder registering their project with CLEAR. This initial registration creates a "draft" submission and generates a unique Project ID. This Project ID is then used throughout the content creation process, linking AI generated content and human modifications to the registered project.

AI contributions are recorded at the point of generation, capturing essential details such as the AI model version, input parameters, and generated output, all linked to the Project ID. This metadata logging process ensures that each AI assisted creation is traceable to its origin, facilitating compliance with evolving copyright regulations and industry standards. To distinguish human modifications from AI generated content, the pro- tocol integrates real time tracking mechanisms that document user edits, alterations, and contributions, also associated with the Project ID.

The CLEAR Registry functions as a centralized repository for AI contribution logs. Each metadata record is assigned a cryptographic signature from the AI model provider, ensuring the authenticity of the logged data. Secure cloud based storage, such as Google Cloud Storage [14], is used to maintain attribution records while preserving privacy and data security. The protocol enhances verifiability by implementing robust timestamping mechanisms. Metadata records are periodically aggregated and structured into a Merkle Tree, producing a unique Merkle Root hash that serves as an immutable representation of AI contributions. This Merkle Root is then submitted to an external timestamping service, such as OpenTimestamps, where it is anchored to a public blockchain (e.g., Bitcoin), ensuring that records cannot be altered retroactively [15].

The verification process within the CLEAR Protocol relies on multiple methods to differentiate AI contri- butions from human modifications. Semantic similarity analysis, which compares the structure and meaning of text [8], is employed to assess content. Edit distance algorithms, which quantify the differences between two sequences [5], are used to determine the number and type of modifications made to AI generated material. By applying user defined weighting systems, the protocol refines AI contribution percentages, allowing for a nuanced assessment of AI involvement. Through these combined mechanisms, the CLEAR Protocol establishes a transparent and verifiable system for AI content attribution.

This section delineates the technical design of the CLEAR Protocol, which leverages Google Cloud's managed services to provide a scalable, secure, and cost effective solution for AI transparency, attribution, and compliance.

4.1 System Components and Services

CLEAR SDKs: The CLEAR Software Development Kits (SDKs) enable seamless integration with both AI models and content creation tools. The AI Model SDK, implemented in Python for frameworks such as Tensor- Flow and PyTorch, intercepts inference calls using native APIs (e.g., tf.function tracing, torch.jit.trace). It logs critical parameters - including model version, input prompts, and generated outputs - and applies cryp- tographic methods such as SHA-256 hashing and the Elliptic Curve Digital Signature Algorithm (ECDSA) with the SECP256k1 curve to digitally sign the log entries [12]. Key management is facilitated via Google Cloud Secret Manager or, for enhanced security, through a Hardware Security Module (HSM) provided by Google Cloud KMS. In parallel, the Content Creation SDK (available in both JavaScript and Python) monitors user modifications in real time - using event listeners and the diff-match-patch library - to capture and securely sign modification logs.

CLEAR API: The CLEAR API, developed as a RESTful interface using serverless technologies (Google Cloud Functions) or containerized deployments (Cloud Run), provides endpoints for logging both AI generated and human modified content. Notable endpoints include:

API security is ensured through the use of API keys (managed via Secret Manager), rate limiting, and exten- sive logging via Cloud Logging. The API uses API keys (managed via Secret Manager) for SDK authentication, implements rate limiting, and utilizes Cloud Logging for auditing and debugging.

CLEAR Registry and Data Storage: The CLEAR Registry functions as the centralized repository for all AI contribution logs. Data is stored as follows:

• Cloud Storage: Archives raw, encrypted JSON logs in a structured bucket system organized by Project ID, content ID, and timestamp.
• BigQuery: Maintains a flattened, query optimized version of the log data, partitioned by creation times- tamp and clustered by Project ID and content identifiers.

Dataflow pipelines, triggered by Cloud Scheduler, aggregate new logs associated with a Project ID to generate a Merkle Tree, whose root hash serves as an immutable summary of the contributions. This Merkle Root is then submitted to an external timestamping service (e.g., OpenTimestamps), anchoring the data to a public blockchain [15]. Intercomponent communication is managed via Google Cloud Pub/Sub, and access control is enforced using Cloud IAM.

Additional Services: Supplementary services include the CLEAR Explorer - a React based web application hosted on Firebase Hosting or Cloud Run - which enables users to interact with the CLEAR API, visualize AI contributions, and perform content verification. Additionally, a Reputation Service, implemented using cloud functions and BigQuery, calculates reputation scores for both users and AI models to foster trust in the system.

4.2 End to End Workflow

The operational workflow of the CLEAR Protocol integrates project registration, AI content generation, human modifications, secure logging, and verifiable timestamping. An overview of the workflow is as follows:

1. Project Registration: A creative or IP holder registers their project with the CLEAR organization via the /register API endpoint. The CLEAR system generates a unique Project ID and creates a draft submission.
2. Project ID Provisioning: The creative/IP holder provides the Project ID to the generative AI model or their chosen content creation tool (e.g., Photoshop, Google Docs). This ID is incorporated into the metadata for all subsequent AI generated content and human modifications.
3. AI Content Generation: An AI model produces content. The integrated AI Model SDK intercepts the inference call to capture essential metadata, including the Project ID. This log entry is digitally signed and prepared for submission.
4. AI Log Submission: The signed log, including the Project ID, is transmitted to the CLEAR API. It undergoes rigorous schema validation, Project ID validation, and signature verification. Once validated, the log is stored in Cloud Storage and ingested into BigQuery, and a corresponding event is published via Google Cloud Pub/Sub.
5. Human Content Modification: When a human user edits the AI generated content, the Content Creation SDK tracks these modifications in real time. The modifications are logged, digitally signed, and include the Project ID, prepared for submission in a manner analogous to the AI log.
6. Human Log Submission: The human modification log, including the Project ID, is submitted to the CLEAR API, validated for schema, Project ID, and signature correctness, and then used to update the relevant records in BigQuery.
7. Merkle Tree Generation and Timestamping: At periodic intervals, Cloud Scheduler triggers a Dataflow pipeline to aggregate new log entries associated with a given Project ID. The pipeline generates a Merkle Tree, and the resulting Merkle Root is submitted to an external timestamping service (such as OpenTimestamps). This process anchors the log data to the Bitcoin blockchain, ensuring immutability and verifiability of the records [15]. The timestamp proof is then stored in Cloud Storage and BigQuery.
8. Content Verification: Upon request, via the CLEAR Explorer or a direct API call, the system retrieves the associated metadata, verifies the Project ID and its registration status, verifies the timestamp proof, and calculates the refined AI contribution percentage using semantic similarity [8] and edit distance algorithms [5]. Additionally, reputation scores from the Reputation Service are integrated into the final verification report.
9. Reputation Management: Continuously, the Reputation Service computes and updates reputation scores for users and AI models based on historical interactions and adherence to protocol guidelines.

This comprehensive workflow not only ensures robust traceability and verifiability of AI generated content but also aligns with international regulatory standards such as the EU AI Act [7] and US Copyright Office guidelines [16].

The rapid proliferation of AI generated content necessitates robust mechanisms for transparency, attribution, and compliance. The CLEAR Protocol presents a structured, verifiable framework that addresses these chal- lenges by integrating project registration, metadata logging, cryptographic timestamping, and secure verification methodologies. By aligning with international regulatory frameworks such as the EU AI Act [7] and others [11]; [6], CLEAR offers a scalable and effective solution for AI transparency across industries. The detailed Google Cloud based architecture demonstrates a practical implementation path. Future research will focus on refining attribution methodologies, enhancing the reputation system, and advancing AI transparency standards to meet the growing demands of the digital economy. Ongoing work will also explore broader integration with dispute resolution mechanisms [18]; [19] to address potential conflicts arising from AI generated content.