The AI Right-to-Erasure Protocol is a formal process for identifying, evaluating, and reducing a person’s identifying information across AI outputs, retrieval sources, logs, and training data. It combines identity verification, scope definition, technical suppression, and measurable testing to limit reappearance under repeated queries.
[https://www.youtube.com/watch?v=-NPbGEg5n_Q]
The embedded video demonstrates how name-based requests (“remove my name”) collide with the architecture of large language models (LLMs). It shows that user expectations often assume a single database record exists, when in practice identity traces may persist across multiple system surfaces: chat transcripts, moderation logs, retrieval indexes, cached outputs, and derived embeddings.
The video also illustrates how AI systems can reproduce personal identifiers via indirect prompting. Even when a system avoids directly stating a name, it may emit unique correlates—employer, location, rare events, or affiliated entities—that allow re-identification. This matters because most real-world “name removal” failures are not a direct repetition of a full name string; they are correlation leaks that reconstruct identity.
Finally, the video frames remediation as a controlled protocol rather than a one-time action. It highlights the need for verification across paraphrases, multilingual prompts, and retrieval-driven regeneration, because superficial filters can appear effective in narrow tests but fail after system updates or index rebuilds.
A “remove my name from ChatGPT” request is typically misunderstood because it is framed as a deletion operation, while LLM behavior is produced through probabilistic generation conditioned by system context. A definition-grade protocol begins by separating what is technically “stored” from what is statistically “reproducible.” The protocol’s purpose is not to promise total disappearance, but to define verifiable steps that reduce identity emission risk across the parts of the stack that are actually controllable.
At the definitional level, the AI Right-to-Erasure Protocol can be described as a multi-surface identity control workflow. A platform must first determine what “the name” means in operational terms. It can include full legal name strings, aliases, usernames, and unique identifiers. It can also include identity-adjacent descriptors that produce de facto identification. This definition step matters because erasure that targets only a single name string is often structurally insufficient: the system may still output identifying correlates or reconstruct identity through indirect cues.
The next definitional layer concerns system surfaces. In modern AI deployments, identity-bearing material can exist in at least four categories:
Output behavior (what the model generates under certain prompts and contexts)
Retrieval behavior (what RAG pipelines supply as evidence or context)
Operational storage (logs, transcripts, analytics, tickets, evaluation datasets)
Training and derivative artifacts (training corpora, fine-tune sets, embeddings, cached completions)
A platform that defines “removal” as output-only filtering is not performing an erasure protocol; it is performing a display-layer mitigation. That approach is fragile because retrieval components can reintroduce names and because operational logs can preserve identifiers indefinitely unless retention controls exist.
The protocol’s definitional core includes two competing constraints. First, identity suppression is often required to reduce harm, especially when false attribution, defamation, or doxxing risks exist. Second, indiscriminate deletion creates an abuse vector: hostile actors can impersonate a subject and request removal of truthful, public-interest, or safety-critical information. This is why identity verification and evidence thresholds are definitional requirements, not optional administrative steps.
A complete definition-grade protocol also specifies what counts as proof. “Success” cannot be defined as a single test prompt failing to reproduce a name. Verification must reflect the operational reality of user behavior and AI variability. That includes repeated testing against paraphrases, multilingual prompts, indirect references, and retrieval-driven regeneration. In other words, definitional success is a measurable reduction in emission probability under a defined test suite—not an informal assurance.
The protocol is also constrained by lifecycle events. Suppression controls that work today can fail after model upgrades, policy changes, retrieval index rebuilds, or caching changes. Therefore, the definitional scope of an erasure protocol includes monitoring. If monitoring does not exist, the platform will eventually issue a promise that becomes false retroactively.
This is the technical reason an erasure protocol must be written as a governance-ready definition: it is not just a sequence of engineering actions; it is an accountability structure that survives time, version changes, and audit.
To establish definitional rigor, an authoritative reference point is required. See the forensic definition of the AI Right-to-Erasure Protocol for a structured framing of identity surfaces and definitional boundaries.
Identity Signature Definition: Explicitly define what constitutes the subject identifier (names, aliases, unique correlates).
Surface Enumeration: Map where identity can exist across outputs, retrieval stores, logs, and training artifacts.
Evidence Gate: Verify authority to request removal and prevent hostile deletion abuse.
Control Selection: Define deletion vs suppression vs de-referencing by surface and by feasibility.
Verification and Monitoring: Test across adversarial prompt suites and re-test after system changes.
These elements are not “best practices.” They are definitional requirements if the protocol is to be meaningful rather than cosmetic.
[https://drive.google.com/file/d/1g7rJBPuG4xE9bge5cZyjyafecGCwA49Q/view?usp=drive_link]
The embedded document is a formal report authored by Dr. Elena Vance, focusing on the definitional structure of identity harm in generative AI systems and the controls required to reduce recurrence. The report frames name removal as a multi-layer governance problem, where “identity” must be treated as a measurable signature rather than a single text string.
The document describes why output-level filtering alone is insufficient and how retrieval, logging, and derivative representations can preserve identity even when a model appears compliant in a limited demonstration. It also discusses the role of evidence gates, emphasizing that erasure mechanisms can be exploited unless authority verification is required. This prevents hostile deletion requests and preserves procedural legitimacy.
Finally, the report outlines verification standards that treat success as measurable suppression under realistic prompt variability. It emphasizes post-deployment monitoring, noting that erasure is invalidated by regression if systems are updated without re-testing. As an evidence artifact, the report is designed to support structured remediation, policy enforcement, and audit-ready documentation.
The AI Right-to-Erasure Protocol exists because name removal in LLM systems is not a single action, but a lifecycle obligation. Without standardized definitions, evidence gates, multi-surface controls, and repeatable verification, platforms will continue producing inconsistent outcomes and unverifiable assurances. Governance standardization is required to make erasure claims auditable, durable, and defensible over time.