The Zero-Trust Data Sanitization (ZTDS) Protocol for Frontier Artificial Intelligence Ingestion

1.1. Problem Statement: The Cloud DLP Paradox

Legacy enterprise data loss prevention (DLP) systems rely on intermediary cloud proxies or central inspection gateways. When applied to modern generative AI workloads, this architecture introduces four critical vulnerabilities:¶

1. Network Latency and Perceptual Lag: Cloud-routed DLP inspection adds round-trip network delays ranging between 150 ms and 400 ms per inference invocation, degrading real-time conversational streaming and sub-agent coordination.¶
2. Single Point of Data Breach: Routing cleartext organizational payloads through intermediary proxy infrastructure creates a centralized target for state-sponsored adversaries and infrastructure compromises.¶
3. Regulatory Subprocessor Multiplication: Under European Union General Data Protection Regulation (GDPR) Article 28, introducing a cloud inspection proxy adds a new data processor into the statutory subprocessor chain, requiring bespoke Data Processing Agreements (DPAs) and cross-border transfer assessments.¶
4. Transport Layer Interception Failure: As client-to-inference transport increasingly adopts end-to-end authenticated encryption, traditional network-level middleboxes require intrusive TLS certificate termination, compromising cryptographic integrity.¶

1.2. Architectural Paradigm: Cloud Proxy vs. Host-Native In-Memory Boundary

ZTDS replaces network-boundary inspection with a host-native, client-boundary sanitization topology [ZENODO-ZTDS]. All entity detection, de-identification, and surrogate replacement occur within volatile memory on the originating client device before any TCP/IP socket serialization.¶

Traditional Cloud DLP Architecture:
+--------+  WAN Cleartext  +-----------+  WAN Cleartext  +-------+
| Client | --------------> | Cloud DLP | --------------> | Cloud |
| Device | <-------------- |   Proxy   | <-------------- |  LLM  |
+--------+  (Risk/Latency) +-----------+ (Audit Friction)+-------+

ZTDS Zero-Trust Endpoint Architecture:
+-----------------------------------+
| Client Host Execution Perimeter   |
| +-----------+     +-------------+ |  Sanitized WAN   +-------+
| | Cleartext | --> |  In-Memory  | | ---------------> | Cloud |
| | Payload S |     | Engine T(S) | | <--------------- |  LLM  |
| +-----------+     +-------------+ |  Surrogate WAN   +-------+
|       ^                  |        |
|       | Local Inverse v           |
|   [Volatile Session Map R in RAM] |
+-----------------------------------+

1.3. Requirements Language

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.¶

1.4. Terminology

Sanitization Engine:

A deterministic computational module that identifies sensitive entities within unstructured text and substitutes them with synthetic surrogate tokens.¶

Surrogate Token:

A non-sensitive, type-preserving synthetic placeholder (e.g., "[NAME_1]", "[EMAIL_2]") that preserves grammatical structure and semantic roles for downstream language models.¶

Session Mapping Dictionary (R):

A temporary bidirectional lookup table mapping surrogate tokens to original cleartext entities, maintained exclusively in volatile host memory.¶

De-Tokenization (Re-identification):

The reverse mapping process executed locally on the client to restore original cleartext values into model completions before user display or local application consumption.¶

Airplane Mode Audit:

A formal compliance procedure verifying that 100% of data sanitization and de-tokenization operations execute successfully while all network interfaces are physically or logically disconnected.¶

2.1. Formal Model Definition

Let an input text prompt or agent payload P be a finite sequence of tokens partitioned into non-sensitive tokens U and sensitive tokens S:¶

P = U UNION S, where U INTERSECT S = EMPTYSET¶

Where S = {s_1, s_2, ..., s_k} represents discrete identifying entity substrings matching statutory, medical, financial, or organizational classification taxonomy.¶

Under the ZTDS protocol:¶

1. An in-memory sanitization transformation T: S -> M is executed locally: M = {m_1, m_2, ..., m_k} where each surrogate token m_i is an orthogonal synthetic label preserving entity syntax.¶
2. The sanitized payload transmitted across the external network perimeter contains strictly U UNION M: Egress(S) = 0 bytes¶
3. The ephemeral session map R = {(m_i, s_i) : 1 <= i <= k} is retained strictly in volatile Random Access Memory (RAM), bound exclusively to the local execution thread or tab context.¶
4. Upon receipt of the model inference response P_out containing surrogate tokens M, an inverse transformation T^(-1) is evaluated locally: P_final = T^(-1)(P_out, R)¶
5. Upon session completion, window close, or process termination, R is deterministically erased from RAM.¶

2.2. The Four Fundamental Invariants

Any implementation claiming conformance with the ZTDS standard MUST satisfy four non-negotiable invariants:¶

3.1. Phase 1: Ingestion and Lexical Boundary Parsing

The cleartext payload P is received by the local host interface. The engine performs lexical scanning across standardized entity taxonomy classes (Section 4). To prevent catastrophic regex backtracking (ReDoS), all evaluation expressions MUST conform to linear-time deterministic finite automaton (DFA) matching semantics.¶

3.2. Phase 2: Contextual Surrogate Tokenization

Each identified entity s_i is registered and assigned an indexed synthetic surrogate m_i. Surrogate formatting adheres strictly to bracketed syntactic labels (e.g., "[NAME_1]", "[IBAN_1]"). If the identical entity string s_i recurs multiple times within the same payload, the engine MUST map all occurrences to the identical surrogate token m_i to preserve coreference resolution for downstream language models.¶

3.3. Phase 3: Ephemeral Session Map Allocation

The association pair (m_i, s_i) is committed to an in-memory hash map R. The map structure is tagged with a cryptographically secure random session identifier and marked for volatile lifecycle management.¶

3.4. Phase 4: Upstream Transmission and Inference

The sanitized payload P_sanitized = U UNION M is serialized and transmitted over TLS to the remote foundation model inference provider. Because raw identifying strings never leave the client boundary, the remote provider's logging infrastructure, fine-tuning pipelines, and prompt caching systems ingest strictly synthetic surrogate identifiers.¶

3.5. Phase 5: Local De-Tokenization (Re-identification)

Upon receipt of the inference response P_out from the foundation model, the client runtime parses the stream for surrogate token patterns. Each occurrence of m_i is matched against local session map R and replaced with corresponding original value s_i:¶

Input Text:    "Transfer $50k from Alice Smith acct 12345678"
Sanitized:     "Transfer $50k from <tt>[NAME_1]</tt> acct [IBAN_1]"
Model Output:  "Confirmation: scheduled transfer for [NAME_1]."
Reconstructed: "Confirmation: scheduled transfer for Alice Smith."

The end user or local consumer receives complete grammatical fidelity without any cleartext exposure to the cloud model provider.¶

3.6. Phase 6: Deterministic Memory Erasure

Upon user session termination, tab close, or explicit pipeline teardown, the host runtime MUST execute a zero-fill overwrite or release of the memory buffer hosting R. In runtimes lacking manual memory management (e.g., JavaScript engines), object references MUST be nullified immediately, and WeakMap structures SHOULD be utilized to allow instantaneous garbage collection.¶

5.1. Cipher and Key Derivation Parameters

The cryptographic handoff protocol mandates the following primitives:¶

• Key Derivation Function: Argon2id conforming to [RFC9106]. Minimum operational parameters: Memory = 64 MiB (65536 KiB), Iterations = 3, Parallelism = 1.¶
• Authenticated Symmetric Encryption: XChaCha20-Poly1305 AEAD cipher conforming to [RFC8439] with extended 192-bit (24-byte) nonces generated using a cryptographically secure pseudorandom number generator (CSPRNG).¶
• Associated Data: The AEAD associated data MUST bind the session UUID and expiration timestamp to prevent ciphertext replay and splicing attacks.¶

6.1. The 5-Step Airplane Mode Audit Procedure

1. Initialize the host application or agent runtime with a test corpus containing statutory PII and secrets.¶
2. Physically disconnect or logically disable all network adapters (Wi-Fi, Ethernet, LTE/5G).¶
3. Execute complete document sanitization. Verify that output contains strictly surrogate tokens.¶
4. Simulate a model response containing surrogate tokens and execute local de-tokenization. Confirm 100% string restoration.¶
5. Inspect host operating system socket tables to confirm exactly zero packets were emitted during the entire test sequence.¶

9.1. Prompt Injection and Training Data Exfiltration

Adversaries executing indirect prompt injection attacks against LLMs attempt to manipulate model context into revealing private user records. Under ZTDS, because raw PII never enters the model context window, prompt injection attacks cannot exfiltrate original credentials; the attacker can at most observe synthetic surrogate labels.¶

9.2. Local Host Memory Inspection and Swap Analysis

If an adversary achieves root-level compromise of the client operating system, they may inspect volatile memory buffers. To mitigate this, compliant implementations SHOULD use memory locking (e.g., mlock on POSIX systems) to prevent volatile memory from being paged to unencrypted swap disks, and explicitly zero memory buffers upon deallocation.¶

9.3. Surrogate Token Collision and Ambiguity

If an input prompt naturally contains text matching the surrogate regex syntax (e.g., a software tutorial discussing "[NAME_1]"), the engine MUST escape or disambiguate literal brackets using namespace prefixes to prevent accidental de-tokenization collision.¶