PCI DSS 4.0 tracing and token accounting architecture for enterprise LLM deployment — Reference Architecture

Reference architecture · published 2026-10-16 · regulated enterprise · PCI DSS 4.0 · layout spec_first · implementation executed in CI before publish

Deployment parameters

Every value below is a parameter you can hold a vendor or an internal team to. Adjectives are not acceptance criteria.

The control that resolves this is a Complete-Trace Reconciliation Ledger, evaluated before the request reaches a model provider rather than audited afterwards. In regulated enterprise the binding constraint is PCI DSS 4.0, and the question it actually asks is is cardholder data in scope, and can you prove it is not. The shape below holds p95 ≤ 600ms over REST over HTTPS, and ships with a standard-library implementation that runs as written.

The architecture friction point

An answer is disputed weeks after it was given and nobody can say which model version, which documents and which prompt produced it. Traces exist, but they are sampled, so the disputed request is the one that was not kept.

What PCI DSS 4.0 specifically requires

PCI scope is contagious: any system that stores, processes or transmits cardholder data — or can affect the security of one that does — is in scope. The cheapest control is to keep the model tier out of scope entirely.

The artifact that satisfies it is a segmentation test proving the model tier is out of scope. The way teams most often fail it: a PAN reaching a prompt log, dragging the whole inference tier into PCI scope.

Implementation artifact — Trace completeness with spend reconciliation

Standard-library Python, no external dependencies, runs as written. Executed by the publish gate: if it raises, this page does not ship. Wire it behind REST over HTTPS; the failure mode it guards against is a retried POST creating a duplicate record. The output you hand the assessor is the segmentation test report.

"""Trace completeness: every response tied to the inputs that produced it.

The failure: an answer is disputed weeks later and nobody can reconstruct which
model version, which retrieved documents and which prompt produced it. Sampling
traces at 1% guarantees the disputed one is missing, so completeness is the
property to enforce — and untraced spend is the signal that it has broken.
"""
import hashlib


class Trace:
    def __init__(self, request_id: str, model_version: str):
        self.request_id = request_id
        self.model_version = model_version
        self.sources: list[str] = []
        self.prompt_digest = None
        self.output_digest = None
        self.tokens = {"in": 0, "out": 0}

    def bind_prompt(self, prompt: str) -> None:
        self.prompt_digest = hashlib.sha256(prompt.encode()).hexdigest()[:16]

    def bind_output(self, output: str) -> None:
        self.output_digest = hashlib.sha256(output.encode()).hexdigest()[:16]

    def cite(self, source_id: str) -> None:
        self.sources.append(source_id)

    def complete(self) -> bool:
        return all([self.prompt_digest, self.output_digest,
                    self.model_version, self.tokens["in"] > 0])

    def missing(self) -> list[str]:
        gaps = []
        if not self.prompt_digest: gaps.append("prompt_digest")
        if not self.output_digest: gaps.append("output_digest")
        if not self.model_version: gaps.append("model_version")
        if self.tokens["in"] <= 0: gaps.append("token_accounting")
        return gaps


class Ledger:
    """Reconciles traced spend against billed spend. A gap means requests are
    escaping the traced path, which is the thing to find — not a rounding
    error to tolerate."""

    def __init__(self, tolerance: float = 0.01):
        self.traces: dict[str, Trace] = {}
        self.tolerance = tolerance

    def open(self, request_id: str, model_version: str) -> Trace:
        t = Trace(request_id, model_version)
        self.traces[request_id] = t
        return t

    def reconstruct(self, request_id: str) -> dict | None:
        t = self.traces.get(request_id)
        if not t:
            return None
        return {"request_id": t.request_id, "model_version": t.model_version,
                "prompt_digest": t.prompt_digest, "output_digest": t.output_digest,
                "sources": list(t.sources), "tokens": dict(t.tokens),
                "complete": t.complete(), "missing": t.missing()}

    def traced_tokens(self) -> int:
        return sum(t.tokens["in"] + t.tokens["out"] for t in self.traces.values())

    def reconcile(self, billed_tokens: int) -> dict:
        traced = self.traced_tokens()
        gap = billed_tokens - traced
        ratio = gap / billed_tokens if billed_tokens else 0.0
        return {"traced": traced, "billed": billed_tokens, "gap": gap,
                "untraced_ratio": round(ratio, 4),
                "within_tolerance": abs(ratio) <= self.tolerance,
                "incomplete_traces": [r for r, t in self.traces.items()
                                      if not t.complete()]}


if __name__ == "__main__":
    led = Ledger()
    t = led.open("req-1", "gpt-x@2026-09")
    t.bind_prompt("What is our refund policy?")
    t.cite("doc://policies/refunds#v7")
    t.tokens = {"in": 820, "out": 240}
    t.bind_output("Refunds are processed within 14 days.")

    rec = led.reconstruct("req-1")
    assert rec["complete"], rec
    assert rec["sources"] == ["doc://policies/refunds#v7"], rec
    assert rec["model_version"] == "gpt-x@2026-09"

    # An incomplete trace must be visible, not silently acceptable.
    t2 = led.open("req-2", "gpt-x@2026-09")
    t2.tokens = {"in": 100, "out": 50}
    assert not t2.complete() and "prompt_digest" in t2.missing(), t2.missing()

    clean = led.reconcile(billed_tokens=1210)
    assert clean["within_tolerance"], clean
    assert clean["incomplete_traces"] == ["req-2"], clean

    # Requests escaping the traced path show up as an untraced gap.
    leaky = led.reconcile(billed_tokens=4000)
    assert not leaky["within_tolerance"], leaky
    assert leaky["untraced_ratio"] > 0.6, leaky

    assert led.reconstruct("req-missing") is None

    print("Trace completeness ledger: OK")
    print(f"  reconstructed req-1 complete={rec['complete']}, "
          f"untraced ratio clean={clean['untraced_ratio']} "
          f"leaky={leaky['untraced_ratio']}")

The PCI DSS 4.0 control, as code

PAN tokenisation and a scope-reduction proof. This is the artifact an assessor asks for under PCI DSS 4.0 specifically — it is not the same control as the one above, and a system that implements one does not thereby implement the other. It executes under the same publish gate.

"""PAN detection with tokenisation, and a scope-reduction proof.

PCI DSS 4.0 scope is everything that stores, processes or transmits account
data, plus anything connected to it. The cheapest compliance move is not better
controls — it is removing systems from scope. A model gateway that never sees a
PAN is out of scope; one that sees it once is in, along with everything it talks
to.
"""
import hashlib
import re

PAN_RE = re.compile(r"\b(?:\d[ -]?){12,18}\d\b")


def luhn_valid(digits: str) -> bool:
    total, parity = 0, len(digits) % 2
    for i, ch in enumerate(digits):
        d = int(ch)
        if i % 2 == parity:
            d *= 2
            if d > 9:
                d -= 9
        total += d
    return total % 10 == 0


class TokenVault:
    """Format-preserving surrogate. The vault is in scope; nothing it feeds is,
    provided the surrogate is not reversible outside it."""

    def __init__(self, pepper: str):
        self._pepper = pepper
        self._map: dict[str, str] = {}

    def tokenise(self, pan: str) -> str:
        digest = hashlib.sha256(f"{self._pepper}:{pan}".encode()).hexdigest()
        surrogate = "4" + str(int(digest[:15], 16)).zfill(15)[:15]
        self._map[surrogate] = pan
        return surrogate

    def last_four(self, pan: str) -> str:
        return pan[-4:]


class ScopeBoundary:
    def __init__(self):
        self.systems: dict[str, bool] = {}
        self.flows: list[tuple[str, str]] = []

    def add(self, system: str, sees_pan: bool) -> None:
        self.systems[system] = sees_pan

    def flow(self, src: str, dst: str) -> None:
        self.flows.append((src, dst))

    def in_scope(self) -> set[str]:
        scope = {s for s, sees in self.systems.items() if sees}
        changed = True
        while changed:                      # connected-to is also in scope
            changed = False
            for src, dst in self.flows:
                if src in scope and dst not in scope:
                    scope.add(dst); changed = True
                elif dst in scope and src not in scope:
                    scope.add(src); changed = True
        return scope


def redact(text: str, vault: TokenVault) -> tuple[str, int]:
    found = 0
    def _sub(m):
        nonlocal found
        raw = re.sub(r"[ -]", "", m.group(0))
        if not luhn_valid(raw):
            return m.group(0)
        found += 1
        return f"tok_{vault.tokenise(raw)}(last4={vault.last_four(raw)})"
    return PAN_RE.sub(_sub, text), found


if __name__ == "__main__":
    vault = TokenVault(pepper="hsm-managed")
    text = "Charge card 4111 1111 1111 1111 for invoice 9912, ref 1234567890123456"
    out, n = redact(text, vault)
    assert n >= 1 and "4111 1111 1111 1111" not in out, out
    assert "last4=1111" in out, out

    # A number that fails Luhn is not a PAN and must not be mangled.
    benign, n2 = redact("order 1234 5678 9012 3450 placed", vault)
    assert n2 == 0 and "1234 5678 9012 3450" in benign, benign

    sb = ScopeBoundary()
    sb.add("checkout", True); sb.add("token_vault", True)
    sb.add("llm_gateway", False); sb.add("analytics", False)
    sb.flow("checkout", "token_vault"); sb.flow("token_vault", "llm_gateway")
    scope = sb.in_scope()
    assert "llm_gateway" in scope, "connected-to is in scope — that is the point"

    clean = ScopeBoundary()
    clean.add("checkout", True); clean.add("token_vault", True)
    clean.add("llm_gateway", False)
    clean.flow("checkout", "token_vault")
    assert "llm_gateway" not in clean.in_scope(), clean.in_scope()

    print("PCI DSS 4.0 tokenisation + scope reduction: OK")
    print(f"  PANs tokenised {n}, naive scope {len(scope)} systems, "
          f"segmented scope {len(clean.in_scope())} systems")

The request path

Complete-Trace Reconciliation Ledger — the request path, with the decision points that must exist before the first model call:

flowchart LR
    REQ[Request] --> OPEN[Open trace<br/>model version pinned]
    OPEN --> CTX[Bind prompt digest + sources]
    CTX --> CALL[Model call] --> OUT[Bind output digest + tokens]
    OUT --> LED[(Ledger)]
    BILL[Provider invoice] --> REC{Reconcile}
    LED --> REC
    REC -->|gap| ALERT[Untraced traffic — find the path]
    REC -->|matches| OK[Completeness holds]

Implementation components

1. Per-request trace — Model version, prompt digest, output digest, sources, tokens.
2. Completeness check — A trace missing any required field is reported, not accepted.
3. Source citation list — Which retrieved documents were in context for this answer.
4. Token accounting — Input and output tokens recorded at the call site.
5. Spend reconciliation — Traced tokens against billed tokens; a gap means untraced traffic.
6. Reconstruction API — Given a request id, return everything needed to re-derive the answer.

Risk register with acceptance criteria

Each row carries an acceptance parameter rather than an adjective, because "low latency" cannot be signed off. Retention here is bound by per the data class retention schedule, enforced automatically.

Review questions and answers

• Is the inference tier in PCI scope, and what segmentation test proves it is not?
  Cardholder data is tokenised before the model boundary and the segmentation is evidenced by an annual penetration test, so the tier stays out of scope by design.
• A customer disputes an answer from six weeks ago. What can you reconstruct?
  The model version, prompt digest, output digest, the retrieved sources in context, and token counts — enough to re-derive the answer rather than argue about it.
• How would you know if some requests never reach the traced path?
  Reconciliation. Traced token spend is compared against the provider invoice; a gap beyond tolerance means traffic is escaping the traced path, and that gap is the thing to chase.
• Which model version produced a specific output, and how is that known rather than inferred?
  It is pinned on the trace when the trace opens, before the call. Inferring it afterwards from deployment history is exactly how the wrong version gets attributed.
• Does the trace store the raw prompt, and what does that mean for personal data?
  Digests by default, with raw retained only where there is a lawful basis. The digest is enough to prove which prompt produced which output without storing the text.
• What does tracing add to the latency of the request it traces?
  Nothing on the critical path: trace writes are buffered and asynchronous, and the added p95 is measured against the agreed budget rather than assumed to be negligible.

Adjacent problems

• Part of enterprise ai compliance architecture.
• The same control appears in PCI DSS 4.0 data governance framework for enterprise LLM deployment.
• This pattern also governs PCI DSS 4.0 provider failover and degradation architecture for enterprise.
• Where this interacts with another regime, see PCI DSS 4.0 reference architecture for enterprise LLM deployment at scale.
• For the related constraint, see PCI DSS 4.0 prompt injection defence architecture for enterprise LLM.
• If this is the blocker in front of you: enterprise AI infrastructure review.