SOC 2 Type II data governance framework for enterprise LLM deployment — Reference Architecture

Reference architecture · published 2026-10-16 · regulated enterprise · SOC 2 Type II · layout control_review · implementation executed in CI before publish

The control that resolves this is a Classification-Routed Multi-Cloud Estate, evaluated before the request reaches a model provider rather than audited afterwards. In regulated enterprise the binding constraint is SOC 2 Type II, and the question it actually asks is did this control operate effectively for the whole observation window. The shape below holds p95 ≤ 600ms over durable queue (SQS / Kafka) with an outbox, and ships with a standard-library implementation that runs as written.

The architecture friction point

Workloads span several providers and regions, and the decision of which model may see which data lives in prompt text, a wiki page, or a developer's judgement. That is not a control, so the governance board cannot approve the estate even though every individual component is defensible.

What SOC 2 Type II specifically requires

SOC 2 Type II is not a point-in-time assertion. The auditor samples evidence across a 3-12 month observation window and asks whether the control operated on every day of it, not whether it exists today.

The artifact that satisfies it is a sampled, time-ordered population the auditor can draw from. The way teams most often fail it: the control was added mid-window, so the population has a hole and the observation period restarts.

Risk mitigation matrix

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.

Technical parameters

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

What the auditor will ask

• Show me the control-execution population for the full window, and the days where it is empty.
  Every request emits an execution record, so the population is continuous by construction. A gap is an alertable incident, not something discovered during the audit.
• What function decides which destination may see RESTRICTED data, and where does it run?
  The routing gate, evaluated before egress. It is a deterministic function of the data's classification and the destination's attested properties, and it fails closed when no destination is lawful.
• Show me the recorded reason for a denial last week.
  Every allow and every deny is written to the decision log with the reason — which destinations were rejected and on which property — so a denial is explainable months later.
• When a document is embedded, does the embedding carry its classification — and is that enforced on read?
  Yes. The classification tag propagates into every derived store, including embeddings, caches and eval sets, and is enforced on read as well as write.
• What happens when a provider adds a subprocessor you have not disclosed?
  The drift detector alerts when a destination's attested properties change, and routing is re-evaluated against the new attestation. Attestations are versioned data, not a one-off questionnaire.
• How do you prove processing stayed in the contracted region?
  The provider endpoint is pinned per region and the served region is asserted on the response; a mismatch fails the request rather than completing it quietly.

Reference architecture

Classification-Routed Multi-Cloud Estate — the request path, with the decision points that must exist before the first model call:

flowchart TB
    IN[Request payload] --> CLS[Deterministic classifier<br/>regex + Luhn/checksum]
    CLS --> LV{Classification}
    LV -->|PUBLIC / INTERNAL| D1[shared-us]
    LV -->|CONFIDENTIAL| D2[eu-pooled<br/>DPA required]
    LV -->|RESTRICTED| D3[eu-dedicated<br/>zero-retention + subprocessors]
    LV -->|no lawful destination| DN[Deny + reason]
    D1 --> LOG[(Decision log)]
    D2 --> LOG
    D3 --> LOG
    DN --> LOG
    D3 --> LIN[Lineage tag on<br/>embeddings / cache / evals]
    ATT[Destination attestations] -.-> LV
    ATT --> DR[Drift detector]

The parts you have to build

1. Deterministic classifier — Pattern plus validator (Luhn, checksum) so the gate does not cry wolf.
2. Destination attestations — Region, max class, retention, DPA, subprocessor disclosure — as data.
3. Routing gate — First lawful destination in preference order; cost can never override a control.
4. Decision log — A recorded reason for every allow AND every deny.
5. Lineage tags — Classification travels with derived artifacts: embeddings, caches, evals.
6. Drift detector — Alerts when a destination's attested properties change.

Working implementation — Classification-driven routing gate with recorded reasons

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 durable queue (SQS / Kafka) with an outbox; the failure mode it guards against is a consumer crash between side effect and acknowledgement. The output you hand the assessor is the observation-window evidence export.

"""Classification-driven routing gate for a multi-cloud LLM estate.

Why this shape: "which model may see this data" is a governance decision, and
if it lives in prompt text or a developer's judgement it is not a control. It
has to be a deterministic function of the data's classification and the
destination's attested properties, evaluated before egress.

The gate below is the smallest thing that an actual data-governance board will
accept: classification in, allowed destinations out, with a recorded reason for
every allow and every deny.
"""
import re
from dataclasses import dataclass
from enum import IntEnum


class Class(IntEnum):
    PUBLIC = 0
    INTERNAL = 1
    CONFIDENTIAL = 2
    RESTRICTED = 3      # PII / PHI / material non-public


DETECTORS = {
    "email": re.compile(r"[\w.+-]+@[\w-]+\.[\w.]{2,}"),
    "iban": re.compile(r"\b[A-Z]{2}\d{2}[A-Z0-9]{10,30}\b"),
    "card": re.compile(r"\b(?:\d[ -]?){13,19}\b"),
    "ssn": re.compile(r"\b\d{3}-\d{2}-\d{4}\b"),
    "mrn": re.compile(r"\bMRN[:\s-]*\d{6,10}\b", re.I),
}


def luhn(number: str) -> bool:
    """Card detectors without Luhn produce false positives on order numbers,
    and a gate that cries wolf gets switched off."""
    digits = [int(c) for c in re.sub(r"\D", "", number)]
    if len(digits) < 13:
        return False
    checksum, parity = 0, len(digits) % 2
    for i, d in enumerate(digits):
        if i % 2 == parity:
            d *= 2
            if d > 9:
                d -= 9
        checksum += d
    return checksum % 10 == 0


def classify(text: str, floor: Class = Class.INTERNAL) -> tuple:
    """-> (classification, evidence). Deterministic and explainable."""
    found = []
    for name, rx in DETECTORS.items():
        for m in rx.finditer(text or ""):
            if name == "card" and not luhn(m.group(0)):
                continue
            found.append(name)
            break
    return (Class.RESTRICTED if found else floor), sorted(set(found))


@dataclass(frozen=True)
class Destination:
    name: str
    region: str
    max_class: Class
    zero_retention: bool
    dpa_signed: bool
    subprocessors_disclosed: bool


class Denied(Exception):
    pass


def route(text: str, destinations: list, required_region: str = None) -> dict:
    """Pick the first destination whose ATTESTED properties cover the data.

    Order matters: destinations are supplied in preference order (cheapest or
    fastest first) and the gate takes the first one that is lawful, so cost
    optimisation can never override a control.
    """
    level, evidence = classify(text)
    reasons = []
    for d in destinations:
        if d.max_class < level:
            reasons.append(f"{d.name}: max_class {d.max_class.name} < {level.name}")
            continue
        if required_region and d.region != required_region:
            reasons.append(f"{d.name}: region {d.region} != {required_region}")
            continue
        if level >= Class.RESTRICTED and not d.zero_retention:
            reasons.append(f"{d.name}: retains prompts, unusable for {level.name}")
            continue
        if level >= Class.CONFIDENTIAL and not d.dpa_signed:
            reasons.append(f"{d.name}: no DPA on file")
            continue
        if level >= Class.RESTRICTED and not d.subprocessors_disclosed:
            reasons.append(f"{d.name}: subprocessors undisclosed")
            continue
        return {"allowed": True, "destination": d.name,
                "classification": level.name, "evidence": evidence,
                "rejected": reasons}
    raise Denied(f"no lawful destination for {level.name} data: {reasons}")


if __name__ == "__main__":
    dests = [
        Destination("shared-us", "us-east-1", Class.INTERNAL, False, True, False),
        Destination("eu-pooled", "eu-west-1", Class.CONFIDENTIAL, False, True, True),
        Destination("eu-dedicated", "eu-west-1", Class.RESTRICTED, True, True, True),
    ]
    print(route("Summarise this quarter's churn drivers.", dests))
    print(route("Patient MRN 4471902 reports side effects.", dests,
                required_region="eu-west-1"))
    try:
        route("Card 4111111111111111 was declined.",
              [d for d in dests if not d.zero_retention])
    except Denied as e:
        print("correctly denied:", str(e)[:96])

The SOC 2 Type II control, as code

Type II evidence sampling over the observation window. This is the artifact an assessor asks for under SOC 2 Type II 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.

"""SOC 2 Type II evidence sampling over an observation window.

Type II is not "the control exists" — it is "the control operated throughout
the period". An auditor samples days from the window and asks for proof the
control ran on each sampled day. A gap of one day with no evidence is an
exception, so the population must be built from operational records rather
than asserted.
"""
import hashlib
from datetime import date, timedelta


class ObservationWindow:
    """The population an auditor samples from, plus deterministic selection."""

    def __init__(self, start: date, end: date, control_id: str):
        if end < start:
            raise ValueError("window end precedes start")
        self.start, self.end, self.control_id = start, end, control_id
        self._evidence: dict[date, list[str]] = {}

    def record(self, day: date, artifact_ref: str) -> None:
        if not (self.start <= day <= self.end):
            raise ValueError(f"{day} outside observation window")
        self._evidence.setdefault(day, []).append(artifact_ref)

    def population(self) -> list[date]:
        n = (self.end - self.start).days + 1
        return [self.start + timedelta(days=i) for i in range(n)]

    def gaps(self) -> list[date]:
        """Days with no evidence. Each one is a Type II exception."""
        return [d for d in self.population() if not self._evidence.get(d)]

    def sample(self, size: int) -> list[date]:
        """Deterministic, reproducible selection — an auditor must be able to
        re-derive the same sample from the same seed rather than trust ours."""
        pop = self.population()
        size = min(size, len(pop))
        seed = f"{self.control_id}:{self.start}:{self.end}"
        scored = sorted(
            pop, key=lambda d: hashlib.sha256(f"{seed}:{d}".encode()).hexdigest())
        return sorted(scored[:size])

    def evidence_for(self, day: date) -> list[str]:
        return list(self._evidence.get(day, []))

    def attestation(self, size: int = 25) -> dict:
        sampled = self.sample(size)
        missing = [d for d in sampled if not self._evidence.get(d)]
        return {
            "control_id": self.control_id,
            "window": f"{self.start}..{self.end}",
            "population_days": len(self.population()),
            "sampled_days": len(sampled),
            "exceptions": [str(d) for d in missing],
            "opinion": "unqualified" if not missing else "qualified",
        }


if __name__ == "__main__":
    start, end = date(2026, 1, 1), date(2026, 3, 31)
    w = ObservationWindow(start, end, "CC6.1-tenant-isolation")
    for d in w.population():
        w.record(d, f"s3://evidence/{d}/isolation-check.json")
    clean = w.attestation()
    assert clean["opinion"] == "unqualified", clean
    assert clean["population_days"] == 90, clean

    # One missing day is one exception — and it must survive sampling to matter.
    w2 = ObservationWindow(start, end, "CC6.1-tenant-isolation")
    sampled = set(w2.sample(25))
    for d in w2.population():
        if d != sorted(sampled)[0]:
            w2.record(d, "ok")
    assert w2.attestation()["opinion"] == "qualified", w2.attestation()
    assert len(w2.gaps()) == 1, w2.gaps()

    # The sample must be reproducible from the same seed.
    assert w.sample(25) == ObservationWindow(start, end,
                                             "CC6.1-tenant-isolation").sample(25)
    print("SOC 2 Type II evidence sampling: OK")
    print(f"  population {clean['population_days']} days, "
          f"sample {clean['sampled_days']}, opinion {clean['opinion']}")

Related architectures

• Part of enterprise ai data governance.
• For the related constraint, see EU AI Act data governance framework for enterprise LLM deployment.
• For the related constraint, see fedRAMP Moderate data governance framework for enterprise LLM deployment.
• Where this interacts with another regime, see ISO/IEC 27001:2022 data governance framework for enterprise LLM deployment.
• Where this interacts with another regime, see PCI DSS 4.0 data governance framework for enterprise LLM deployment.
• If this is the blocker in front of you: enterprise AI infrastructure review.