SOC 2 Type II provider failover and degradation architecture for enterprise LLM deployment — Reference Architecture

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

The control that resolves this is a Labelled Degradation Ladder, 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 REST over HTTPS, and ships with a standard-library implementation that runs as written.

The request path

Labelled Degradation Ladder — the request path, with the decision points that must exist before the first model call:

flowchart LR
    REQ[Request + min_quality] --> T1{Tier 1 breaker closed?}
    T1 -->|open| T2{Tier 2}
    T1 -->|closed| CALL1[Call tier 1]
    CALL1 -->|fails| BRK[Record failure<br/>maybe open breaker] --> T2
    T2 -->|below min_quality| REFUSE[Refuse: no acceptable tier]
    T2 -->|ok| CALL2[Call tier 2] --> LAB[Label response<br/>degraded=true]

Modular implementation framework

1. Quality-ranked tiers — Explicit integer quality per provider, not an implicit order.
2. Circuit breaker — A failing tier leaves rotation instead of being retried into the ground.
3. Half-open probe — One probe after cooldown; recovery is tested, not assumed.
4. Tier label on response — Every answer names its tier and whether it was degraded.
5. Quality floor per caller — A caller declares a minimum; below it is a refusal.
6. Degradation events — Each failover recorded — the signal the incident needs.

Enterprise implementation parameters

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

Why this blocks production

When the primary provider degrades, traffic fails over to a weaker model and the answers keep flowing. Nobody downstream is told the quality changed, so the incident is discovered in complaints.

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.

Implementation artifact — Degradation ladder with circuit breaker and quality floor

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 observation-window evidence export.

"""Degradation ladder with a circuit breaker and labelled quality tiers.

The failure: a provider degrades, traffic silently fails over to a weaker
model, and nobody downstream knows the answer came from a lower tier. The
control is that degradation is explicit and labelled on the response, so a
consumer can refuse a tier it cannot accept, and that a failing provider is
taken out of rotation rather than retried into the ground.
"""
import time


class ProviderDown(Exception):
    pass


class CircuitBreaker:
    def __init__(self, threshold: int = 3, cooldown_s: float = 30.0):
        self.threshold, self.cooldown_s = threshold, cooldown_s
        self.failures, self.opened_at = 0, None

    def record_failure(self, now: float) -> None:
        self.failures += 1
        if self.failures >= self.threshold and self.opened_at is None:
            self.opened_at = now

    def record_success(self) -> None:
        self.failures, self.opened_at = 0, None

    def closed(self, now: float) -> bool:
        if self.opened_at is None:
            return True
        if now - self.opened_at >= self.cooldown_s:
            self.opened_at, self.failures = None, 0   # half-open: allow a probe
            return True
        return False


class Tier:
    def __init__(self, name: str, quality: int, call):
        self.name, self.quality, self.call = name, quality, call
        self.breaker = CircuitBreaker()


class DegradationLadder:
    """Tiers in descending quality. The response always names the tier that
    produced it; a caller declaring min_quality gets a refusal rather than a
    quietly worse answer."""

    def __init__(self, tiers: list):
        self.tiers = sorted(tiers, key=lambda t: -t.quality)
        self.events: list[dict] = []

    def complete(self, prompt: str, min_quality: int = 0, now=None) -> dict:
        now = now if now is not None else time.monotonic()
        attempted = []
        for tier in self.tiers:
            if tier.quality < min_quality:
                continue
            if not tier.breaker.closed(now):
                attempted.append({"tier": tier.name, "skipped": "circuit open"})
                continue
            try:
                text = tier.call(prompt)
            except ProviderDown:
                tier.breaker.record_failure(now)
                attempted.append({"tier": tier.name, "failed": True})
                continue
            tier.breaker.record_success()
            degraded = tier is not self.tiers[0]
            self.events.append({"tier": tier.name, "degraded": degraded})
            return {"text": text, "tier": tier.name, "quality": tier.quality,
                    "degraded": degraded, "attempts": attempted}
        raise ProviderDown(
            f"no tier at or above quality {min_quality} available: {attempted}")


if __name__ == "__main__":
    def primary(p): raise ProviderDown("502 from primary")
    def secondary(p): return f"secondary:{p}"
    def local(p): return f"local-small:{p}"

    ladder = DegradationLadder([
        Tier("frontier", 100, primary),
        Tier("secondary", 70, secondary),
        Tier("local-small", 30, local)])

    r = ladder.complete("summarise", now=0.0)
    assert r["tier"] == "secondary" and r["degraded"] is True, r
    assert r["attempts"][0]["failed"], r

    # A caller that cannot accept degradation gets an error, not a worse answer.
    strict = DegradationLadder([Tier("frontier", 100, primary),
                                Tier("local-small", 30, local)])
    try:
        strict.complete("x", min_quality=90, now=0.0)
        raise AssertionError("served below the declared quality floor")
    except ProviderDown:
        pass

    # The breaker opens after repeated failures and stops the retry storm.
    t = Tier("flaky", 50, primary)
    for i in range(3):
        try:
            t.call("x")
        except ProviderDown:
            t.breaker.record_failure(now=float(i))
    assert not t.breaker.closed(now=3.0), "breaker should be open"
    assert t.breaker.closed(now=40.0), "breaker should half-open after cooldown"

    print("Degradation ladder + circuit breaker: OK")
    print(f"  served tier={r['tier']} degraded={r['degraded']}, "
          f"events {len(ladder.events)}")

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']}")

Risk and control 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.

Questions to answer before approval

• 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.
• When the primary provider fails, how does a downstream consumer learn the answer came from a weaker model?
  The response names the tier that produced it and carries a degraded flag. A consumer reading the label can treat a degraded answer differently; silent failover is what this design exists to prevent.
• What stops a failing provider from being retried thousands of times a minute?
  The circuit breaker. After the failure threshold it opens and the tier is skipped entirely until the cooldown elapses, so a failing provider leaves rotation rather than absorbing retries.
• Can a caller that requires top-tier quality be served a degraded answer?
  No. The caller declares a minimum quality and tiers below it are never attempted — the ladder raises instead of serving, which is the correct outcome when no acceptable tier exists.
• How do you know a recovered provider is actually healthy before restoring full traffic?
  The breaker goes half-open after cooldown and admits a single probe. Full rotation is restored only after that probe succeeds, so recovery is tested rather than assumed.
• What prevents failover from moving data outside the contracted region?
  Each tier carries a residency attribute and policy excludes out-of-region tiers from the ladder, so a failover cannot quietly relocate processing.

Adjacent problems

• Part of enterprise ai compliance architecture.
• For the related constraint, see ISO/IEC 27001:2022 provider failover and degradation architecture.
• This pattern also governs EU AI Act provider failover and degradation architecture for enterprise.
• This pattern also governs PCI DSS 4.0 provider failover and degradation architecture for enterprise.
• The same control appears in HIPAA provider failover and degradation architecture for enterprise LLM.
• If this is the blocker in front of you: enterprise AI infrastructure review.