SOC 2 Type II vendor evaluation criteria for enterprise AI implementation — 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 Gated Vendor Evaluation Framework, 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 specific problem here is RFP and structured evaluation. The shape below holds p95 ≤ 600ms over gRPC with mTLS, and ships with a standard-library implementation that runs as written.

The control gap

A vendor comparison built as a weighted average lets a supplier average its way past a control it does not have. The scorecard ranks everyone, the committee cannot see which gates were failed, and the decision gets re-litigated after the contract is signed.

What makes RFP and structured evaluation specifically hard

An RFP scored as a weighted average lets a vendor average its way past a control it does not have. The scoring model, not the questions, is usually what makes the evaluation indefensible.

What resolves it is a two-layer scorecard: pass/fail gates, then weights. The failure to design against: the highest-scoring vendor failing security review after award.

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 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.

Deployment parameters

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

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.
• Which criteria are pass/fail, and which vendor failed one?
  Gates are evaluated first and independently of score: SOC 2 Type 2 currency, DPA and subprocessor disclosure, tenant isolation evidence, residency. A failure on any of them disqualifies.
• Which criteria are pass/fail, and which vendor failed one?
  The gate layer is pass/fail: SOC 2 Type 2 currency, DPA and subprocessor disclosure, tenant isolation evidence, and data residency. Gates are evaluated before any weighted scoring, so a vendor cannot average past a control it lacks.
• For each passed gate, what artifact proves it and what is its date?
  Each gate names the artifact that satisfies it — the report, the signed DPA, the architecture document — with its date. A claim is not evidence.
• Is the latency target in the signed agreement with a remedy, or on a status page?
  It must be in the signed agreement with a remedy. A status page is not a commitment; the scorecard scores the remedy, not the promise.
• What does exit look like — specifically, what can you export and has it been rehearsed?
  Export of prompts, embeddings, eval sets and fine-tunes, rehearsed during the pilot rather than promised in the contract. Portability that has never been executed is not portability.
• Was the reference deployment in the same vertical and regulatory regime as ours?
  It must be — a reference in the same vertical and the same regulatory regime, worked through a structured question set, not a vendor-selected happy customer.

The request path

Gated Vendor Evaluation Framework — the request path, with the decision points that must exist before the first model call:

flowchart TB
    V[Vendor responses] --> G{Gates - pass/fail}
    G -->|any gate failed| DQ[Disqualified<br/>with named gates]
    G -->|all gates passed| W[Weighted scoring]
    W --> TCO[3-year fully loaded TCO]
    W --> REF[Reference call<br/>comparable regulated deployment]
    TCO --> R[Ranking]
    REF --> R
    R --> DR[Decision record<br/>score + reason per criterion]
    DQ --> DR
    DR --> SC[Steering committee]

Component breakdown

1. Gate layer — Pass/fail controls: SOC 2 Type 2 currency, DPA, tenant isolation, residency.
2. Weighted layer — Ranks only vendors that cleared every gate.
3. Evidence requirement — Each gate names the artifact that satisfies it, not a claim.
4. TCO model — Fully loaded three-year: licence, run cost, internal effort.
5. Reference protocol — A structured call with a comparable regulated deployment.
6. Decision record — Ranking plus failed gates plus a written reason per score.

The implementation — Weighted scorecard with disqualifying gates

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 gRPC with mTLS; the failure mode it guards against is a half-open HTTP/2 stream silently dropping the deadline. The output you hand the assessor is the observation-window evidence export.

"""Weighted vendor scorecard with disqualifying gates.

Why this shape: a procurement scorecard that is a weighted average lets a vendor
average its way past a control it simply does not have. Real evaluations have
two layers — GATES that are pass/fail, and weights that only ever rank the
vendors that already cleared every gate.

This produces the artifact a steering committee signs: a ranking, the gates each
vendor failed, and a written reason per score.
"""
from dataclasses import dataclass, field


@dataclass(frozen=True)
class Criterion:
    key: str
    weight: float          # relative; normalised at scoring time
    gate: bool = False     # True -> pass/fail, cannot be averaged away
    note: str = ""


DEFAULT_CRITERIA = [
    Criterion("soc2_type2_current", 0.0, gate=True,
              note="Type 2 report inside 12 months, not a Type 1 or a roadmap"),
    Criterion("dpa_and_subprocessors", 0.0, gate=True,
              note="DPA signable as-is and a disclosed subprocessor list"),
    Criterion("tenant_isolation_evidence", 0.0, gate=True,
              note="Architectural isolation, not a WHERE clause"),
    Criterion("data_residency", 0.0, gate=True,
              note="Processing stays in the contracted region"),
    Criterion("exit_and_portability", 2.0,
              note="Export of prompts, embeddings, evals; no model lock-in"),
    Criterion("latency_slo_contractual", 3.0,
              note="p95 in the agreement with a remedy, not a status page"),
    Criterion("integration_depth", 3.0,
              note="Works against the real system of record, not a demo schema"),
    Criterion("implementation_capacity", 2.0,
              note="Named engineers, not a bench claim"),
    Criterion("tco_3yr", 3.0, note="Fully loaded: licence, run, internal effort"),
    Criterion("reference_in_our_vertical", 2.0,
              note="A reference call with a comparable regulated deployment"),
]


class Disqualified(Exception):
    pass


@dataclass
class Vendor:
    name: str
    gates: dict = field(default_factory=dict)    # key -> bool
    scores: dict = field(default_factory=dict)   # key -> 0..5
    reasons: dict = field(default_factory=dict)


def evaluate(vendors, criteria=None):
    """-> (ranked, disqualified). Gates are evaluated first, always."""
    criteria = criteria or DEFAULT_CRITERIA
    gate_keys = [c.key for c in criteria if c.gate]
    weighted = [c for c in criteria if not c.gate and c.weight > 0]
    total_w = sum(c.weight for c in weighted) or 1.0

    ranked, disqualified = [], []
    for v in vendors:
        failed = [k for k in gate_keys if not v.gates.get(k, False)]
        if failed:
            disqualified.append({"vendor": v.name, "failed_gates": failed})
            continue
        got = 0.0
        breakdown = []
        for c in weighted:
            s = float(v.scores.get(c.key, 0))
            if not 0 <= s <= 5:
                raise ValueError(f"{v.name}.{c.key}={s} outside 0..5")
            contribution = (c.weight / total_w) * (s / 5.0)
            got += contribution
            breakdown.append({"criterion": c.key, "score": s,
                              "weight": c.weight,
                              "contribution": round(contribution, 4),
                              "reason": v.reasons.get(c.key, ""),
                              "standard": c.note})
        ranked.append({"vendor": v.name, "score": round(got * 100, 1),
                       "breakdown": breakdown})
    ranked.sort(key=lambda r: -r["score"])
    return ranked, disqualified


if __name__ == "__main__":
    passing = {k: True for k in
               ("soc2_type2_current", "dpa_and_subprocessors",
                "tenant_isolation_evidence", "data_residency")}
    a = Vendor("Vendor A", gates=dict(passing),
               scores={"exit_and_portability": 4, "latency_slo_contractual": 5,
                       "integration_depth": 4, "implementation_capacity": 3,
                       "tco_3yr": 3, "reference_in_our_vertical": 5},
               reasons={"latency_slo_contractual": "p95 450ms with 10% credit"})
    b = Vendor("Vendor B", gates={**passing, "tenant_isolation_evidence": False},
               scores={k: 5 for k in
                       ("exit_and_portability", "latency_slo_contractual",
                        "integration_depth", "implementation_capacity",
                        "tco_3yr", "reference_in_our_vertical")})
    ranked, dq = evaluate([a, b])
    for r in ranked:
        print(f"{r['vendor']}: {r['score']}")
    print("disqualified despite perfect scores:", dq)

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 vendor evaluation.
• Where this interacts with another regime, see SOX vendor evaluation criteria for enterprise AI implementation.
• Where this interacts with another regime, see GDPR vendor evaluation criteria for enterprise AI implementation.
• HIPAA vendor evaluation criteria for enterprise AI implementation applies the same mechanism under a different obligation.
• fedRAMP Moderate vendor evaluation criteria for enterprise AI applies the same mechanism under a different obligation.
• If this is the blocker in front of you: enterprise AI infrastructure review.