Model risk documentation pack

Putting Sentinel in your model inventory.

Written for the model risk function that has to classify, validate and monitor a purchased control. It covers the classification question first — because under SR 26-2 the honest answer may be that Sentinel is not a model at all — then gives you the evidence for whichever way you decide.

Applies to  Sentinel 0.2.0
Frameworks  SR 26-2 (Fed/OCC/FDIC, 17 Apr 2026) · Treasury FS AI RMF · NIST AI RMF
Every artefact below  retrievable from your own instance, not supplied by us as a claim

1. Start with the classification question

Most vendors hand a model risk team a model card and let them work it out. The more useful thing we can do is tell you what the guidance actually says about software like ours, and give you the evidence to make the determination yourself.

“The term ‘model’ in this guidance excludes simple arithmetic calculations, such as those found within spreadsheets, as well as deterministic rule-based processes and software where there are no statistical, economic, or financial theories underpinning their design or use.”

SR 26-2, page 3

Sentinel’s enforcement path is comparison operators, an AST expression evaluator, condition types and fixed verdict precedence. No statistical, economic or financial theory underpins it. On the face of the guidance, that falls inside the exclusion.

That determination is yours, not ours

We are not going to tell you Sentinel is out of scope of your MRM programme. A vendor asserting its own exclusion is worth nothing to an examiner. What we can do is make the evidence checkable, and be explicit about the one component that complicates the picture.

2. The evidence, and the complication

Fourteen pipeline components are classified. Twelve are deterministic. Two are genuinely statistical, and we say so rather than rounding down.

ComponentClassificationCan affect a verdict?Disableable
Rule engine, operators, expressions, verdict precedenceDeterministicYes
Injection, PII, toxicity, safety, hallucination, faithfulness, data qualityDeterministicYesIndividually
Governance mode transformDeterministicYes
Anomaly scorerStatistical — running mean and variance, z-scoreYesanomaly_enabled
Drift detectorStatistical — distribution comparisonNo — reporting only

One statistical component can influence a verdict, and it is disableable. With anomaly_enabled=false, every component in the decision path is deterministic. That is the configuration in which the SR 26-2 exclusion argument is strongest, and it is a configuration you set, not a claim we make.

The classification is verified against the code, not maintained by hand

A hand-written statement that “our engine is deterministic” is worth what any vendor assertion is worth. Ours is checked by an AST scan: each declared-deterministic module is parsed and searched for statistical constructs — imports of numpy, scipy, statistics, random; calls to math.sqrt, exp, log, erf; identifiers naming variance, standard deviation, z-scores or Welford’s algorithm.

Paired tests assert the declarations match reality in both directions, so a component that starts computing a variance fails our build rather than quietly invalidating this page. A component declared statistical that contains no statistics also fails.

# Retrieve the classification from your own instance
curl $BASE/governance/determinism/classification

# And the attestation for your current configuration
curl $BASE/governance/determinism/attestation

The classification endpoint is configuration-independent, so you can export it before deciding anything. The attestation reflects the config actually running.

3. Whichever way you classify it

If you conclude Sentinel is not a model

  • Section VII still applies. Vendor and third-party product obligations survived the SR 26-2 rewrite intact and are not conditional on the product being a model.
  • Record the determination and its basis. Export the determinism classification and attestation as the supporting evidence, with the configuration they were taken under.
  • Re-check on upgrade. The attestation is config-dependent; enabling the anomaly scorer changes the answer.
  • Your agents are still in scope even if the gate is not. Sentinel governs them; it does not absorb their classification.

If you classify it as a model anyway

  • Conservative and defensible. Several institutions will, and we would rather support that than argue.
  • Register it in your inventory with the model card fields in §4.
  • Assign materiality from exposure and purpose, which sets your validation cadence under SR 26-2 rather than a fixed annual cycle.
  • Use the validation evidence in §5 as the outcomes-analysis and ongoing-monitoring inputs.

4. Inventory record

Fields for your model inventory. Sentinel keeps its own inventory for the agents it governs, so the shape will be familiar.

FieldValue
NameSentinel — AI Decision Control Plane
VendorIntegrators AI LLC (Integrity Stack)
Version0.2.0 — confirm via GET /health on your instance
PurposePre-execution policy enforcement on AI-agent decisions
MethodologyDeterministic rule evaluation; one optional statistical component (§2)
InputsProposed action parameters, optional model output and retrieved context
OutputsAPPROVE / BLOCK / FLAG / ESCALATE, with reasons and a hash-chained record
DeploymentSelf-hosted in your infrastructure; no data egress in the decision path
Development dataNone. No training data exists — nothing is fitted. Rules are authored by you.
OwnerYour policy owner. Governance-mode changes record a named accountable actor.
Downstream relianceThe agent types you route through it — export via GET /agents

The development data row is the one most inventory templates have no good answer for with an AI vendor. Ours is that there is no training corpus, no fitted parameter and no retraining schedule, because nothing was learned. Confidence values in the security stages are hand-authored constants, which is a design choice with real limitations — see the measured detection rates.

5. Validation evidence

SR 26-2 keeps three validation components: conceptual soundness, outcomes analysis, and ongoing monitoring. Here is what to collect for each, and where it comes from.

Conceptual soundness

The determinism classification (§2) and its AST verification are the design evidence. The security whitepaper covers architecture, data flow and the threat model. The evidence bundle specification is the published integrity design, with an independent verifier.

GET /governance/determinism/classification

Outcomes analysis — guardrails

Detection performance is measured and published rather than asserted: precision 1.00 and recall 0.03 against a public benchmark, with zero false positives on lending language. The unflattering figure is on the page, along with the two false-positive defects the evaluation found in our own product.

Reproduce it yourself with make eval-injection. See the published eval.

Outcomes analysis — your policy

Guardrail performance is ours to evidence. Whether your rules produce correct verdicts on your traffic is yours, and the product is built to generate that evidence: release-gated eval sets with held-out cases that must pass before a policy change ships, and monthly QA sampling producing a human-versus-gate disagreement rate suitable as a board metric.

GET /governance/eval-sets · GET /monitoring/qa/metrics

Performance and capacity

Latency is measured on a reproducible harness with the hardware and payloads stated: p50 1.3 ms end to end on the heaviest realistic payload. Methodology and known limitations.

make benchmark

Ongoing monitoring

Bias monitoring against the four-fifths rule, drift detection, data-quality scoring per decision, and the QA disagreement rate. Governance-mode and policy changes append to the audit chain with a named actor, so change control is evidenced rather than attested.

GET /monitoring/bias/report · GET /audit/chain/verify

Change control and testing

Over 2,300 automated tests run on every change, including the adversarial corpus and the published eval. Tests assert that our own verifier and an independent reimplementation agree, so a change silently altering the integrity contract fails the build.

6. Section VII — vendor obligations

These survived the SR 26-2 rewrite intact and apply to a vendor product regardless of how you classify it.

ObligationWhat we supply
Validation by internal or outside partiesEvidence in §5, all reproducible on your instance. No third-party validation has been commissioned — see §7.
Understanding of conceptual soundness, design and development dataDeterminism classification, whitepaper, published evidence spec. No development data exists (§4).
Ongoing monitoring and outcomes analysisBias, drift, data quality, QA sampling, release gates — all producing exportable evidence.
Customisations documented, justified and evaluatedYour rules and thresholds are the customisation. Rules are versioned, threshold changes are recorded, and release-gated evals are the evaluation step.

Sentinel also runs a Section VII register for your vendor products, inheriting each product’s materiality from the agent types it feeds. That is a separate capability from this pack — see the SR 26-2 brief.

7. What we cannot give you

  • Independent third-party validation. None has been commissioned. Our evidence is reproducible by you, which is a different and weaker thing than an outside party having reproduced it.
  • A penetration test report. Not commissioned. Internal adversarial testing exists in the suite and is not the same thing.
  • SOC 2 or ISO 27001. Not held. Current posture and the trigger for starting.
  • Backtesting against your portfolio. We have no customer data. A shadow-mode pilot is how that evidence gets generated, and it generates it in your environment.
  • A benchmark against competing products. We have not run one and will not publish a comparison we cannot reproduce.
  • An assertion that we are out of scope. §1. That determination is yours.

If your MRM template has a field this pack does not answer, send it. We would rather add it here than answer it once verbally.

Design partners

Bring your MRM template.

The fastest validation evidence is a shadow-mode pilot: it produces outcomes analysis on your own traffic, with zero enforcement risk while it runs.