High-risk AI without accuracy, robustness, and AI-specific security measures

Declare accuracy metrics per model version, add a fail-safe fallback, gate retraining on verified labels, and defend against poisoning, adversarial input and tampering.

Four pieces for the high-risk system. An evaluation report per model version with the declared accuracy metrics and thresholds, and a CI step that blocks promotion when they regress. A fallback in the serving path (rules-based decision or human review) when the model errors, times out or returns low confidence. For systems that keep learning, a retraining pipeline that accepts only human-verified labels, never the model's own predictions or LLM outputs, and promotes a new model only after holdout, drift and bias checks; plus AI-specific security controls such as hash-verified training data and model artifacts, safe serialization formats, adversarial and out-of-distribution input checks, and rate limits against extraction.

Where it goes: 11 CI/CD pipeline, 1 application source code, 12 repository artifacts, 13 tests and evals.

What reviewers look for: an eval report with declared metrics tied to the deployed model version and a CI gate on it; a fallback branch in the inference handler; no partial_fit on predictions or training_examples.append of model outputs, with retraining limited to label_source == 'human' and gated by holdout, drift and bias checks; integrity checks (checksums or signatures) before loading training data and weights.

y_pred = model.predict(X_batch)
model.partial_fit(X_batch, y_pred)        # learns from its own outputs

rows = feedback.fetch(batch_ids, label_source='human')    # verified labels only
if rows:
    candidate = copy.deepcopy(model)
    candidate.partial_fit(rows.X, rows.y)
    if passes_holdout_drift_bias(candidate, holdout):       # gate before promotion
        model = candidate

Engineering guidance, not legal advice. Each provision below may add its own details (a cadence, a deadline, a required notice element): open it for those.