Set release boundaries for the model-run dictionary

Operational risks for the model-run record dictionary center on serializing secrets (API keys) into the run record JSON, plus the earlier failure mode (KeyError on missing version, or silent overwrite of accuracy with a string). Safety lives in executable gates, allowlists, redaction, and a named owner—not in a warning paragraph under an unsafe function.

SECRET_KEYS={'api_key','token','password'}
def public_record(run):
    return {k:v for k,v in run.items() if k not in SECRET_KEYS}
raw={'name':'ready-v1','accuracy':0.91,'ready':True,'owner':'ml-lab','api_key':'sk-live'}
print(public_record(raw))

Expected evidence: record without api_key. A failed assertion means stop, investigate, and do not publish the model-run dictionary.

Name who can disable the feature, what data is retained, and how to roll back to the last known good artifact. Pin the reviewed configuration (versions, thresholds, allowlists) so “what shipped” is reconstructable for python-dictionaries.

Write the release blocker as a predicate, not a feeling: “Do not ship the model-run dictionary if serializing secrets (API keys) into the run record JSON.” Pair it with a passing control that shows the reviewed configuration still works for name='ready-v1', accuracy=0.91, ready=True, owner='ml-lab'. Name an owner and a rollback handle (git tag, docs_version, previous image).

Security pages must not paste the happy-path demo. If your gate code looks like the implementation page, replace it with a deny/allow check aimed at serializing secrets (API keys) into the run record JSON.

State the data retention rule in one line (what is stored, for how long, who can read it). Then state the kill switch (env flag, config pin, or feature owner). The model-run dictionary is not shippable without both, even when assert required keys; .get('version','unversioned') returns a string looks healthy.

At the safety and operations stage for python-dictionaries, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about name='ready-v1', accuracy=0.91, ready=True, owner='ml-lab' that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: a handwritten field list before coding the dict.

For this page specifically, success looks like an executable deny gate for the lab-specific threat while still centering the user decision to store one training run so later code can read named fields without positional guessing. If you cannot point to a file, command, or assertion that proves that for the model-run dictionary, stay on this page instead of advancing.

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