Safety, privacy, and governance · Lesson 5 of 15 · 8 min

Check data retention

Retention describes how long data is kept and for what purpose. A retention setting should match policy and actual deletion behavior, not just a label in a form.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-safety-governance-005.json. Separate the recorded facts from your assumptions.
Hint & worked example
Retention describes how long data is kept and for what purpose. A retention setting should match policy and actual deletion behavior, not just a label in a form.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-safety-governance-005.json
```
Common mistake to avoid: A policy statement automatically changes the configured retention.
2
Isolate the deciding signal
Select the configured_days field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Reconcile the 30-day setting with the seven-day policy and verify deletion behavior.
A successful JSON query isolates configured_days.
```
jq .configured_days /opt/ai-lab/cases/aiops-safety-governance-005.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: reconcile-retention, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-safety-governance-005-decision.txt.
Hint & worked example
Reconcile the 30-day setting with the seven-day policy and verify deletion behavior.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'reconcile-retention' > /home/learner/lab/aiops-safety-governance-005-decision.txt
```
Common mistake to avoid: A policy statement automatically changes the configured retention.

Understand the pattern

Retention describes how long data is kept and for what purpose. A retention setting should match policy and actual deletion behavior, not just a label in a form.

Interpret the evidence

Reconcile the 30-day setting with the seven-day policy and verify deletion behavior.

A tempting mistake

A policy statement automatically changes the configured retention.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-safety-governance-005.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "policy_days": 7,
  "configured_days": 30,
  "deletion_verified": false
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .configured_days /opt/ai-lab/cases/aiops-safety-governance-005.json

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'reconcile-retention' > /home/learner/lab/aiops-safety-governance-005-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “reconcile-retention” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Check data retention” justify?Make the change now and gather the missing evidence afterward.Reconcile the 30-day setting with the seven-day policy and verify deletion behavior.A policy statement automatically changes the configured retention.

Apply it elsewhere

A colleague concludes: “A policy statement automatically changes the configured retention.” What should you do?Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.Treat this fixture as a live measurement.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Inspect access boundaries Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision