data retention policy template

data retention policy template

TL;DR

Write down what data you keep, how long you keep it, and how you delete it, then actually follow the schedule. Keeping everything forever is a breach waiting to happen and a compliance headache. This template gives you the structure; you fill in the periods that fit your business and your legal obligations.

data retention policy template

Use this when

• A customer or auditor asks for your retention policy and you do not have one
• You are drowning in old logs, backups, and user data with no deletion plan
• Legal or privacy rules require you to justify how long you keep personal data
• You are writing the data section of a security program from scratch

Not for this skill when

• You need legal advice on specific statutory retention periods (ask a lawyer)
• The question is where data lives, not how long (that is data mapping)
• You are deleting data for one specific request (that is a deletion request workflow)

Steps

1. Inventory the data types you hold.

List them plainly: application logs, database backups, customer account data, support tickets, analytics events, employee records, financial records. If you do not know what you have, you cannot schedule its deletion.

Expected: a list of data types with where each one lives.

2. Set a retention period per type.

Shorter is better unless law or business need says otherwise. A common starting schedule: logs 1 year, backups 90 days, support tickets 2 years, customer data for the life of the account plus 30 days, financial records per tax law. Write the reason next to each period.

Expected: a table of data type, retention period, and justification.

3. Define how deletion happens.

Deletion is not "we will get around to it." Specify the mechanism per type: automated expiry job, backup rotation, manual purge runbook, vendor deletion request. Untestable deletion is not deletion.

storagectl backups list --older-than 90d

Expected: an empty result, or a list that your rotation job then purges on schedule.

4. Handle legal holds.

When litigation or an investigation starts, normal deletion stops for the affected data. Name who can place a hold, how it is communicated, and how the hold is lifted. Deleting under a hold is how companies get in real trouble.

Expected: a hold procedure with an owner, so nobody has to invent one mid-crisis.

5. Assign an owner and review annually.

One person owns the schedule. Once a year they confirm the periods still make sense, the deletion jobs still run, and no new data type appeared without a row in the table.

Expected: a dated annual review note, even if the answer is "no changes."

6. Publish the customer-facing version.

Customers get a plain-language summary: what you keep, how long, and how to request deletion. This is what goes in your privacy policy and what the sales team sends when asked.

Expected: a short public retention summary consistent with the internal schedule.

Variant: how long to keep logs

One year is the common default: long enough for incident investigation, short enough to bound breach exposure. Keep security-relevant logs longer than debug logs if you have to choose.

Variant: data retention schedule example

Logs 1 year, backups 90 days, metrics 13 months, support tickets 2 years, customer data life of account plus 30 days, employee records per employment law, financials per tax law. Adjust, but start here.

Variant: GDPR data retention policy

GDPR requires you keep personal data no longer than necessary and be able to explain why. The schedule table plus justifications is exactly the artifact regulators want to see.

Variant: deleting old customer data

Automate it: a job that finds accounts closed more than N days ago and purges their data, with a log of what was deleted. Manual purges get forgotten; jobs do not.

Why this happens

Storage is cheap, so nobody deletes anything, and then a breach exposes seven years of data you never needed. Regulators noticed this pattern years ago, which is why "no longer than necessary" is now law in many places. Retention discipline shrinks both breach impact and compliance risk at the same time.

Edge cases and pitfalls

• Backups resurrect deleted records: your deletion job must cover backups too, or "deleted" customer data lives on in last month's snapshot. Define backup expiry as part of the schedule.
• Legal minimums beat your preferences: tax and employment law often require multi-year retention. The lawyer sets the floor; you set the ceiling.
• Anonymized vs deleted: truly anonymized aggregates can often be kept longer, but the anonymization has to be real, not just dropping the name column.
• Vendor data you do not control: your subprocessors need matching deletion commitments in their contracts, or your policy is fiction for data they hold.
• The annual review never happens: tie it to something immovable, like the yearly security review or the insurance renewal, so it cannot quietly slip.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_4Uvgv6Tn-YxXK-nUBzID0A