AI Coding Assistant Log Retention for GPL-3.0 and AGPL-3.0 Claims

Why retention is an evidence-design question

A GPL-3.0 or AGPL-3.0 claim against a software vendor usually argues that an AI-suggested snippet reproduced copyleft-licensed code and that the vendor shipped it without meeting the license terms. The claim may surface years after the code was written, through customer diligence, an M&A review or litigation. By then the question is not how many days of logs exist. It is whether the vendor can show what was suggested, what was accepted, where it came from, and which policy applied when the decision was made.

That reframes the work. A generic retention number, such as 90 days or one year, says nothing about when a claim can arrive. Retention should be anchored to claim exposure windows, and the records worth keeping should be chosen for their value as evidence, not for how easy they are to collect.

AGPL-3.0 adds a deployment dimension. Its obligations can attach when users interact with software over a network, so for hosted products the versions that were live, and the code in them, matter for as long as the vendor may have to answer for those versions.

Which records are material to a copyleft claim

Five categories are the working set: prompts, assistant suggestions, accepted code, license and origin metadata, and policy decisions. They do not carry equal weight in a defense.

Accepted code and its link to a commit and release show what actually shipped. License and origin metadata show whether a match to known open source code was identified, and under which license. Policy decisions show the rule in force and the action taken, whether allow, redact, block or escalation to a human. Together these answer the core questions a claimant, auditor or acquirer will ask.

Prompts and raw suggestions are the bulkiest category and the most likely to contain confidential code or customer information. Much of their evidentiary value can be preserved by the derived records above, which is why they are the best candidates for minimization. Whether a given prompt set contains nonpublic or contractually protected information is a question for counsel and for customer contracts, and the answer should shape how long raw interaction data is held.

A tiered retention model

Set retention per tier and record the rationale for each period, so the schedule can be defended if challenged. The table shows how the tiers differ in purpose and burden.

• Tier 1: Interaction-level evidence: Prompts, raw suggestions and session context. Highest confidentiality and storage burden, lowest standalone value. Keep for a short, reviewed period, redact where possible, and delete on schedule with deletion evidenced.
• Tier 2: Code provenance records: Origin, license and security context linked to accepted code, commit and release. Core defense evidence. Retain for the supported life of the release plus the claim exposure window.
• Tier 3: Policy-decision records: Policy version, the decision applied (allow, redact, block, escalate), time and approver where relevant. Compact and low in sensitive content, so long retention is inexpensive. Retain at least as long as Tier 2.

Deriving the period from exposure windows

For Tier 2 and Tier 3, work from the longest of three inputs rather than a single default.

First, the product release lifecycle. Evidence about a release should outlast the period in which that release is supported or deployed, since that is when a claim about it remains practically relevant. Second, the limitation periods that counsel identifies for copyright and license-breach claims in the jurisdictions where the vendor sells and operates. These vary, and counsel should confirm them rather than the compliance team assuming them. Third, contractual audit obligations. Customer agreements often set their own record-availability terms, and these can exceed what a limitation analysis alone would suggest.

The working rule is: retention end date equals the end of supported life, plus the applicable limitation period, or the longest contractual audit term if that is later. Add the ability to place a legal hold that suspends deletion when a claim, diligence request or dispute is anticipated. Revisit the calculation when products reach end of support, because the clock for that release then starts to run down.

Insurance-sector customer and regulatory expectations

Vendors serving carriers and brokerages face audit expectations that flow down from insurer obligations. The NAIC AI Model Bulletin has a section on data and record retention. As of November 2026, it is reported to tell insurers to expect questions about AI programs during regulatory investigations and market conduct actions, and to retain documentation on AI systems, including data and models developed by third parties. The bulletin applies to insurers holding certificates of authority, so its reach to vendors is practical and contractual: a carrier that must retain documentation about a third-party AI system will ask its vendor for the material to do so.

Customers may also cite NYDFS Circular Letter 7 and Colorado SB 24-205 in their questionnaires. Rather than building to each regime separately, map each customer's stated requirements to the three tiers and identify which deliverables they actually depend on, usually provenance and policy-decision evidence rather than raw prompts.

NAIC working materials are also moving toward a third-party data and models framework. One draft control, covering assurance, retention and attestation, lists a retention schedule and an examination index as evidence. These are draft comment materials, not binding requirements, but they point the same way: a documented schedule, and records that can be located and produced on request.

Balancing long retention against minimization

Longer retention of evidence conflicts with data-minimization and confidentiality duties. Data-security texts such as the FTC Safeguards Rule and the NAIC Insurance Data Security Model Law call for periodic review of retention schedules to avoid keeping unnecessary data. The tiered model is the practical resolution: keep what proves provenance and policy, and let raw interaction data expire.

• Name an owner for the retention schedule and document the rationale for each tier's period.
• Review the schedule on a fixed cadence and after any change in products, contracts or customer requirements.
• Redact sensitive content at the point of capture so Tier 1 data is less burdensome to hold.
• Maintain an index that lets you locate records by release, repository and time range.
• Evidence deletions, so you can show that expired data was removed under the schedule.
• Support legal holds that override scheduled deletion for specific records.

How runtime enforcement produces retainable evidence

Evidence reconstructed after the fact from assistant logs is hard to defend and expensive to store. Evidence produced at the moment a decision is made is more compact and more credible. When policy is enforced in the execution path across coding assistants, each suggestion can be checked at runtime, and the decision (allow, redact or block) is recorded alongside the code's origin, license and security context. That yields Tier 2 and Tier 3 records as a by-product of enforcement, not as a separate collection effort.

Trussed enforces policy in the execution path across coding assistants and provides code provenance and audit-ready evidence generated by that enforcement. In a self-managed deployment in the customer cloud, retention is configurable and data residency stays under the customer's control, so each tier can carry its own period and region. Whatever tooling is used, the same test applies: can the vendor produce, for a given release, the code's origin and license context and the policy applied at the time?