AI Data Retention Policy Template for 2026: What to Keep, What to Delete

TL;DR: AI Data Retention Policy Template for 2026: What to Keep, What to Delete, a practical compliance guide for enterprise and HR teams in 2026.

AI systems produce data that your existing records retention policy almost certainly did not contemplate. When your legal team wrote your document retention schedule in 2019, they were not thinking about inference logs, prompt histories, bias audit records, or model training datasets. Those categories now exist in your environment, they contain personal data in many cases, and multiple regulatory frameworks say something about how long you can keep them.

The difficulty is that those frameworks often say contradictory things. The EU General Data Protection Regulation (GDPR), which became enforceable May 25, 2018, tells you to delete personal data when you no longer need it. The US Equal Employment Opportunity Commission (EEOC) tells you to keep hiring-related records for at least one year, and the OFCCP extends that to two years for federal contractors. The EU AI Act, whose Annex III high-risk obligations are set to apply from December 2, 2027 (roughly two months from now), requires post-market monitoring logs for the system's operating lifetime. These requirements can and do apply simultaneously to the same data.

This guide provides a practical framework for handling those conflicts. It covers the main AI-generated data types, the requirements each jurisdiction places on them, a ready-to-use retention schedule table, and technical guidance on implementing automated deletion.

What AI-generated data types need retention policies

Before you can write a retention policy, you need a clear inventory of what you are actually generating. AI systems create several distinct data categories, and organizations routinely conflate them.

Prompt logs are records of the inputs sent to an AI system. Depending on your tool and configuration, these may be stored by the vendor (in which case your Data Processing Agreement governs retention), by your own infrastructure, or both. Prompt logs frequently contain personal data when employees include customer names, email addresses, or case details in their prompts.

Output logs are records of what the AI system returned. For systems used in consequential decisions, such as credit scoring, hiring screening, or medical triage, the output log is often the record you need to demonstrate that a decision was made and how. Output logs are less likely than prompt logs to contain personal data by default, but they may contain inferences about individuals that regulators treat as personal data under GDPR Article 4(1).

Inference logs are generated by model serving infrastructure and record metadata about each API call: timestamps, model version, token counts, latency, and sometimes input/output hashes. They are primarily useful for performance monitoring, cost attribution, and incident investigation. They are lower sensitivity than prompt or output logs in most cases.

Model training datasets are the data used to fine-tune or train a model. If you fine-tune a model on employee communications, customer service transcripts, or other records, those training datasets need their own retention policy. The EU AI Act Article 10 requires that providers of high-risk AI systems document their training data, validation data, and testing data, and that documentation must be maintained for ten years after the system is placed on the market.

Bias audit records are the outputs of algorithmic audits, fairness evaluations, or testing for discriminatory outcomes. New York City Local Law 144, which took effect July 5, 2023, requires employers using AI hiring tools to conduct annual bias audits and publish summary results. Records underlying those audits should be retained long enough to respond to any challenge or investigation, which in practice means at least two years after the audit.

Human review records capture decisions made by humans when reviewing or overriding AI recommendations. Under the EU AI Act Article 14, high-risk AI systems must allow human oversight. Those human review records may be your primary evidence of compliance if an enforcement authority investigates.

Retention requirements by jurisdiction

The following requirements are drawn from regulations in force or taking effect by mid-2026. Where a law's retention requirement depends on context, that context is noted.

Under GDPR, no specific retention periods are prescribed for most AI data categories. Article 5(1)(e) requires storage limitation: personal data must be kept in a form that permits identification of data subjects for no longer than necessary for the purposes for which it is processed. In practice this means your retention period must be justified by a documented purpose. The European Data Protection Board's guidelines on automated decision-making recommend short retention for inputs to automated systems unless a specific legal basis supports longer retention.

The EU AI Act imposes specific retention obligations that vary by role. Providers of high-risk AI systems listed in Annex III must retain technical documentation under Article 11 for ten years after the system's last placement on the market. Deployers of high-risk AI systems must retain logs under Article 26(5) for at least six months unless other law requires more. For Annex III systems used in employment, workers management, or access to self-employment, the six-month floor may be exceeded by member state employment law.

US EEOC regulations under 29 CFR Part 1602 require employers with fifteen or more employees to keep employment records, including records related to hiring decisions, for one year from the date of the record or the date of the personnel action, whichever is later. If a charge of discrimination is filed, all relevant records must be kept until the final disposition of the charge.

OFCCP regulations under 41 CFR Part 60-1 impose a two-year retention requirement on supply and service contractors with fifty or more employees and contracts over $50,000. This applies to application records, applicant flow data, and supporting data for hiring decisions, which now includes AI system outputs if AI was used in the screening process.

The California Consumer Privacy Act (CCPA), as amended by CPRA effective January 1, 2023, requires that privacy notices disclose retention periods or the criteria used to determine them. There is no minimum retention requirement; the requirement is for transparency. Businesses must honor deletion requests for personal data within 45 days, though data retained under a legal obligation is exempt.

The Federal Trade Commission Act Section 5 gives the FTC broad authority to act against unfair or deceptive practices. The FTC's 2023 policy statement on commercial surveillance noted that failure to implement adequate data retention and deletion practices can constitute an unfair practice. The FTC has not yet issued formal AI retention rules as of mid-2026, but its settlements with companies like Amazon (Ring, 2023) and Rite Aid (2023) included specific data deletion requirements as corrective action.

Conflict resolution framework

When two or more requirements apply to the same data, apply these principles in order.

First, apply the longest legally required period. Legal obligations take precedence over data minimization preferences. If EEOC requires one year and GDPR says minimize, you keep the data for one year, documenting the legal basis as Article 6(1)(c) compliance with a legal obligation.

Second, limit access and purpose during the extended retention period. Data kept longer than its operational purpose warrants should be archived with restricted access. Only HR, legal, and compliance personnel should have access during the extended retention period.

Third, apply pseudonymization or aggregation where the legal requirement allows it. EEOC record-keeping requirements focus on maintaining the record of the decision; they do not require that the data remain in individually identifiable form for the full retention period in all cases. Check with employment counsel before applying pseudonymization to records subject to regulatory requirements.

Fourth, document everything. Your rationale for each retention period, the regulation you relied on, and the date you conducted the analysis should be recorded in your Record of Processing Activities (required under GDPR Article 30) and your AI governance documentation. When an enforcement authority asks why you kept something for three years, the answer must be in writing.

Retention schedule table

Copy and adapt this table for your organization. Periods shown are minimums based on applicable law; your organization may choose longer periods if a documented business purpose supports them.

Copy-paste retention schedule: 8 AI data types

The general schedule earlier in this article covers a broad set of categories. The table below is narrower and more specific: eight AI data types that appear in almost every organization using AI tools, with concrete retention periods, the legal basis for each, the trigger that starts the deletion clock, and who owns the decision.

Retention periods shorter than the legal minimums are not compliant, even if storage cost is the motivation. For EU AI Act Article 12, the 3-year minimum for high-risk AI systems is a floor, not a suggestion.

Technical implementation

A written retention schedule has no effect unless systems are configured to enforce it. The following covers the main implementation patterns.

For cloud object storage, AWS S3 lifecycle policies allow you to automatically transition objects to Glacier (cold storage) or delete them after a configured number of days. A policy that deletes prompt logs after 30 days looks like: set an expiration rule on the S3 bucket or prefix where logs land, with expiration set to 30 days. For Google Cloud Storage, the equivalent is Object Lifecycle Management rules. Azure Blob Storage uses lifecycle management policies under the Storage Account settings. All three support tag-based policies, meaning you can tag objects at write time with a retention class (e.g., retention=30d, retention=2y) and apply different rules to each class.

For database logs, implement a scheduled job that runs daily and deletes records older than your retention threshold. In PostgreSQL, a cron job running DELETE FROM prompt_logs WHERE created_at < NOW() - INTERVAL '30 days' handles the basic case. Use pgAudit to log the deletion job itself, so you have a record that deletion happened. For high-volume log tables, consider partitioning by date, which makes dropping old partitions faster and more reliable than row-by-row deletion.

For AI vendor-held logs, your Data Processing Agreement should specify the maximum retention period the vendor applies to your data. Many vendors default to 30 days but will negotiate shorter periods. Review your DPA with Anthropic, OpenAI, Google, or other providers annually. If a vendor cannot contractually commit to a specific retention period, document that gap in your AI tool register and escalate it as a risk. See the AI tool register template for a template that includes vendor retention terms as a tracked field.

For model training data, retention is handled differently than log data. Training datasets are typically stored in controlled data science environments, not in general application logs. Implement a data catalog (AWS Glue, Google Data Catalog, or an open-source alternative like DataHub) that records the location, contents, and intended retention period of each dataset. When a dataset is no longer needed for retraining or audit purposes, the deletion should be authorized by your AI governance lead and recorded in the catalog.

Retention tag configuration at write time is the most reliable approach for mixed-sensitivity logs. When your application writes a log entry, include a field or tag that specifies the applicable retention class. For example, tag all hiring-related output logs with retention_class=employment_2y, and configure your deletion job to apply the appropriate schedule to each class. This is easier to audit than trying to infer sensitivity from log contents at deletion time.

Test your deletion processes quarterly. A scheduled deletion job that has silently failed for three months leaves you with a much larger data inventory than your policy contemplates, and that is the kind of finding that regulators view unfavorably. Implement monitoring that alerts when expected daily deletion jobs do not run or when log table row counts grow unexpectedly.

Connecting retention policy to your broader governance program

A retention schedule is one component of a larger AI governance program. It should be linked to your Record of Processing Activities under GDPR Article 30, your AI system register, and your Data Protection Impact Assessments for high-risk processing activities. See the AI data privacy for small teams GDPR/CCPA guide for coverage of DPIA requirements and how they intersect with AI systems.

Under the EU AI Act compliance guide for small teams, deployers of high-risk AI systems must maintain logs for six months minimum and make them available to market surveillance authorities on request. Your retention policy should explicitly reference this requirement and ensure that the storage system used for high-risk AI logs is accessible to your compliance team, not just engineering.

For organizations building their AI governance program from the foundation, the AI governance guide for small teams covers the overall program structure, including how retention policy fits into the governance documentation hierarchy.

How to implement this policy

Write your retention policy as a formal policy document that references this schedule and is reviewed annually. The policy document should:

Name the data owner responsible for each data category. For prompt logs, this is typically the engineering team. For bias audit records, it is typically HR or Legal. For training datasets, it is the data science or AI team.

Specify the legal basis for each retention period. Do not just state a number; state the regulation and article number that drives it. This documentation is what you will rely on in an enforcement action.

Include an exceptions process. There will be cases where a regulatory hold or active litigation requires you to suspend automated deletion for specific records. Your policy must address how litigation holds override the standard schedule and who has authority to impose a hold.

Set a review trigger. The policy should be reviewed any time a new AI system is deployed, any time a regulation with retention implications takes effect, and on a fixed annual cycle at minimum. Given how quickly the regulatory environment is changing in 2026, an annual review is not optional.

The retention policy is not a solved problem you file away. Regulations are changing fast enough that the schedule in this article will need updating as new requirements take effect. Build the review cadence into your compliance calendar and assign an owner who is responsible for monitoring regulatory changes.

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