Billing records, security logs, traces, and model payloads should have separate minimization and retention policies because they serve different purposes and expose different types of information. Billing should generally use account-level usage and transaction evidence rather than model content. Security logs should capture security-relevant events without credentials or request bodies by default. Traces should be sampled, redacted, and retained only for active diagnostic needs. Model payloads—including prompts, outputs, embeddings, attachments, and retrieved context—should default to no persistence beyond processing unless a defined feature or customer instruction requires storage.
At a glance: four data classes require four distinct policies
The short answer
A single “AI data retention” rule is usually too broad to be useful. Each data class needs its own purpose, allowed fields, prohibited fields, collection trigger, retention basis, access scope, storage boundary, sharing rules, deletion method, and exception process.
The recommended defaults are:
- Billing records: Keep only what is necessary for invoicing, reconciliation, disputes, tax, and contractual obligations. Keep prompts, outputs, attachments, and retrieved context out of financial systems.
- Security logs: Record events needed to detect, investigate, and demonstrate security-relevant activity. Exclude credentials, API tokens, secrets, full request bodies, and model payloads by default.
- Traces: Prefer sampling, field allowlists, redaction, and short diagnostic retention. Permit full-fidelity capture only for a documented debugging or testing need.
- Model payloads: Treat prompts, outputs, embeddings, attachments, and retrieved context as the most content-sensitive class. Do not persist them beyond processing unless storage supports an explicit feature or customer instruction.
These categories can contaminate one another. A prompt may be copied into an error trace, an API key may appear in a header log, or retrieved documents may enter an observability platform through an unrestricted request-body field. Controls therefore need to operate at collection and ingestion—not only through downstream retention policies.
Operational metadata also deserves scrutiny. A timestamp, model identifier, token count, account ID, latency measurement, or request ID may appear less sensitive than a prompt, but combinations of metadata can still reveal user activity, business patterns, or identifiable behavior. Pseudonymization can reduce direct identifiability; it does not make data anonymous.
Comparison matrix: purpose, allowed data, prohibited data, retention, access, and deletion
| Data class | Primary purpose | Data generally allowed | Data prohibited by default | Retention approach | Access scope | Deletion trigger |
|---|---|---|---|---|---|---|
| Billing records | Invoicing, payment reconciliation, dispute handling, tax, and contractual records | Account or contract ID, billing period, applicable usage totals, rates, invoice status, transaction references | Prompts, outputs, embeddings, attachments, retrieved context, credentials, secrets, full request bodies | Retain according to finance, tax, contractual, dispute, and legal needs; avoid extending retention merely because another system keeps longer logs | Finance and other personnel with a defined billing, dispute, audit, or legal role | End of the applicable financial or contractual retention obligation, subject to documented holds |
| Security logs | Detection, investigation, response, and evidence of security-relevant activity | Event time, event type, outcome, pseudonymous actor or tenant identifier, resource, source context, policy decision, correlation ID | Passwords, API keys, access tokens, secrets, full request or response bodies, model payloads | Use tiers based on investigative value and operational need rather than one period for every event | Security operations, incident response, and narrowly authorized platform personnel | Expiration of the relevant security tier or closure of an investigation, unless a documented hold applies |
| Traces | Debugging, reliability analysis, performance diagnosis, and test validation | Sampled timing data, component names, status codes, sanitized attributes, correlation IDs, model or route identifiers where necessary | Prompt and response bodies, retrieved documents, attachments, credentials, unrestricted headers, unnecessary user identifiers | Keep routine diagnostics short; tie exceptional full-fidelity traces to an incident, test, or debugging window | Engineers assigned to the affected service or incident, with exceptional traces more tightly restricted | End of the diagnostic window, incident, or test, followed by removal of exceptional captures |
| Model payloads | Inference processing and explicitly selected payload-dependent features | Prompts, outputs, embeddings, attachments, and retrieved context only to the extent required for processing or an authorized feature | Content unrelated to the declared purpose; secrets or sensitive identifiers that the workflow does not require | Default to no persistence beyond processing unless caching, evaluation, feedback, abuse review, or another defined use requires storage | The processing service and personnel or systems explicitly authorized for the selected purpose | Completion of processing or the end of the separately defined feature, customer instruction, review, or cache lifecycle |
This matrix is a starting policy framework, not a universal legal retention schedule. Appropriate periods vary with jurisdiction, contract terms, tax rules, dispute status, litigation holds, operational needs, and organizational policy. Privacy, security, finance, and legal owners should review the resulting schedule.
Use the same policy template without applying the same retention rule
A consistent template makes the four policies easier to govern. Consistency does not mean applying the same fields, access permissions, or deletion date to every category. It means asking the same control questions and documenting purpose-specific answers.
Define purpose, minimum fields, prohibited fields, and collection triggers
Start each policy with a narrow purpose statement. “Operations” or “analytics” is usually too vague. A useful statement identifies the decision or workflow the data enables, such as producing an invoice, investigating an authentication anomaly, diagnosing a failed inference request, or delivering a customer-selected feedback feature.
For every purpose, define:
- Minimum necessary fields: List the fields required to perform the stated function. Prefer an allowlist over collecting everything and removing sensitive fields later.
- Prohibited fields: Identify data that must not enter the destination, even if it is technically available upstream.
- Collection trigger: Specify whether collection occurs for every event, through sampling, after an alert, during an approved test, or only when a customer enables a feature.
- Transformation at ingestion: Apply redaction, truncation, hashing, tokenization, or pseudonymization before data reaches logging, tracing, billing, or analytics infrastructure.
For billing, usage totals or account-level measurements are generally more appropriate than prompts and outputs. Finance systems need enough evidence to calculate and reconcile charges, but the presence of a model payload rarely makes a billing record more useful. Where detailed usage events are necessary, separate them from invoice records and minimize their identifiers.
For security logs, collect events that support detection and investigation: authentication outcomes, permission decisions, configuration changes, administrative actions, and relevant service failures. Logging middleware should suppress authorization headers, cookies, credentials, API tokens, secrets, and request bodies unless an explicitly approved investigation requires a narrower exception.
For traces, routine observability should favor sanitized attributes and sampled spans. Do not assume application frameworks or tracing agents will exclude model content automatically. Prompt text can enter span attributes through convenience instrumentation, while secrets can appear in URL parameters, headers, stack traces, or exception messages.
For model payloads, distinguish processing from secondary use. Permission to send a prompt for inference does not automatically establish a purpose for retaining it for evaluation, feedback analysis, abuse review, or future model development. Each additional use should have its own trigger, access rule, retention basis, and deletion event.
Assign a retention basis, access scope, storage boundary, and sharing rules
Retention should follow the purpose rather than the technical convenience of the storage platform.
Billing retention should be determined with finance and legal teams. Relevant considerations include invoicing cycles, payment reconciliation, contractual audit rights, tax records, disputes, and applicable holds. A billing obligation does not justify copying model content into financial records.
Security-log retention can be tiered according to investigative value. High-volume routine events may have a different useful life from alerts, privileged administrative actions, or records linked to a confirmed incident. When an event is promoted into a case, preserve only the case-relevant evidence under the incident process instead of retaining every surrounding log indefinitely.
Trace retention should normally be short and diagnostic. Routine sampled traces and exceptional full-fidelity traces should not share the same policy. A full-fidelity exception should identify the affected service, approved fields, responsible owner, authorized users, start and end conditions, and deletion trigger.
Model-payload retention should be purpose-specific. Consider these separate data uses rather than one general payload store:
- transient inference processing;
- response or semantic caching;
- quality evaluation;
- user feedback;
- abuse or safety review;
- incident investigation;
- customer-requested conversation or workflow history.
Caching deserves particular attention because it can turn transient input into persisted content. The policy should define what enters the cache, whether entries contain original content or derived representations, how scope is separated, what invalidates an entry, and how deletion requests affect cached copies. Embeddings should remain within the model-payload policy when they represent customer content; they should not be treated as harmless metadata by default.
Storage boundaries should identify the systems, environments, regions where relevant, backups, exports, observability tools, support platforms, and subprocessors that may receive each class. Downstream sharing should be purpose-limited: data minimized in the primary service can become overexposed if it is copied into ticketing, analytics, or debugging systems with broader access.
Access should be based on a defined operational role and need, not simply on engineering or administrative status. Access to billing records, security evidence, diagnostic traces, and payload content should be considered separately. In particular, permission to operate inference infrastructure should not automatically imply permission to read customer prompts.
Document deletion methods, owners, reviews, and approved exceptions
A deletion rule needs both a trigger and an implementation path. “Delete when no longer needed” is difficult to test unless the organization defines what ends the need and which systems must act.
For each class, document:
- the event that starts the retention period;
- conditions that pause normal deletion, such as an active dispute or incident;
- deletion from primary storage, indexes, caches, exports, and diagnostic copies;
- how backup lifecycle handling aligns with the policy;
- the system owner responsible for execution;
- the policy owner responsible for approving changes;
- evidence that deletion jobs and manual workflows operate as intended.
Exceptions should be narrow, time-bound, and documented. If an engineer needs payload-bearing traces to reproduce a failure, the exception should specify the affected traffic, captured fields, reason, access group, expiration event, and person accountable for cleanup. Closing the incident or test should initiate deletion rather than leaving the capture active until someone remembers to remove it.
Periodic reviews should compare written rules with actual system behavior. Useful tests include:
- sending synthetic credentials and confirming they do not reach logs;
- inserting recognizable test prompts and checking whether they appear in traces;
- verifying that disabled payload capture remains disabled after deployments or agent upgrades;
- confirming that cache invalidation and deletion workflows reach derived copies;
- reviewing exports and support tooling for fields that bypass primary controls;
- sampling billing events to ensure they contain usage evidence rather than model content.
These tests are important because data-class boundaries often fail through instrumentation defaults, exception handling, or field propagation—not through the main application database.
Evaluate AI infrastructure against the policy
Provider and architecture reviews should translate the framework into concrete questions. Teams evaluating AI infrastructure should ask:
- Can logging fields and event categories be configured, or are request details captured automatically?
- Under what conditions can prompts, outputs, attachments, embeddings, or retrieved context enter logs or traces?
- Are field allowlists, sampling, and redaction available at ingestion?
- How is cache persistence defined, scoped, invalidated, and deleted?
- How are tenant or workload boundaries handled across logs, traces, caches, and exports?
- Which retention settings are configurable, and which are fixed by the service or contract?
- What deletion workflows cover primary storage, caches, diagnostic copies, exports, and backups?
- Which personnel, systems, and subprocessors can access each data class?
- Can customers export relevant records without receiving unnecessary payload content?
- What audit evidence shows that policy changes, exceptional access, and deletion actions occurred?
- How do managed API access and private deployment differ in telemetry flow and operational responsibility?
Routing, caching, batching, and telemetry design can all influence what data is created, combined, or retained. For example, routing may generate provider-selection metadata; caching may persist content or derived representations; batching may combine requests within an operational workflow; and telemetry may propagate attributes into external observability systems. Quantization and GPU scheduling are primarily serving decisions, but their surrounding control-plane telemetry should still follow the relevant minimization policy.
Token Forge Cloud Private LLM Inference supports private deployment paths where models, prompts, and telemetry remain in the customer’s controlled environment. This provides a useful context for defining ownership and control requirements, but teams should still establish the exact logging, caching, access, retention, deletion, residency, and administrative responsibilities required for their deployment.
Token Forge Cloud Managed Model APIs provide an API-first path for model access and usage data, with a path toward private deployment. Teams using managed access to validate demand should apply the same four-class analysis early so that billing information, operational telemetry, traces, and model content do not become conflated as usage grows.
Token Forge Cloud also treats latency-sensitive chat, batch enrichment, and agentic workflows as different serving-policy problems. Their data policies may differ as well: chat can involve conversational history, batch enrichment may process large datasets, and agentic workflows can create tool-call records and retrieved context. The common policy template remains useful, but collection and retention decisions should follow each workload’s actual purpose.
Contact Token Forge Cloud to discuss your API access, private deployment, and LLM inference cost-control needs.