The entity implements policies and procedures over system outputs

TSC-PI1.4 sits in the SOC 2 Processing Integrity criteria and targets a common failure point: systems can be secure and available, yet still produce the wrong results. For most service organizations, “system outputs” include customer-facing reports, invoices, exports to customers, API responses, files sent to downstream processors, internal dashboards used for operations, and any data feed that drives customer decisions or contractual deliverables.

Operationalizing this requirement means turning output quality into a controlled process, not an informal “someone checks it” habit. You need policies that set expectations, procedures that describe the checks, and evidence that shows those checks ran consistently across the audit period. The strongest implementations are output-specific: a single generic policy rarely satisfies auditors unless it clearly maps to your real outputs, owners, tools, and exception handling process.

This page gives requirement-level guidance to help a Compliance Officer, CCO, or GRC lead stand up a credible control quickly: scope what counts as an output, pick control types by output risk, implement reviews and exception workflows, and package evidence in an auditor-friendly way.

Regulatory text

SOC 2 Trust Services Criteria (Processing Integrity), TSC-PI1.4: “The entity implements policies and procedures over system outputs.” ¹

What the operator must do:
You must (1) document what controls exist over outputs, (2) implement them so they operate in practice, and (3) be able to prove operation with retained evidence. “Over system outputs” means the control environment covers the creation, review, approval, distribution, and correction of outputs, not only the inputs or processing steps.

Plain-English interpretation

Your systems generate results. This requirement expects you to manage the risk that results are wrong, incomplete, sent to the wrong recipient, generated from the wrong version of logic, or changed after generation.

A practical interpretation auditors accept:

• You know which outputs matter (especially customer-impacting outputs).
• You have defined “quality” for those outputs (what must be true for the output to be acceptable).
• You have checks to prevent/detect errors.
• You investigate exceptions, correct them, and prevent recurrence.
• You can show evidence across the audit period.

Who it applies to (entity and operational context)

Applies to service organizations pursuing SOC 2 Processing Integrity coverage ¹. In practice, this includes:

• SaaS providers producing customer reports, billing, usage statements, compliance exports, model outputs, or data pipelines.
• Fintech/payment processors producing settlement files, transaction confirmations, dispute outputs, or reconciliation reports.
• Data processors producing extracts, transformations, and onward transfers to customers or other third parties.
• Internal shared services where outputs drive customer commitments (SLAs, invoices, entitlement, provisioning).

Outputs typically in scope

Use this as a scoping checklist; tailor to your environment:

• Customer-facing: invoices, statements, customer portals, audit logs provided to customers, “download your data” exports, compliance reports.
• Machine-to-machine: API responses, webhooks, batch exports, SFTP files.
• Operational: provisioning outputs (entitlements), ticket automation outcomes, scheduled jobs that update customer data.

What you actually need to do (step-by-step)

Step 1: Build an output inventory (the auditor anchor)

Create a register that lists each in-scope output with:

• Output name and description
• Producing system(s) and data sources
• Output type (report, API response, file, dashboard)
• Consumer (customer, internal team, third party)
• Frequency/trigger
• Owner (role, not person)
• Materiality and risk rating (customer impact, financial impact, regulatory impact)
• Key quality criteria (accuracy, completeness, timeliness, authorization)
• Control(s) that govern it and evidence location

Tip: Start with the outputs that appear in contracts, invoices, customer SLAs, and customer-facing UI. Those get the most scrutiny.

Step 2: Define output quality criteria that can be tested

For each output (or grouped class of outputs), write criteria that a reviewer or automated check can validate. Examples:

• Completeness: all required records included; no missing partitions/time windows.
• Accuracy: calculations align to approved logic; totals reconcile to source systems.
• Timeliness: output generated within your internal delivery window.
• Authorization and distribution: only authorized users can access; correct tenant/customer boundaries.

Avoid vague criteria like “output is correct.” Write what “correct” means in your context.

Step 3: Choose control types based on output risk

Use a layered approach; auditors like to see prevention and detection.

Control menu (mix and match):

• Automated validations: schema checks, range checks, referential integrity, required fields, idempotency checks for jobs.
• Reconciliations: compare totals between source and output; compare job counts; compare ledger totals to settlement totals.
• Maker-checker reviews: human review and approval for high-risk outputs (billing runs, settlement files, regulatory exports).
• Access controls over distribution: least privilege for report download, export endpoints, and file delivery locations.
• Change management ties: outputs dependent on code/config should link to approved changes and testing.
• Monitoring and alerting: job failures, anomaly detection on volumes, threshold alerts.
• Exception handling: tickets for mismatches, defined severity, root cause analysis, re-runs, customer notification triggers.

Step 4: Write the policy and procedures (keep them operational)

You need two layers of documentation:

1. Policy: sets governance expectations (scope, roles, minimum required checks, retention, escalation).
2. Procedures / runbooks: how checks happen for each major output class.

A workable policy outline:

• Scope definition for “system outputs”
• Ownership model (output owners, engineering owners, reviewers)
• Minimum control requirements by risk tier
• Evidence requirements and retention expectations
• Exception management and escalation
• Periodic review cadence for the output inventory and controls (state the cadence you can meet)

Procedures should be specific enough that a new hire can execute a review without tribal knowledge.

Step 5: Implement evidence capture as part of the workflow

Most SOC 2 misses here: controls may exist, but evidence is scattered or ephemeral.

Design evidence capture so it happens automatically:

• For automated checks: retain logs, monitoring screenshots/exports, or system-generated reports with timestamps.
• For manual reviews: require approval records in a ticketing system, workflow tool, or signed checklist with date/time and reviewer identity.
• For exceptions: require a ticket with root cause, remediation, and validation steps.

If your team uses Daydream to run compliance workflows, treat the output inventory as the control hub: map each output to its control, attach evidence templates, and keep an audit-ready trail without chasing screenshots at the end of the period.

Step 6: Test operation before the audit period closes

Run an internal operating effectiveness check:

• Sample outputs from across the period.
• Verify evidence exists, is complete, and ties to the output.
• Confirm exceptions show closure and corrective action.

Create a short “control operation memo” for each high-risk output that explains how the control worked and where evidence lives.

Required evidence and artifacts to retain

Auditors typically ask for evidence that spans the audit period and ties back to defined controls. Retain:

• Output inventory/register with owners, risk tier, and mapped controls
• Output governance policy and procedures/runbooks
• Data quality/reconciliation evidence (reports, logs, job run IDs, monitoring exports)
• Manual review evidence (approvals, sign-offs, ticket workflow history)
• Exception tickets with investigation notes, root cause, remediation, and validation
• Change records for output-affecting logic (links to testing and approval)
• Access control evidence for distribution endpoints (role mappings, permission reviews where applicable)

Evidence should be time-stamped and attributable to a role or system identity.

Common exam/audit questions and hangups

Expect these lines of questioning:

• “List your system outputs in scope for SOC 2 Processing Integrity. Which are customer-impacting?”
• “Show how you define output correctness and completeness for billing/exports/API.”
• “For this sample output, show the control and the evidence for a specific date.”
• “What happens when an output check fails? Who decides to re-run? When do you notify customers?”
• “How do you know output logic changes were tested and approved before release?”
• “How do you prevent cross-tenant data exposure in exports and reports?”

Hangups that create findings:

• No formal output inventory; controls described only at a system level.
• Evidence exists, but it does not clearly tie to a specific output instance.
• Manual reviews are ad hoc (“reviewed in Slack”) with no durable record.
• Exceptions are fixed, but there is no record of validation after remediation.

Frequent implementation mistakes and how to avoid them

Mistake	Why it fails in SOC 2	Fix
Single generic “data quality policy” with no procedures	Auditors cannot test operation against real outputs	Add output classes and runbooks; map outputs to controls and evidence
Treating outputs as only “reports”	APIs, exports, and files are often the riskiest outputs	Include all delivery channels in the output inventory
No owner per output	No accountable party for review and exceptions	Assign a role owner and backup owner
Evidence stored in personal drives	Evidence is incomplete and hard to sample	Centralize evidence in ticketing/compliance system with standard templates
Alerting exists but no follow-through	Monitoring without response is not an operating control	Require incident/ticket creation and closure with validation evidence

Enforcement context and risk implications

SOC 2 is an attestation framework; public enforcement is not typically framed as “TSC-PI1.4 violations.” Your real risk is commercial and operational: incorrect outputs can trigger customer disputes, billing errors, SLA breaches, restatements of customer reports, and downstream processing failures. For service organizations, output defects frequently become audit findings because they are observable, sampleable, and easy for auditors to challenge.

Practical 30/60/90-day execution plan

Days 0–30: Scope and design

• Identify in-scope systems for Processing Integrity and draft the output inventory.
• Classify outputs by risk tier and define quality criteria per tier.
• Draft the Output Governance Policy and at least one procedure per high-risk output class (billing, exports, API).
• Decide where evidence will live (ticketing system, GRC tool, log archive) and standardize naming conventions.

Days 31–60: Implement and pilot

• Implement or formalize automated validations and reconciliations for the highest-risk outputs.
• Add maker-checker approval where needed (especially for financial/customer deliverables).
• Build exception workflow: ticket templates, severity definitions, and escalation paths.
• Pilot evidence capture for a subset of outputs; run a mini-sample test as if you were the auditor.

Days 61–90: Operate, test, and harden

• Expand controls and evidence capture across remaining in-scope outputs.
• Perform an internal operating effectiveness review: sample outputs across time, confirm evidence completeness.
• Fix weak spots: missing timestamps, unclear ownership, exceptions without validation, inconsistent logs.
• Prepare an audit “evidence map” that lists each output control, where evidence is stored, and how sampling will work.

Frequently Asked Questions

What counts as a “system output” for TSC-PI1.4?

Any data, file, report, API response, or downstream feed your system produces that customers, internal teams, or third parties rely on. If an output can impact customers, billing, entitlements, or contractual reporting, treat it as in scope.

Do we need manual reviews for every output?

No. High-volume, low-risk outputs can rely on automated validations and monitoring. Reserve manual maker-checker reviews for high-risk outputs like billing runs, settlement files, or sensitive customer exports.

What evidence is most persuasive to SOC 2 auditors?

Evidence that is specific, time-stamped, and ties to a control execution for a particular output instance (for example, reconciliation results for a given job run ID plus the exception ticket if it failed). Policies alone rarely pass; auditors sample operation.

How do we handle outputs generated continuously (APIs, streaming)?

Define quality controls at the service boundary: contract tests, schema validation, error-rate monitoring, anomaly alerts on volumes, and access controls over tenant scoping. Retain logs and monitoring exports that show the checks ran and alerts were handled.

We fix output issues quickly in engineering. Is that enough?

Speed helps, but auditors still want a durable record: detection signal, ticket/incident, root cause, remediation, and validation. Build that record into your normal engineering workflow so it’s not a separate compliance exercise.

Where does Daydream fit for this requirement?

Daydream is useful when evidence is the bottleneck: it can track the output inventory, assign control owners, standardize evidence requests, and keep an audit-ready trail of reviews and exceptions without end-of-period scrambling.

Related compliance topics

• 2025 SEC Marketing Rule Examination Focus Areas
• Access and identity controls
• Access Control (AC)
• Access control and identity discipline
• Access control management

Footnotes

1. AICPA TSC 2017