SI-15: Information Output Filtering

SI-15: Information Output Filtering requires you to validate outputs from defined applications so the information leaving the system matches what you expect, and does not expose unauthorized or malformed data. To operationalize it quickly, scope the “output points,” set explicit validation rules per output type, implement technical checks (or compensating review), and retain repeatable evidence. ¹

Key takeaways:

• Scope the requirement to specific “software programs/applications” and their output channels, not the entire environment.
• Define “expected content” as testable rules (format, classification, recipients, fields, ranges) and enforce them with automated gates where possible.
• Evidence matters as much as design: you need repeatable artifacts that prove validation runs and exceptions are handled.

Compliance teams tend to treat “output filtering” as a generic DLP topic. SI-15 is narrower and more operational: it asks whether the outputs of specific applications are validated against expected content before those outputs are released or acted upon. The control is about preventing integrity and confidentiality failures at the moment information becomes an “output,” such as a report, an API response, a file export, a notification email, a print job, or a data feed to a third party.

The practical challenge is definitional. You must name which applications are in scope and what “expected content” means for each output type. Once you define that, implementation becomes straightforward: build validation rules (schema checks, allowlists, classification checks, field suppression, recipient gating, and error handling), then prove the checks run consistently and exceptions are controlled.

This page is written for a Compliance Officer, CCO, or GRC lead who needs requirement-level guidance they can hand to engineering or operations without translation. The goal is assessment-ready implementation with minimal reinvention and clean evidence.

Requirement: si-15: information output filtering requirement (what it means)

Control statement: You must validate information output from specified software programs/applications so the output is consistent with expected content. ¹

Plain-English interpretation

• “Information output” means anything the application emits for humans or systems to consume: UI-rendered reports, PDFs, exports, API responses, logs sent to a SIEM, messages on a queue, emails, printed documents, EDI files, SFTP drops, webhook callbacks, or data shared with a third party.
• “Validate” means you perform checks that catch unexpected or unauthorized content before it is released, transmitted, displayed, or used to make decisions.
• “Expected content” is your defined standard for what a given output is allowed to contain and how it must be formatted.

This is not limited to sensitive data exfiltration. SI-15 also covers integrity failures like incorrect totals in financial reports, corrupted file structures, wrong customer data in a statement, misrouted notifications, or API responses containing fields that should never be exposed.

Regulatory text

“Validate information output from the following software programs and/or applications to ensure that the information is consistent with the expected content: {{ insert: param, si-15_odp }}.” ¹

Operator translation (what you must do):

1. Declare the in-scope applications (the “following software programs/applications” parameter is your scoping hook).
2. Define expected content per output channel and output type (reports vs. APIs vs. exports).
3. Implement validation controls (automated checks preferred; manual review only where automation is infeasible and risk-accepted).
4. Prove operation with repeatable evidence (test results, logs, configs, exceptions, and change history).

Who it applies to

Entity applicability

• Federal information systems and contractor systems handling federal data aligned to NIST SP 800-53 control baselines. ¹

Operational contexts where SI-15 is commonly assessed

• Systems producing regulated outputs: customer notices, statements, adverse action letters, claims summaries, or billing.
• Systems exchanging data with third parties: APIs, SFTP, webhooks, data feeds, managed file transfer.
• Analytics/BI and reporting pipelines that publish dashboards or scheduled exports.
• Multi-tenant SaaS where a single output defect can cross customer boundaries.

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

Step 1: Scope the “output surface”

Create an Output Inventory for each in-scope application:

• Output types: API response, report/PDF, CSV export, email, message bus event, log forwarding, print.
• Destinations: internal user groups, customer users, third parties, public endpoints.
• Data classifications present in the output (your policy taxonomy).
• Trigger paths: scheduled job, user request, admin action, automated workflow.

Deliverable: a table that ties each application to its output channels and owners.

Step 2: Define “expected content” as enforceable rules

For each output type, define validation rules that can be tested. Examples:

• Schema/format rules: JSON schema validation, CSV column order, required fields present, allowed character sets.
• Field-level allow/deny rules: permitted fields only; block internal notes, secrets, access tokens, or debug traces from escaping.
• Classification rules: if output contains restricted data, enforce encryption-in-transit, authenticated recipients, and approved destinations.
• Recipient/tenant rules: ensure the subject (customer, tenant, account) in the output matches the requester’s authorization context.
• Range/logic rules (integrity): totals reconcile; dates in valid ranges; currency codes valid; record counts non-negative.
• Redaction/masking rules: suppress fields for certain roles; mask identifiers in low-trust outputs.

Deliverable: an Output Validation Standard (one-pager) plus per-application validation matrices.

Step 3: Implement validation gates in the delivery path

Pick the strongest feasible control per output channel:

APIs

• Response filtering via serialization allowlists (DTOs/view models) instead of raw object dumping.
• Contract tests that fail builds if new fields appear in responses without approval.
• Authorization-bound queries that prevent cross-tenant data joins.

Reports/exports

• Approved templates with locked data bindings.
• Pre-release validation checks (row counts, headers, classification tags).
• Watermarking/classification labeling where policy requires it.

Emails/notifications

• Template governance and preview approval for high-risk templates.
• Recipient gating (domain allowlists for certain notifications).
• No sensitive data in subject lines; limit payloads to references with authenticated retrieval.

Data feeds to third parties

• File format validation and checksum.
• Destination control (only approved endpoints/keys).
• Reject-and-alert behavior on validation failure.

Deliverable: implementation notes that map each output type to the technical mechanism used.

Step 4: Define exception handling that auditors accept

You need a documented path for when output fails validation:

• Block, quarantine, or downgrade output (based on risk).
• Alert routing (engineering on-call; security for suspected exfiltration).
• Ticketing for root cause and corrective action.
• Time-bound exception approvals for business-critical outputs, with compensating controls.

Deliverable: a short Output Validation Exception Procedure plus sample tickets.

Step 5: Build evidence into normal operations

Treat SI-15 as “always-on,” not a one-time test:

• Logging: validation pass/fail events with trace IDs.
• Monitoring: alerts on spikes in failures or high-risk fields detected.
• Change control: validation rule changes require review, especially for high-risk outputs.

If you use Daydream to manage control operations, configure SI-15 with a named owner, a repeatable test procedure, and a scheduled evidence request list so you do not rebuild the same package each assessment cycle.

Required evidence and artifacts to retain

Use this checklist to stay assessment-ready:

1. Scope statement listing in-scope applications for SI-15 and why. ¹
2. Output Inventory (application → output channels → destinations → owners).
3. Output validation rules/matrix (expected content definitions per output type).
4. Technical configuration evidence, such as:
   • API response field allowlists/config
   • Report template governance settings
   • DLP/output proxy rules where applicable
5. Test evidence
   • Unit/contract test results for output schemas
   • Negative test cases (attempted forbidden fields) and outcomes
6. Operational logs
   • Validation failure logs and alert records
   • Examples of blocked/quarantined outputs
7. Exceptions
   • Approved exceptions with owner, rationale, compensating controls, expiration
   • Closure evidence and post-incident fixes
8. Change records
   • PR reviews for output-related code paths
   • Release notes that mention output validation changes

Common exam/audit questions and hangups

Expect assessors to probe these points:

• “Which applications are in scope for SI-15?” If you cannot answer cleanly, you will fail the intent of the parameterized requirement. ¹
• “Define expected content.” Vague statements like “no sensitive data” won’t hold. They want concrete rules and examples.
• “Show me it works.” They will ask for test outputs, logs, and a recent failure handled correctly.
• “How do you prevent regressions?” They want CI/CD gates, contract tests, or controlled templates.
• “How do third-party outputs get validated?” Data feeds and outbound integrations are high scrutiny because mistakes are hard to claw back.

Frequent implementation mistakes (and how to avoid them)

Mistake	Why it fails SI-15	Fix
Treating SI-15 as only DLP tooling	SI-15 is about validating application outputs vs. expected content, including integrity	Define output rules per app/output type, then decide where DLP fits
No scoping list of in-scope apps	Requirement is parameterized to “following applications” 1	Maintain an explicit SI-15 scope statement tied to system inventory
Relying on “manual review” with no proof	Auditors will ask how often, who, and where evidence is	Create a checklist, require sign-off, retain artifacts for sampled outputs
Validations exist but are easy to bypass	Backdoors, debug endpoints, admin exports bypass controls	Map “alternate output paths” in the Output Inventory and gate them too
Exceptions never expire	Temporary becomes permanent risk	Require expiration dates and periodic review in the exception workflow

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for this requirement. From a risk perspective, SI-15 failures tend to show up as:

• Data exposure through unexpected fields in APIs or exports
• Cross-tenant or wrong-recipient delivery
• Business integrity issues from incorrect outputs (financial, operational, safety)

The operational implication is simple: output defects propagate fast, and downstream recipients may store or forward the data. Your control design should assume you will not be able to “recall” bad outputs after release.

Practical 30/60/90-day execution plan

First 30 days (Immediate)

• Assign SI-15 control owner and backups.
• Produce the SI-15 scope statement and initial Output Inventory for the highest-risk application(s). ¹
• Identify top output channels: public APIs, scheduled exports, third-party feeds.
• Implement quick wins: API response allowlists on the most sensitive endpoints; disable debug output in production; tighten template permissions.

Days 31–60 (Near-term)

• Finalize expected content rules and validation matrices per output type.
• Add CI checks: schema/contract tests for APIs; linting checks for templates; unit tests for redaction logic.
• Stand up logging and alerting for validation failures.
• Document and run an exception workflow; create at least one tabletop scenario (wrong-recipient export).

Days 61–90 (Operationalize)

• Expand coverage to remaining in-scope applications and alternate output paths (admin exports, bulk tools).
• Run a control self-test and capture an evidence bundle (configs, tests, logs, sample incidents).
• Integrate SI-15 evidence collection into your recurring GRC cycle (calendar-based requests, review meetings, and tracked remediation). Daydream is a practical place to centralize the SI-15 procedure and recurring evidence tasks so audits do not depend on heroics.

Frequently Asked Questions

What counts as “information output” for SI-15?

Treat any system-emitted content that leaves a trust boundary as output, including API responses, exports, emails, files sent to third parties, and published reports. If a consumer can act on it, it is an output worth scoping and validating.

Can we satisfy SI-15 with DLP alone?

Sometimes DLP helps, but SI-15 expects validation against “expected content,” which often requires app-level rules (field allowlists, tenant checks, schema controls). Use DLP as a backstop, not as the only control.

How do we define “expected content” without writing a novel?

Start with a matrix per output type: allowed fields, forbidden fields, recipient rules, required formats, and classification handling. Keep it testable and tie it to code/config controls so you can prove it runs.

What’s the minimum evidence an auditor will accept?

A clean scope list, a documented validation standard, and proof of operation (test results and logs) for sampled outputs. Add exception records if any outputs are allowed to bypass validation under approval.

What about outputs generated by third-party SaaS tools we use?

If the output is generated within your authorization boundary (reports, exports, notifications) and contains your data, include it in scope. Contractually require the third party to support output controls, then collect configuration screenshots, audit logs, and sample outputs as evidence.

How should we handle validation failures in production?

Default to blocking or quarantining high-risk outputs, and degrade gracefully for low-risk outputs with clear user messaging. Route alerts to an accountable team, open tickets, and track root cause through remediation and retesting.

Footnotes

1. NIST SP 800-53 Rev. 5 OSCAL JSON