SI-10: Information Input Validation

To meet the si-10: information input validation requirement, you must define which inputs matter, validate them at trust boundaries (UI, APIs, files, queues), reject or safely handle invalid data, and retain evidence that validation is designed, implemented, tested, and monitored. Operationalize SI-10 by standardizing validation patterns, assigning ownership, and proving coverage across in-scope systems.

Key takeaways:

• Validate inputs where data enters your system, not after it is stored or processed.
• Standardize rules (allowlists, type/length checks, encoding, schema validation) and apply them consistently across apps, APIs, and integrations.
• Keep assessor-ready evidence: inventory of inputs, validation specs, code/config examples, test results, and monitoring/exception handling records.

SI-10 is a requirement-level control that examiners interpret very literally: can you show that your systems check the validity of information inputs and do so consistently across the places data enters your environment. The highest-friction part is rarely coding validation in one application. It is proving coverage across the organization’s “input surface area,” including APIs, partner integrations, batch uploads, admin consoles, and message queues.

For a Compliance Officer, CCO, or GRC lead, “input validation” is easy to describe and hard to operationalize because it spans engineering standards, secure development, change management, logging/monitoring, and third-party data exchanges. SI-10 also intersects with vulnerability management and incident response: weak validation is a common precursor to injection, deserialization, path traversal, and data integrity failures. Even when security teams understand the risk, auditors still expect traceable artifacts that tie (1) defined input types, (2) validation methods, (3) enforcement points, and (4) ongoing testing into one coherent control narrative.

This page gives you an implementation playbook you can hand to engineering and still use for audit readiness: scope, steps, evidence, common pitfalls, and an execution plan.

Regulatory text

Control requirement (excerpt): “Check the validity of the following information inputs: {{ insert: param, si-10_odp }}.” ¹

What the operator must do:
You must identify the information inputs relevant to your system(s) and implement validation checks that confirm inputs conform to expected formats, ranges, types, encodings, and business rules before the system accepts or processes them. Maintain documentation and evidence that validation exists, is consistently applied at defined entry points, and remains effective as systems change. ²

Plain-English interpretation

SI-10 expects you to treat all inbound data as untrusted until it is validated. “Validity” means more than “not empty.” Inputs should be constrained to what the system expects (allowlists), rejected or sanitized safely when invalid, and handled in a way that prevents downstream security and integrity failures.

From an audit standpoint, SI-10 is met when you can show:

1. you know where inputs enter your system,
2. you have defined validation rules for those inputs,
3. those rules are implemented in code/config at the entry points, and
4. you test and monitor that validation continues to work after releases.

Who it applies to (entity and operational context)

Typical in-scope entities

• Federal information systems and programs using NIST SP 800-53 controls. ³
• Contractor systems handling federal data, including cloud/SaaS and integrators supporting federal workloads. ³

Operational contexts where SI-10 is examined

• Internet-facing applications and APIs (customer portals, mobile backends).
• Internal business systems with high integrity needs (finance, HR, admin tools).
• Data ingestion pipelines (SFTP drops, file uploads, ETL jobs).
• Integrations with third parties (EDI, webhook callbacks, partner APIs).
• Messaging and eventing systems (queues, pub/sub) where payloads can be malformed or hostile.

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

1) Assign ownership and define scope

• Name a control owner (often AppSec or Engineering Risk) and a GRC owner responsible for evidence and audit response.
• Define scope: which applications, APIs, pipelines, and platforms are in the authorization boundary (or equivalent compliance boundary).
• Document the intent: SI-10 applies at trust boundaries (any place data crosses from a less-trusted source into a more-trusted component).

Deliverable: SI-10 control narrative (owner, scope statement, validation approach).

2) Build an “input surface inventory”

Create a lightweight register of input channels per system:

• UI forms and admin panels
• REST/GraphQL endpoints
• File upload endpoints and batch imports
• Webhooks and callback endpoints
• Message topics/queues and event consumers
• CLI/admin scripts that accept external files
• Database write paths that accept user-controlled content indirectly

For each channel, capture:

• input name/type (e.g., email, amount, callback_url, filename, json_payload)
• source (end user, internal user, third party system)
• expected format (schema/regex/type/range)
• validation location (edge/API gateway, service layer, library)
• rejection/handling behavior (HTTP 400 with error code, quarantine queue, dead-letter queue)

Deliverable: Input inventory table (system-by-system).

3) Standardize validation rules and patterns (do not ad hoc it)

Create a small set of approved patterns engineering can reuse:

• Schema validation for structured payloads (JSON schema, protobuf contracts, GraphQL input types).
• Type/length/range allowlists for primitives (int, decimal, bounded strings).
• Encoding normalization (UTF-8 handling, canonicalization where relevant).
• File upload controls (MIME/type allowlist, max size, filename/path handling, malware scanning if required by your risk model).
• Business rule validation (e.g., amount > 0, end_date >= start_date, allowed state transitions).

Prefer “validate then map” patterns: validate raw input, then map into internal types/objects.

Deliverables: Secure coding standard section for input validation; reusable libraries/middleware guidance.

4) Implement validation at the right enforcement points

Auditors look for consistent enforcement. Good enforcement points:

• API gateways/WAF for coarse checks (size limits, content types), not as the only validation.
• Service layer validation (request DTO validation, schema checks).
• Message consumers validate before processing and before persistence.
• Batch import jobs validate each record; quarantine invalid records.

Avoid relying only on front-end validation. Client-side checks are UX controls, not security controls.

Deliverables: Architecture notes showing validation points; code/config examples from representative services.

5) Define “reject vs. sanitize vs. quarantine” rules

Make an explicit decision matrix so teams behave consistently:

Input type	Default handling	Rationale	Evidence
API request fields	Reject with structured error	Prevent malformed data entering system	API error specs, logs
Third party webhooks	Validate signature + schema; reject invalid	Integrity + resilience	webhook validator config
File uploads	Quarantine + scan; reject unsafe	Limits blast radius	quarantine workflow
Queue messages	Dead-letter invalid payloads	Keeps pipeline healthy	DLQ metrics, runbooks

Deliverable: Input handling standard and runbook references.

6) Test validation continuously (design-time and run-time)

Minimum coverage expectations (qualitative, not a numeric quota):

• Unit tests for validators (happy path + edge cases + known-bad inputs).
• API contract tests for schema violations.
• Security testing that includes injection-style payloads where applicable.
• Negative tests in CI/CD that fail builds if schema validation is removed.

Deliverables: Test plans; sample test cases; CI evidence of validator tests running.

7) Monitor and respond to validation failures

Validation failures are a signal. Treat them as:

• security telemetry (possible probing)
• integration health indicators (breaking changes from a third party)
• data quality risks

Operational steps:

• Log validation failures with a stable error code and context (without logging sensitive raw input).
• Alert on spikes and repeated failures.
• Track exceptions/waivers with expiration dates and compensating controls.

Deliverables: Logging standard; sample log events; alert rules; exception register.

8) Map SI-10 to owners, procedures, and recurring evidence (assessment readiness)

Make SI-10 “auditable by design”:

• control statement
• implementation procedure
• evidence list and collection frequency
• systems in scope and validation coverage

Daydream typically fits here as the system of record for the control narrative, ownership, tasking, and evidence requests across engineering teams, so you can prove operation without rebuilding the story for each assessment cycle.

Required evidence and artifacts to retain

Keep artifacts that show design, implementation, and operation:

Governance

• SI-10 control narrative (scope, responsibilities, enforcement approach)
• Secure coding standard / engineering standard for input validation
• Exception/waiver register for nonconforming systems

Technical

• Input surface inventory ¹
• Representative code snippets or configuration showing validation (DTO validators, schema files, gateway policies)
• API schemas/contracts (OpenAPI/GraphQL/protobuf) and validation configs
• File upload validation and quarantine workflow documentation
• Message validation and dead-letter/quarantine procedures

Testing + operations

• Validator unit tests and CI/CD evidence (build logs, test reports)
• Security testing results where input validation is in scope (e.g., SAST findings tied to validation fixes)
• Monitoring/alert evidence for validation errors
• Incident tickets or investigations tied to repeated invalid input patterns (when they occur)

Common exam/audit questions and hangups

1. “Show me where validation occurs for this endpoint.”
   Be ready to walk an assessor from API spec → validator code → rejection behavior → logs.

2. “How do you ensure consistency across teams?”
   Point to standards, shared libraries, and CI checks.

3. “What about third party inputs?”
   Show how you validate webhooks, partner files, EDI, and queue payloads.

4. “How do you handle exceptions?”
   Auditors dislike indefinite waivers. Use time-bound exceptions with documented compensating controls.

Frequent implementation mistakes and how to avoid them

• Relying on client-side validation only. Fix: enforce server-side validation at the boundary.
• Blacklisting bad patterns instead of allowlisting good ones. Fix: constrain by type, length, schema, and allowed values.
• Validating after persistence. Fix: validate before write and before downstream processing.
• Inconsistent error handling that leaks details. Fix: standard error codes; avoid returning stack traces or detailed parser errors externally.
• No evidence trail. Fix: map SI-10 to owner, procedure, and recurring evidence artifacts; collect artifacts continuously, not at audit time. ¹

Enforcement context and risk implications

No public enforcement cases were provided in the source catalog for SI-10, so this page does not cite specific actions. Practically, weak input validation increases the likelihood of exploitable conditions (injection and data integrity failures) and can also cause material operational outages when malformed partner data breaks ingestion pipelines. Treat SI-10 as both a security control and a reliability control.

A practical 30/60/90-day execution plan

First 30 days (Immediate: scope + minimum viable control)

• Assign SI-10 control owner and GRC evidence owner.
• Build an input surface inventory for the highest-risk systems (internet-facing, privileged admin, core data ingestion).
• Publish an input validation standard (one-pager): allowlists, schema validation expectation, reject/quarantine rules, logging expectations.
• Identify quick wins: add schema validation middleware, set API payload size limits, ensure file upload restrictions exist.

Next 60 days (Near-term: consistency + testing)

• Roll out shared validation libraries/patterns per language/framework.
• Add CI checks: require schema files, require validator unit tests for new endpoints.
• Implement monitoring for validation failures and create a triage runbook.
• Start an exception register and force time bounds for deviations.

Next 90 days (Ongoing: coverage + audit readiness)

• Expand inventory coverage to remaining systems and integrations.
• Perform targeted testing of validation controls (negative testing on key endpoints and ingestion paths).
• Produce an assessor-ready package: narrative, inventory, code/config samples, test evidence, monitoring evidence, and exception status.
• Track SI-10 as a recurring control with periodic evidence collection in Daydream so the story stays current across releases.

Frequently Asked Questions

Does SI-10 require validation for internal-only tools?

Yes if the tool ingests untrusted or semi-trusted data (imports, admin inputs, third party feeds). Scope decisions should follow trust boundaries, not whether something is “internal.”

Is input sanitization enough to meet SI-10?

Sanitization can be part of the approach, but auditors typically expect explicit validity checks against defined formats and allowlists. Use sanitization only when you can show it is safe for the data type and sink.

How do we handle third party data feeds that frequently break schema?

Put strict validation at ingestion and quarantine invalid records so pipelines stay healthy. Treat recurring schema breaks as a third party management issue and require contract/interface governance.

What evidence is most persuasive to auditors?

A mapped trail: input inventory → validation rule/spec → code/config enforcement → negative test results → logs/alerts showing failures are handled. Missing any one of these creates audit friction.

Can an API gateway or WAF satisfy SI-10 by itself?

Gateways help with coarse checks, but they rarely validate business rules or full payload semantics. Keep gateway controls, then validate again in the service layer where context exists.

How should we manage exceptions for legacy systems?

Document the specific input paths that lack validation, add compensating controls (segmentation, strict upstream checks, enhanced monitoring), and set an expiry date tied to a remediation milestone.

Footnotes

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

2. NIST SP 800-53 Rev. 5; Source: NIST SP 800-53 Rev. 5 OSCAL JSON

3. NIST SP 800-53 Rev. 5