SI-9: Information Input Restrictions

To meet the si-9: information input restrictions requirement, you must define and enforce technical and procedural limits on what data users, systems, and third parties can submit into your applications, interfaces, and workflows, so untrusted or malformed input cannot trigger security failures. Operationalize SI-9 by scoping input points, setting explicit allow-rules, implementing validation/sanitization, and keeping testable evidence. ¹

Key takeaways:

• Inventory every “input surface” (UI forms, APIs, file uploads, integrations) and apply explicit restrictions per data type and trust level.
• Enforce restrictions in code and platform controls (allowlists, schema validation, size/rate limits), backed by secure SDLC gates and tests.
• Retain evidence that restrictions exist, are consistently applied, and are verified (standards, configs, test results, change records). ²

SI-9 focuses on a simple failure mode with expensive consequences: systems accept input they were never designed to handle, and that input becomes the attack path. For a CCO or GRC lead, the practical challenge is translating “input restrictions” into a control that engineers can implement consistently across products, internal tools, and third-party connected systems.

This requirement page is written to help you stand up SI-9 quickly, in a way that survives assessment. The core approach is to treat “input” as an inventory and governance problem first, then as an engineering pattern: identify every place data enters the boundary, classify the trust level and acceptable formats, enforce strict validation and handling rules, and verify those rules through repeatable testing and change control. Your evidence should show traceability from policy to implementation to verification, not just a general secure coding statement.

SI-9 is part of NIST SP 800-53 Rev. 5 and is commonly inherited or referenced in federal system security plans and contractor environments handling federal data. ¹

Regulatory text

Excerpt (as provided): “NIST SP 800-53 control SI-9.” ²

Operator meaning: You must restrict information inputs so only expected, validated, and properly formatted data is accepted into your system components. In practice, that means you define allowed input characteristics (type, format, length, range, encoding, origin, rate), implement controls that enforce those rules at the right layers (client, server, API gateway, WAF, message bus), and test that invalid input is rejected safely. ¹

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Plain-English interpretation (what SI-9 expects)

SI-9 expects you to prevent “unexpected input” from becoming a security or reliability event. Unexpected input includes:

• Data that violates business rules (negative quantities, impossible dates).
• Data that violates technical rules (too long, wrong encoding, malformed JSON/XML).
• Data with hostile intent (injection strings, traversal patterns, script payloads).
• Input sent at unsafe volumes (bursts intended to exhaust resources).
• Input from untrusted sources where trust is assumed (unauthenticated integrations, spoofed service identities).

A clean SI-9 implementation has two traits assessors look for:

1. Specificity: documented allow-rules per input surface, not vague “sanitize inputs.”
2. Consistency: a standard pattern used across teams and services, with verification evidence. ¹

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Who it applies to (entity and operational context)

Entity scope

• Federal information systems implementing NIST SP 800-53 controls. ¹
• Contractor systems handling federal data where NIST SP 800-53 is flowed down contractually or used to meet program requirements. ²

Operational scope (where SI-9 shows up)

• Customer-facing apps (web/mobile), admin portals, internal tools.
• Public and private APIs, partner APIs, webhook receivers.
• File upload pipelines (documents, images, CSV, logs).
• Messaging and event ingestion (queues, streaming topics).
• ETL jobs and data pipelines pulling from third parties.
• Authentication and identity flows (claims, SAML/OIDC attributes).
• Endpoint telemetry agents ingesting device data.

Third-party risk angle If a third party can send you data (API integration, managed service, outsourced operations), that third party expands your input surface. SI-9 is partly a contract and interface governance problem: define input contracts, enforce them technically, and monitor for violations.

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

1) Assign ownership and define the control boundary

• Name a control owner (often AppSec, Platform Security, or Engineering). GRC owns oversight and evidence.
• Define systems in scope and the environments that matter (prod, staging, shared services).
• Document where you will enforce restrictions (application layer, gateway, WAF, ETL validators). ¹

Daydream tip: In Daydream, map SI-9 to a single accountable owner plus supporting owners (API gateway team, AppSec, data engineering). Track recurring evidence tasks so SI-9 doesn’t become a one-time policy upload.

2) Inventory and classify “input surfaces”

Build an inventory that is audit-ready:

• UI forms (including hidden fields and client-side validated fields).
• API endpoints (REST, GraphQL, gRPC).
• File upload entry points.
• Integration entry points (webhooks, SFTP drops, EDI feeds, service bus topics).
• Admin-only ingestion paths (support tooling, bulk import features).

For each input surface, record:

• Data types accepted (strings, numbers, structured objects, files).
• Trust level (public internet, authenticated user, internal service, third party).
• Security impact if abused (auth bypass, data corruption, RCE, resource exhaustion).
• Enforcement points (shared validation library, schema validation, gateway rules).

3) Define explicit restrictions (turn “should” into “must” rules)

Create a short standard your teams can follow. Use an allowlist mindset:

• Format/schema: JSON schema, protobuf schema, strict XML parsing rules.
• Type/range/length: numeric bounds, max string lengths, enum allowlists.
• Encoding: reject mixed or invalid encodings; normalize where required.
• File restrictions: allowlisted MIME types, max size, content sniffing, decompression limits, malware scanning where applicable.
• Rate/volume: request size limits, rate limits, concurrency limits on expensive endpoints.
• Origin and identity: mTLS, signed webhooks, service identities, token audience checks for machine inputs.

Document these as enforceable engineering requirements (coding standards + gateway baselines), not as general policy statements. ¹

4) Implement controls at the correct layers

A practical implementation pattern that scales:

Baseline layer (platform/gateway)

• Request body size limits.
• Content-type enforcement.
• Rate limits and burst controls.
• Block obviously malformed payloads early.

Application layer (authoritative validation)

• Server-side validation for every field (never trust client-side checks).
• Central validation libraries to reduce drift.
• Safe parsing (no dangerous deserialization patterns).
• Consistent error handling (fail closed; do not echo raw input).

Data layer (defense-in-depth)

• Parameterized queries/ORM protection.
• Constraints and checks in database schema where feasible.
• Quarantine flows for “suspicious but needed” data (manual review lanes).

5) Verify with tests and security gates

Assessment-grade verification usually includes:

• Unit tests for validators and schema checks.
• Negative tests: known-bad inputs rejected (oversized payloads, invalid enums, malformed JSON).
• SAST rules or secure coding checks that flag raw concatenation into queries or unsafe parsers.
• DAST/API testing focused on input vectors (injection, path traversal, SSRF payload patterns).
• CI/CD gates: changes to input schemas require review by code owners or AppSec.

Keep the results and tie them to releases. ¹

6) Operational monitoring and exception handling

• Log validation failures with enough context for triage (endpoint, rule violated), without storing sensitive raw payloads.
• Alert on spikes in rejection rates or repeated violations from a source.
• Create an exception process for legacy endpoints that cannot immediately meet the standard (time-bound remediation plan, compensating controls). ¹

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Required evidence and artifacts to retain

Use this as your SI-9 evidence checklist:

Governance artifacts

• SI-9 control statement mapped to owner, scope, and review cadence. ²
• Secure coding standard section on input validation and parsing rules.
• Architecture/security standard for API gateways and ingestion services.

Technical artifacts

• Input surface inventory (system, endpoint/interface, trust level, enforcement point).
• Schema definitions (OpenAPI/JSON schema/protobuf) and validation configs.
• Gateway/WAF configuration exports showing size/type/rate restrictions.
• Code references to shared validation libraries or framework validators.

Verification artifacts

• Test plans and results (unit tests, integration tests, DAST runs) demonstrating rejection of invalid inputs.
• Change tickets/PRs showing review for schema or validator changes.
• Exception register and compensating controls for known gaps.

Daydream tip: Store artifacts as recurring evidence objects (configs + test outputs + change approvals). Assessments often fail SI-9 on “we do it, but can’t prove it.”

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Common exam/audit questions and hangups

Expect assessors to ask:

• “Show me where input validation is defined and enforced for your highest-risk APIs.”
• “How do you prevent oversized payloads or unexpected file types?”
• “Is validation server-side, and is it consistent across microservices?”
• “How do you manage third-party inputs like webhooks and batch files?”
• “Show test evidence that invalid input is rejected safely.” ¹

Hangups that slow teams down:

• Relying on developer discretion instead of a standard library/pattern.
• Missing coverage for non-HTTP inputs (queues, ETL, admin imports).
• Evidence scattered across repos with no traceability.

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Frequent implementation mistakes (and how to avoid them)

1. Only client-side validation

• Fix: require server-side validation as the enforcement point; treat client-side as UX only.

2. Blacklist-based filtering

• Fix: define allowlists and strict schemas; blacklists become unmaintainable.

3. No limits on size, decompression, or parsing

• Fix: set request size caps, file caps, and safe parsing limits at gateway and app layers.

4. Inconsistent rules across services

• Fix: publish a “golden path” library and CI checks; block merges that bypass validators.

5. No evidence

• Fix: set up an evidence bundle per critical system: inventory → configs → tests → change control. ¹

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Enforcement context and risk implications

No public enforcement cases were provided in the supplied source catalog for SI-9, so this page does not list specific actions or settlements.

Risk-wise, weak input restrictions commonly contribute to:

• Injection and deserialization vulnerabilities.
• Data integrity failures (bad data polluting downstream systems).
• Availability incidents from oversized or high-rate inputs.
• Third-party integration abuse (spoofed webhooks, malformed batch files).

From an assessment perspective, SI-9 findings often appear as “control implemented inconsistently” or “insufficient evidence,” even when engineering teams believe they validate inputs.

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A practical 30/60/90-day execution plan

First 30 days (triage and standard)

• Assign SI-9 owner and publish a one-page engineering standard for input restrictions. ¹
• Inventory top-risk input surfaces (internet-facing APIs, auth flows, file uploads, third-party webhooks).
• Implement gateway baselines for size/type limits and rate limits on the most exposed endpoints.
• Define evidence storage: where configs, schemas, and test outputs will live.

By 60 days (implement and prove)

• Roll out server-side schema validation for top-risk APIs and ingestion jobs.
• Add negative test cases to CI for those interfaces; retain test outputs per release.
• Formalize the exception register for endpoints that cannot conform yet, with compensating controls and owners.

By 90 days (scale and stabilize)

• Expand inventory to remaining systems and “non-obvious” inputs (bulk import tools, queues, ETL).
• Standardize shared libraries/patterns across teams; add code scanning or lint rules to reduce drift.
• Run an internal control test: pick sample endpoints and walk auditors through inventory → enforcement → test evidence → change history.

(Phase labels are guidance, not a regulatory timeline.) ¹

Frequently Asked Questions

Does SI-9 require allowlists for every single field in every application?

You need explicit restrictions appropriate to the risk and input type. Start with high-risk entry points (public APIs, uploads, third-party integrations) and standardize a schema/validation approach that can expand across the rest.

Are WAF rules enough to satisfy SI-9?

A WAF helps but rarely closes SI-9 by itself because application logic still needs authoritative server-side validation. Treat WAF/gateway controls as baseline filtering and rate/size enforcement.

How do I handle third-party webhooks and partner feeds under SI-9?

Treat them as untrusted input unless you cryptographically verify source and integrity (for example, signed payloads and strong service identity). Enforce schema validation and replay protections, and document the input contract in the integration spec.

What evidence is most persuasive to auditors for SI-9?

A tight bundle: input inventory, the written validation standard, configs (gateway limits + schema validators), and test outputs showing invalid inputs are rejected. Tie each artifact to the system and release/change record. ²

We have legacy systems that can’t add strict validation quickly. What’s acceptable?

Use a documented exception with compensating controls (gateway size limits, extra monitoring, restricted network exposure) and a tracked remediation plan owned by engineering. Keep the exception list current and review it on a fixed cadence.

How should we operationalize SI-9 tracking across many teams?

Put SI-9 into your control matrix with a single accountable owner, then collect recurring evidence per system. Daydream is a practical way to assign owners, track implementation procedures, and request evidence on a schedule so SI-9 stays assessment-ready. ²

Footnotes

1. NIST SP 800-53 Rev. 5

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