SI-10(2): Review and Resolve Errors

SI-10(2): review and resolve errors requirement means you must review input validation errors and fix them within a defined timeframe set by your organization (the control parameter). To operationalize it, you need centralized error capture, clear ownership, severity-based triage, and auditable evidence that each validation error was reviewed, resolved, and verified within your stated SLA. ¹

Key takeaways:

• Define “within X time” for input validation errors, then enforce it through ticketing, logging, and on-call workflows. ¹
• Treat validation errors as security-relevant signals; track review, root cause, and remediation to closure with verification. ¹
• Audit-readiness depends on evidence: logs, tickets, RCA records, and trend reporting tied to the stated timeframe. ¹

Compliance teams often see “input validation” as an application security detail owned by engineering. SI-10(2) turns it into an operational requirement: errors must be reviewed and resolved within a defined window, and you must be able to prove it. That proof matters because input validation failures can indicate attempted injection, malformed requests hitting exposed endpoints, broken client integrations, or unsafe parsing behavior. Even if the immediate error is “handled,” a backlog of unreviewed validation errors becomes a blind spot and a reliability drag.

This page gives requirement-level implementation guidance you can hand to an application owner, SOC lead, or platform team and expect consistent execution. The core work is straightforward: set the timeframe parameter, define what counts as an input validation error in your environment, route errors into a system of record, and run a repeatable triage-to-fix loop with clear closure criteria. Your job, as a Compliance Officer, CCO, or GRC lead, is to make the requirement testable: scope, ownership, workflows, and evidence. ²

Regulatory text

Requirement (verbatim): “Review and resolve input validation errors within {{ insert: param, si-10.2_prm_1 }}.” ¹

What the operator must do:

1. Set the parameter for the time window (“within X”). SI-10(2) is not implementable until your organization defines that value in a policy, standard, or control statement. ¹
2. Continuously capture input validation errors from in-scope systems (applications, APIs, integration services, and relevant platforms).
3. Review each error (human review or approved automated classification with human oversight) to determine cause, impact, and required remediation.
4. Resolve each error within the defined window, where “resolve” includes a fix, configuration change, compensating control, or documented determination that the event is expected and requires no change, with approval and closure rationale. ¹

Plain-English interpretation (what SI-10(2) really asks for)

• “Input validation errors” are failures when a system rejects, sanitizes, truncates, or otherwise flags inbound data as not conforming to expected format, type, length, encoding, schema, or business rules.
• “Review” means you do more than log-and-forget. Someone (or a governed automation) looks at the error, classifies it (bug, attack noise, misconfiguration, integration drift), and decides the next action.
• “Resolve” means the error is driven to closure with a documented outcome. Closure should include verification: the error stops occurring for the addressed cause, or you can explain why it is acceptable and controlled.
• “Within X” is your SLA. Examiners will test against what you declared, not what you intended.

Target keyword alignment: This page operationalizes the si-10(2): review and resolve errors requirement by turning “within X” into an enforceable workflow and evidence set. ¹

Who it applies to (entity and operational context)

Entity scope (typical):

• Federal information systems and contractor systems handling federal data implementing NIST SP 800-53 Rev. 5 controls. ¹

Operational scope (what systems to include):

• Public-facing and internal web applications, APIs, mobile backends, and data ingestion pipelines that validate inbound input.
• Identity and access flows where validation errors can signal enumeration or malformed token abuse (treat as security telemetry).
• Third-party integrations where partners send payloads; validation failures can reflect contract drift or risky parsing. “Third party” issues still belong in your error review and resolution loop.

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

1) Define the parameter and scope in control language

Create a short control statement your auditors can test:

• Timeframe parameter: Define “within X” for review and resolution of input validation errors. ¹
• In-scope systems: List system boundaries (apps/APIs/services) or reference your system inventory.
• Definition: Document what your organization counts as an “input validation error” (examples: schema validation failures, request body parse errors, rejected file uploads, invalid query parameters, failed deserialization).

Deliverable: a control implementation statement in your SSP/control library that maps SI-10(2) to owners and evidence. ¹

2) Centralize capture: logs, APM, and alerting

Minimum operational pattern:

• Application logs emit structured events for validation failures (fields: service, endpoint, error type, client identifier, correlation ID, timestamp).
• Centralize into a log platform/SIEM or observability stack.
• Configure alert rules for spikes, high-risk endpoints, and repeated failures from the same source.

Goal: you can produce a list of validation errors for any period and show their disposition.

3) Create a “system of record” workflow (tickets or cases)

Decide how an error becomes trackable work:

• Auto-ticketing for defined conditions (for example, repeated errors, high sensitivity endpoints, or errors linked to security exceptions).
• Manual triage queue for low-signal noise, with sampling rules documented.

Each tracked item should have:

• classification (bug / attack-like / integration mismatch / expected)
• severity/priority
• owner (service team) and due-by date based on the SI-10(2) parameter
• remediation plan and test/verification notes

4) Triage: review criteria that auditors can understand

Define “review complete” criteria:

• Confirm source (internal client, third party, anonymous internet, batch job).
• Identify root cause category (bad client input, missing server-side checks, parsing library issue, config regression).
• Determine impact (availability degradation, data quality issue, security concern).
• Decide action: fix, block, rate limit, update schema/contract, improve validation rules, or document as acceptable with approval.

Practical tip: if you cannot explain how you differentiate “expected noise” from “actionable,” your reviewers will struggle to defend closure decisions.

5) Resolve: remediation paths and closure rules

Common resolution types:

• Code fix: correct validation logic, error handling, and secure parsing.
• Contract fix: publish updated schema and versioning; coordinate with third party senders.
• Compensating control: WAF/API gateway rules, rate limits, input size constraints.
• Accepted risk: only for truly expected cases; require rationale and approval trail, and revisit if frequency changes.

Closure requires evidence of verification (test results, post-deploy monitoring notes, or reduced recurrence supported by logs).

6) Measure compliance and drive continuous improvement

Track at least:

• number of validation errors created (by system)
• time to review
• time to resolve
• backlog aging against the defined parameter
• repeat offenders (same endpoint/client)

This is where tools like Daydream fit naturally: mapping SI-10(2) to a control owner, implementation procedure, and recurring evidence artifacts is the difference between “we do it” and “we can prove it on demand.” ¹

Required evidence and artifacts to retain

Keep evidence that is:

• Time-bound (shows review and resolution within your parameter)
• Traceable (ties back to a specific error event)
• Repeatable (supports sampling across periods)

Suggested evidence pack:

• SI-10(2) control statement with the defined timeframe parameter. ¹
• System inventory or boundary list showing in-scope apps/APIs.
• Logging standard for validation events (fields, retention, and routing).
• Sample log extracts showing validation errors (redact sensitive data).
• Ticket/case records: created date, triage notes, assignment, due-by, closure, verification evidence.
• Change records: PR links, deployment records, configuration diffs for fixes.
• Exception/acceptance records for “expected” errors with approval and review cadence.
• Metrics snapshots (dashboards or reports) that show backlog and SLA performance.

Common exam/audit questions and hangups

Auditors tend to probe:

• What is your “within X” value and where is it documented? If it’s not written, it doesn’t exist for assessment purposes. ¹
• How do you define “input validation error”? They will test edge cases (file uploads, API gateway rejections, deserialization errors).
• Show me a sample of errors and prove review + resolution happened within the window. Expect sampling across different systems.
• How do you prevent silent failures? If an app logs locally but nothing central collects it, review cannot be demonstrated.
• Who owns closure when a third party causes malformed input? “It’s their fault” is not closure; your system still must review and resolve.

Frequent implementation mistakes (and how to avoid them)

1. Setting the timeframe but not operationalizing it. Avoid by adding due-by timestamps in tickets and SLA dashboards tied to the parameter. ¹
2. Treating validation errors as pure “dev bugs.” Some are security signals. Route suspicious patterns to security triage.
3. No closure criteria. Define what counts as “resolved” versus “mitigated” versus “accepted,” and require verification notes.
4. Over-logging sensitive input. Validation events should not store secrets or full payloads; log safely (hashes, sizes, schema versions).
5. Ignoring third-party integrations. Build a contract management loop so partner payload drift becomes trackable work, not permanent noise.

Risk implications (why the control exists)

Unreviewed validation errors create:

• Security exposure: recurring malformed input can be probing for weaknesses; poor parsing and validation are common precursors to injection and deserialization issues.
• Availability risk: spikes in invalid requests can degrade services and mask denial-of-service patterns.
• Data quality risk: ingestion pipelines that “fail open” or partially accept malformed input can corrupt downstream analytics and reporting.

SI-10(2) is a medium-severity control expectation in many baselines because it connects day-to-day engineering hygiene to detectable, auditable operational discipline. ¹

Practical 30/60/90-day execution plan

First 30 days (establish testable requirements)

• Define the SI-10(2) timeframe parameter and publish it in your control documentation. ¹
• Identify in-scope systems and error sources (apps, APIs, gateways, ingestion jobs).
• Standardize what an “input validation error event” looks like in logs.
• Stand up a single triage queue (ticketing or case management) with an owner and backup.

Days 31–60 (make it operational and measurable)

• Implement centralized collection for validation error events for in-scope systems.
• Create routing rules: which errors become tickets automatically vs. batched review.
• Train service owners on review criteria and closure requirements.
• Start weekly reporting: backlog, aging, and examples of resolved items.

Days 61–90 (prove it, then harden)

• Run an internal control test: sample validation errors and trace each to review + resolution evidence within the parameter. ¹
• Add guardrails: alerting thresholds, runbooks, and escalation paths.
• Formalize exception handling for “expected” validation errors with approval.
• Use Daydream (or your GRC system) to map SI-10(2) to the control owner, implementation procedure, and recurring evidence artifacts so audits become a retrieval exercise, not a scramble. ¹

Frequently Asked Questions

What counts as an “input validation error” for SI-10(2)?

Define it explicitly for your environment: schema violations, type/length/format rejections, parsing/deserialization failures, invalid file uploads, and API gateway validation failures all commonly qualify. Your definition must match what your logging and triage workflow captures. ¹

Does “resolve” always mean a code change?

No. Resolve means you reach an approved closure state within your timeframe: code fix, config change, compensating control, or documented acceptance with rationale and approval. Keep verification evidence for whatever path you choose. ¹

How do we comply if validation errors are constant “internet noise” on public APIs?

Use classification rules. You still review patterns, then resolve by blocking, rate limiting, tightening validation, or documenting recurring benign patterns with approval and monitoring triggers for change. ¹

Can automated tooling satisfy the “review” requirement?

Automation can triage and classify, but you should document the logic, ownership, and oversight. Auditors usually expect evidence that the automation is governed and that exceptions or high-risk patterns get human review. ¹

What evidence is most persuasive in an assessment?

A sampled chain from log event → ticket creation → triage notes → fix/change record → verification note, all time-stamped and within the defined parameter. Add a dashboard showing backlog aging against the same parameter. ¹

How do we handle third-party-caused validation errors (partner integrations)?

Treat them as in-scope: review, open a tracked item, coordinate a contract/schema fix, and close with verification. If the partner cannot remediate quickly, use compensating controls and document interim risk acceptance. ¹

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