This checklist is built for enterprise teams running user-generated content: Trust & Safety, Compliance/Legal, Engineering/Platform, Product, and Operations. Use it during RFP/RFI, vendor shortlisting, pilot/POC, go-live, and periodic audits. Every item starts with a clear action and points to verifiable artifacts (docs, logs, SLAs, reports) so your team can make decisions based on evidence, not promises.

Note on scope: Laws and standards vary by jurisdiction and platform size. This guide emphasizes widely recognized requirements and enterprise best practices. Where you see testable metrics (latency, accuracy, etc.), validate them with your own data and workloads.

How to use this checklist

• Align on success criteria: Define your risk priorities (e.g., minors’ safety, illegal items, harassment), service levels, and acceptable trade-offs.
• Collect artifacts up front: Ask vendors to provide documents, sample logs, demo environments, and test datasets you can run independently.
• Run a pilot: Test accuracy by class, latency at load, scalability, and workflow fit. Record outcomes and decide based on evidence.
• Keep it living: Re-run key checks after model updates, new market launches, or policy changes.

1) Regulatory and policy compliance

• Confirm DSA transparency readiness and artifacts.
• Ask for current or sample transparency reports showing moderation actions and automated system accuracy/error rates, as required by the EU Digital Services Act for applicable services from 2024 onward. See the European Commission’s overview in the EU Digital Strategy DSA transparency explainer (2024).
• Review statements-of-reasons and appeals workflows.
• Verify templates and SLAs for user-facing statements explaining moderation decisions and accessible complaint/appeals handling, aligned with the DSA framework outlined in the European Commission DSA Q&A (2024).
• Validate trusted flagger intake and handling procedures.
• Request process docs for priority handling of trusted flagger notices consistent with Article 22 and national designations described on the European Commission’s trusted flaggers page (2024).
• Map Online Safety Act duties and evidence (UK).
• Obtain illegal content risk assessments, child-safety measures (e.g., age gates), user reporting and appeals SLAs, and transparency report templates aligned to Ofcom codes, as introduced in the GOV.UK Online Safety Act explainer (2024).
• Verify GDPR/UK GDPR data protection practices and automated decision safeguards.
• Confirm lawful basis, clear privacy notices, data minimization, retention, and rights handling. Where decisions could have legal or similarly significant effects, ensure meaningful human review and appeals in line with the ICO’s guidance on automated decision‑making and profiling under GDPR.

Pro tip for buyers: When evaluating minors’ safety controls and defaults, you may find additional background in this overview of protecting minors in online content environments (contextual reading).

2) Security and privacy foundations

• Request independent assurance evidence.
• Ask for a recent SOC 2 Type II report (covering the last 6–12 months) and/or a current ISO/IEC 27001 certificate with the Statement of Applicability that clearly includes the moderation services in scope. The AICPA’s Trust Services Criteria and ISO’s overview of ISO/IEC 27001 information security management explain what these attestations typically cover.
• Check data security controls in practice.
• Verify encryption in transit/at rest, key management, access control (RBAC/ABAC), privileged access review cadence, secure software development lifecycle, and incident response runbooks.
• Confirm data residency/localization options.
• Require documentation for EU/UK/US/APAC deployment choices, routing, storage, and cross-border transfer mechanisms (e.g., SCCs), with breach notification timelines.
• Review privacy notices and DPAs.
• Ensure clear, plain-language notices; confirm DPA terms include subprocessors, retention, deletion/portability, and audit rights. You can compare against your internal standards, and review a vendor’s stated privacy responsibilities for alignment, e.g., this example of a privacy responsibilities page (contextual reading).

3) Modalities and abuse taxonomy coverage

• Confirm multimodal support with test data.
• Validate coverage for text, images, video (pre-recorded and frames), audio, and live streams. Ask for confusion matrices and per-class metrics on your datasets. For a deeper technical primer on multimodal recognition and real-time APIs, see this overview of advanced content recognition and real-time monitoring (contextual reading).
• Require a comprehensive taxonomy and clear definitions.
• Check label granularity across nudity/sexual content, minors’ safety, violence, extremism, weapons, drugs, gambling, scams/fraud, hate and harassment, self-harm, privacy/IP violations (e.g., non-consensual intimate imagery), and signals for CSAM.
• Include synthetic and manipulated media.
• Ensure policies and detection support for deepfakes and other synthetic media, with labeling workflows and escalation paths for consent or deception risks.
• Validate multilingual and locale nuances.
• Test performance across your top languages and dialects; confirm ability to localize rules and thresholds per market.

4) AI model evaluation and governance

• Test precision/recall/F1 by class on your data.
• Define risk-weighted acceptance thresholds (e.g., prioritize recall for child-safety categories) and verify with independent test runs.
• Review calibration and thresholding.
• Request reliability plots and show how decision thresholds shift by risk tolerance and enforcement tier.
• Assess fairness and bias.
• Require a bias/fairness audit across demographics and languages relevant to your platform, with mitigation plans.
• Check adversarial robustness.
• Evaluate against obfuscation, slang, emoji/leet-speak, and common evasion tactics; inspect ongoing red‑teaming plans and retraining cadence.
• Establish model governance.
• Ask for model cards, versioning, change logs, rollback plans, and drift monitoring SLOs; require documented approval workflows for model updates.

Acceptance artifact examples: Per-class confusion matrices; threshold sensitivity analysis; calibration plots; red-team test report; model card and change log samples.

5) Real-time performance and scale

• Validate latency under load.
• Run load tests that mirror your traffic. Set targets such as p95 within 100–500 ms and p99 under 1–2 seconds for real-time experiences, then confirm with logs and dashboards. Treat these as best-practice ranges to test against rather than fixed standards.
• Measure throughput and concurrency.
• Define expected TPS and concurrent sessions; inspect autoscaling, queueing/backpressure behavior, and graceful degradation paths.
• Confirm regional/edge deployment options.
• Verify multi-region active-active setups, geo-routing, and data sovereignty alignment; validate failover drills and health-check policies.
• Inspect cold-start mitigation.
• Ask for pre-warmed model pools, incremental rollout strategies, and HITL fallback when latency SLOs are at risk.

Artifacts: Load test reports with p95/p99; autoscaling policies; failover runbooks; incident postmortems; observability dashboards (metrics, traces, logs).

6) Human-in-the-loop operations and reviewer well-being

• Review QA sampling and targets.
• Ask for QA plans (random/stratified sampling, reverse QA) and accuracy targets for human decisions; track appeals-derived error rates. The Trust & Safety Professional Association’s curriculum on content moderation quality assurance outlines common practices.
• Validate escalation and specialization.
• Confirm tiered escalation paths (e.g., child safety, legal, crisis) and specialized reviewer training and certifications where applicable.
• Confirm psychological safety measures.
• Request evidence of wellness programs, workload rotation, tooling for sensitive media (e.g., blur-by-default), and alignment with occupational health frameworks such as ISO 45003 concepts.
• Ensure appeals and user redress SLAs.
• Define turnaround times, communication templates, and quality feedback loops into policy and model updates.
• Verify trusted flagger intake and audits.
• Ensure onboarding criteria, prioritization, and periodic accuracy reviews for trusted flagger sources.

Artifacts: QA policy and sampling plan; calibration session notes; training curricula; wellness program summary; appeals metrics; trusted flagger review logs.

7) Policy customization and localization

• Check configurable policy engines.
• Confirm support for tiered enforcement (warn, restrict, remove), explainability tools for decisions, and configurable thresholds by category and jurisdiction.
• Validate child-safety defaults and safeguards.
• Ensure heightened defaults in minors’ contexts (e.g., stricter nudity thresholds, keyword/visual combinations, location sharing restrictions) and age-appropriate experiences.
• Localize for languages and laws.
• Confirm your team can adapt definitions and thresholds per locale, including synonyms/slang and context-sensitive phrases.

Artifacts: Policy editor screenshots; rules export; per-market configuration guides; explainability UI demos.

8) Auditability and reporting

• Require immutable decision logs.
• Ensure logs capture timestamps, actor IDs, decision reasons, and whether automation was involved—supporting statements of reasons and transparency duties referenced in the DSA. Sample a log export to verify fields and integrity proofs.
• Enable SIEM/BI exports.
• Confirm structured exports or APIs to integrate with your SIEM and analytics tools for audits and ongoing oversight.
• Request transparency report templates and audit packs.
• Ask for templates that break down actions by type, automated vs human, trusted flagger metrics, complaints/appeals, and error rates. Verify availability of “audit packs” with policies, procedures, training records, risk assessments, and effectiveness tests.

Artifacts: Log schemas and samples; SIEM integration guide; transparency report template; evidence binder checklist for audits.

9) Integration and developer experience

• Verify APIs/SDKs and webhooks.
• Request API references, client libraries, and webhook examples; confirm idempotency, retries, and rate limits.
• Test in sandbox and staging.
• Ensure realistic test data, a stable sandbox, and a clear path to staging and production environments with environment-specific credentials.
• Inspect observability and incident response.
• Review metrics/traces/logs dashboards, alerting standards, runbooks, and on-call coverage.
• Confirm data residency options and migration support.
• Validate regional deployments, transfer mechanisms, and documented procedures for data migration or deletion.

Artifacts: API reference; SDK samples; webhook payload specs; sandbox credentials; observability dashboard screenshots; IR playbooks.

10) Commercials, SLAs, and procurement terms

• Clarify pricing and overage.
• Request a transparent price model (per call/MAU/tiered), enterprise minimums, overage rates, and volume discounting policies.
• Define SLAs and credits.
• Set uptime, support TAT, and latency SLOs; specify credits for breaches and measurement methods.
• Structure the POC with acceptance criteria.
• Agree on datasets, per-class accuracy targets, latency at defined loads, HITL response times, and artifact delivery (confusion matrices, latency logs, QA summaries) before starting.
• Plan implementation, migration, and exit.
• Ask for onboarding timelines, training, change management support, and termination assistance with data export/deletion.

Artifacts: Pricing and SLA schedules; POC plan and success criteria; implementation Gantt; termination checklist.

Compact pilot test plan (copyable)

Use this 10-step sequence to evaluate short-listed vendors in 2–4 weeks:

1. Define scope and risks: List top content types, markets, and risk categories to prioritize in testing.
2. Prepare datasets: Curate labeled samples per class and language; include adversarial variants and borderline cases.
3. Establish acceptance thresholds: Set per-class targets (e.g., higher recall for child safety; tighter precision for borderline policy content).
4. Stand up environments: Connect to vendor sandbox; confirm API/webhook integrations; enable logs/metrics.
5. Run baseline accuracy tests: Compute precision/recall/F1 by class; produce confusion matrices and calibration plots.
6. Load and latency testing: Simulate real traffic; record p95/p99; observe autoscaling and backpressure.
7. HITL workflow drills: Submit edge cases; measure reviewer TAT, escalation correctness, and QA sampling.
8. Compliance artifacts review: Collect transparency templates, SoR examples, DPAs, data residency documentation, and audit evidence.
9. Evaluate operational fit: Assess policy editor usability, localization, observability dashboards, and incident playbooks.
10. Score and decide: Weight results by risk; document trade-offs and governance requirements for go-live.

Next steps

• Align stakeholders on this checklist, customize acceptance criteria, and launch a measured pilot with 2–3 vendors. Document evidence and revisit assumptions after the first month in production.
• If you are exploring market options, solutions like DeepCleer can be evaluated alongside others to assess fit for multimodal moderation and risk controls. Disclosure: DeepCleer is our product.

For additional background on how teams evolve from manual review to AI-assisted operations, see this overview on moving from manual to intelligent content moderation systems.