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Implement real-time input filtering using automated moderation tools

Control activities

Typical evidence

Should include?

Integrating automated moderation tools to filter inputs before they reach the foundation model. For example, integrating third-party moderation APIs, implementing custom filtering rules, configuring blocking or warning actions for flagged content, and establishing confidence thresholds based on risk category and severity

B005.1 Config: Input filtering

Moderation tool integration showing API configuration, filtering rules, action settings (block/warn/modify), and confidence thresholds for different violation categories - this could be screenshots of configuration files, admin dashboard settings, or API integration code. Example moderation tools: OpenAI Moderation API, Claude content filtering, VirtueAI/Hive/Spectrum Labs

Typical location
Eng: User LLM input filtering logicEngineering Tooling
Capabilities
Text-generationVoice-generationImage-generation
May include?

Documenting the moderation logic and rationale. For example, explaining chosen moderation tools, threshold justifications, and decision criteria for different risk categories.

B005.2 Documentation: Input moderation approach

Document explaining moderation approach including tool selection rationale, threshold settings with justifications, action logic for different violation types, and examples of how different input categories are handled.

Typical location
Internal processesEngineering Practice
Capabilities
Text-generationVoice-generationImage-generation

Providing feedback to users when inputs are blocked.

B005.3 Demonstration: Warning for blocked inputs

User-facing messages or UI flows showing how blocked inputs are communicated to users - this could be error messages, warning dialogs, or alternative suggestions provided when content is filtered.

Typical location
Product
Capabilities
Text-generationVoice-generationImage-generation

Logging flagged prompts for analysis and refinement of filters, while ensuring compliance with privacy obligations.

B005.4 Logs: Input filtering

Logging system showing how flagged inputs are captured, what metadata is included/excluded for privacy, retention policies, and audit trail - may include privacy documentation explaining logging disclosures to users.

Typical location
Logs
Capabilities
Text-generationVoice-generationImage-generation

Periodically evaluating filter performance and adjusting thresholds accordingly. For example, accuracy, latency, false positives/negatives.

B005.5 Documentation: Input filter performance

Report or dashboard showing analysis of filter performance metrics (false positives, false negatives, accuracy, latency) and documented threshold adjustments made based on performance data - should include timestamps and rationale for changes.

Typical location
Engineering Practice
Capabilities
Text-generationVoice-generationImage-generation

Organizations can submit alternative evidence demonstrating how they meet the requirement.