LLM-based System Safety Measure

A LLM-based System Safety Measure is a system safety measure for LMM-based systems (for safety risks within AI systems).

• AKA: LLM Safety Filter, Language Model Safety System, AI Safety Evaluator, LLM Guardrail System.
• Context:
  • It can implement Real-time Content Moderation through moderation api.
  • It can utilize Safety Tuned LLM for content filtering.
  • It can deploy Guardrail Framework for safety enforcement.
  • It can perform Infrastructure Hardening via security architecture.
  • It can maintain Continuous Monitoring for safety compliance.
  • ...
  • It can integrate with OpenAI Moderation API for content classification.
  • It can support Meta Llama Guard through safety taxonomy.
  • It can utilize Fiddler Guardrails for risk scoring.
  • It can implement NVIDIA NeMo Guardrails via safety boundary.
  • ...
  • It can range from being a Basic Rule Filter to being an Advanced LLM Judge, depending on its implementation approach.
  • It can range from being a Simple Pattern Matcher to being a Complex Safety Analyzer, depending on its detection capability.
  • It can range from being a Local Safety Check to being a Distributed Safety System, depending on its deployment scale.
  • ...
  • It can provide Safety Protocols through tiered approach.
  • It can enforce Security Standards via compliance framework.
  • It can implement Data Protection through encryption protocol.
  • It can enable Anomaly Detection via monitoring system.
  • It can support Manual Review through human oversight.
  • ...
• Examples:
  • Commercial Safety Solutions, such as:
    • Cloud Provider Systems, such as:
      • Azure AI Content Safety for enterprise protection.
      • OpenAI Safety API for content moderation.
    • Specialized Platforms, such as:
      • Lakera Guard for rule enforcement.
      • Prompt Armor for input validation.
  • Safety Frameworks, such as:
    • Enterprise Solutions, such as:
      • Anthropic Safety Levels for risk management.
      • OWASP LLM Top 10 for vulnerability protection.
    • Monitoring Platforms, such as:
      • Confident AI for safety tracking.
      • Langfuse for output monitoring.
  • Security Implementations, such as:
    • Infrastructure Protections, such as:
      • Zero Trust Architecture for model isolation.
      • Containerized Deployment for system hardening.
  • ...
• Counter-Examples:
  • Traditional Content Filter, which lacks adaptive learning.
  • Static Security Rule, which lacks contextual analysis.
  • Manual Safety Process, which lacks automated detection.
  • Basic Pattern Matcher, which lacks semantic understanding.
• See: Safety Mechanism, Content Moderation System, Security Framework, Language Model System, Safety Protocol, Infrastructure Security, Monitoring System.

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