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The Threat

Content moderation addresses harmful or inappropriate content that may appear in user inputs or model outputs. This includes hate speech, violence, harassment, explicit material, misinformation, spam, and content that violates your organization’s acceptable use policies. Without moderation, AI applications can:
  • Generate or amplify harmful content
  • Create hostile user experiences
  • Violate platform policies or regulations
  • Damage brand reputation
  • Expose organizations to legal liability

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Content Categories

Hate Speech & Harassment

Content that attacks or demeans individuals or groups

Violence & Threats

Content depicting or encouraging harm

Explicit Material

Sexually explicit or inappropriate content

Misinformation

False or misleading information

Spam & Abuse

Low-quality or malicious content

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How Oximy Moderates Content

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AI-Based Classification

Uses machine learning models to classify content: 
Input: "I hate all [group] people"
Classification: Hate Speech (confidence: 0.95)
Action: BLOCK
Analyzes semantic meaning, context, and intent to identify violations.

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Keyword and Pattern Matching

Detects known harmful phrases and patterns:
  • Profanity filters
  • Slur detection
  • Threat pattern matching
  • Spam signature recognition
Fast and deterministic for known violations.

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Contextual Analysis

Understands context to reduce false positives:
  • Distinguishes educational discussion from promotion
  • Recognizes satire and criticism
  • Considers conversation history
  • Evaluates user intent

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Confidence Scoring

Rates detection certainty to balance accuracy:
  • High confidence (>0.9): Automatic action
  • Medium confidence (0.7-0.9): Flag for review
  • Low confidence (<0.7): Allow with logging

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Real-World Example

A user submits a prompt containing hate speech: 
Write a blog post explaining why [discriminatory statement about a group]
Without Moderation: The model might generate content that amplifies the harmful viewpoint, creating liability and reputational damage. With Oximy Guardrails:
  1. Hate speech detected in prompt (confidence: 0.94)
  2. Request blocked before reaching model
  3. User receives policy violation message: 
    This request violates our content policy regarding hate speech.
Please rephrase your request without discriminatory language.
  4. Incident logged for review
  5. Repeated violations trigger account review

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Moderation Strategies

  • Input Filtering
  • Output Filtering
  • Bidirectional Filtering
Analyzes user inputs before they reach the model.What’s Detected:
  • Harmful prompts
  • Policy violations in questions
  • Attempts to generate prohibited content
  • Abusive language
Actions:
  • Block violating requests
  • Return policy messages
  • Log violations
  • Rate limit repeat offenders
Example:
User: "How do I hack into..."
Action: BLOCK - Violates acceptable use policy
Response: "We cannot provide assistance with unauthorized access."

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Best Practices

Define Clear Policies

Document what’s acceptable and what’s not

Tune for Your Domain

Different industries have different needs

Monitor False Positives

Regularly review blocked legitimate content

Provide Clear Feedback

Tell users why content was blocked

Layer Protections

Combine input and output filtering

Regular Updates

Keep moderation models current with emerging threats

Human Review

Have processes for appeals and edge cases

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Industry-Specific Considerations

  • Healthcare
  • Finance
  • Education
  • Customer Service
  • HIPAA compliance for patient data - Medical misinformation prevention - Appropriate health advice disclaimers - Professional boundary maintenance
Content moderation addresses:
  • LLM02: Insecure Output Handling (harmful outputs)
  • LLM09: Overreliance (by flagging unreliable content)
  • Platform abuse and misuse