Child Safety Policy
Overview
SafetyKit's Child Safety Policy provides comprehensive protection against child sexual abuse material (CSAM) and grooming behavior. Our solution combines hash-based detection with advanced AI models to identify both known and novel harmful content while minimizing false positives.
Platforms can deploy this policy to meet regulatory requirements, protect minors, and maintain trust — with industry-leading accuracy that reduces operational burden on moderation teams.
Detection Capabilities
SafetyKit detects and prevents multiple categories of child safety violations:
Known CSAM
Hash matching against comprehensive databases including all NCMEC hashes.
Novel CSAM
AI-powered detection of previously unseen abusive imagery and content.
Grooming Behavior
Detection of predatory communication patterns and solicitation attempts.
Minor Exploitation
Identification of content that sexualizes or exploits minors in any form.
NCMEC Hash Integration
SafetyKit integrates with all National Center for Missing & Exploited Children (NCMEC) hash databases to ensure comprehensive coverage of known CSAM:
Hash Technologies
- PhotoDNA: Microsoft's robust hashing for image matching
- MD5/SHA-1: Cryptographic hashes for exact file matching
- PDQ: Facebook's perceptual hash for near-duplicate detection
- Video hashing: Frame-level analysis for video content
Database Coverage
- Full NCMEC hash list integration
- Real-time hash list updates
- International law enforcement databases
- Industry shared hash databases (GIFCT)
Edge Case Handling
Child safety moderation involves complex edge cases that require nuanced policy guidance. SafetyKit provides both AI models and policy frameworks to handle these difficult decisions:
Age Ambiguity
Sophisticated age estimation with conservative thresholds for borderline cases.
Context Analysis
Distinguishing harmful content from legitimate contexts like news or education.
AI-Generated Content
Detection of synthetic CSAM and AI-generated exploitative imagery.
Policy Guidance
Expert-developed frameworks for consistent decision-making on complex cases.
Precision & Recall
SafetyKit is engineered to reduce false positives without compromising recall — ensuring harmful content is caught while minimizing burden on moderation teams and avoiding wrongful actions against users.
Our Approach
- High recall: Comprehensive detection ensures harmful content does not slip through
- Reduced false positives: Advanced models distinguish harmful content from benign imagery
- Confidence scoring: Granular confidence levels enable appropriate routing and escalation
- Continuous improvement: Models are regularly updated based on new patterns and feedback
Regulatory Compliance
SafetyKit's Child Safety Policy helps platforms meet legal obligations across jurisdictions:
- NCMEC Reporting: Automated CyberTipline report generation and submission
- Take It Down Act: Compliance with minor intimate image removal requirements
- EU Digital Services Act: Meeting DSA obligations for illegal content
- UK Online Safety Act: Priority illegal content detection and removal
- Australian Online Safety Act: Compliance with eSafety requirements
Our compliance infrastructure handles evidence preservation, chain of custody, and regulatory reporting to reduce legal risk and operational burden.
Enforcement with SafetyKit
When enabled, child safety detection operates automatically with configurable enforcement actions:
Detect
Hash matching and AI analysis on all content in real-time.
Block
Immediate removal and upload prevention for confirmed violations.
Report
Automated NCMEC CyberTipline reporting with required evidence.
Document
Complete audit trails for compliance and law enforcement cooperation.
Platforms maintain control over escalation paths and can configure thresholds for automatic action versus human review based on confidence levels and content categories.
Ready to Deploy
SafetyKit's Child Safety Policy is available immediately. Protect your platform and users with industry-leading detection, compliance automation, and reduced operational burden from false positives.
