Traceable AI VS Repello AI

Traceable AI Pricing

Repello AI Pricing

Traceable AI

• API Discovery: Complete visibility and cataloging of entire API ecosystem
• Threat Detection: AI-powered identification of behavioral anomalies and threats
• Attack Protection: Prevention of sensitive data disclosure and malicious activities
• Fraud & Bot Security: Real-time fraud detection and prevention
• API Security Testing: Context-aware vulnerability discovery and testing
• Security Data Lake: Purpose-built storage for API security data correlation
• Contextual Analysis: AI/ML-powered analysis for threat detection and insights
• Gen AI Security: Specialized protection for generative AI implementations

Repello AI

• Continuous AI Security: Provides ongoing monitoring and threat mitigation for GenAI applications in real-time.
• Automated AI Red-Teaming: Tests AI applications from an attacker's perspective to identify vulnerabilities.
• CI/CD Integration: Integrates security features directly into development pipelines for early detection.
• Context-Aware Tailoring: Adapts security measures to the specific needs and use case of the GenAI application.
• Comprehensive Reporting: Delivers detailed reports on vulnerabilities, failure modes, and mitigation steps.
• Blackbox Testing: Simulates real attacks without requiring access to underlying algorithms or code.
• Broad Vulnerability Coverage: Safeguards against over 270 vulnerability types.
• Model Agnostic & Multimodal Support: Compatible with various AI models and supports text, image, audio, and video modalities.
• AI Security Framework Mapping: Benchmarks application security against global AI security and safety standards.

Traceable AI Use Cases

• API Discovery and Cataloging
• Security Testing
• Sensitive Data Protection
• Account Takeover Prevention
• Bot Mitigation
• Incident Response
• Data Privacy Compliance
• Fraud Ring Detection

Repello AI Use Cases

• Securing Generative AI applications against novel risks.
• Protecting against prompt injection attacks.
• Preventing jailbreak attempts on AI models.
• Mitigating risks of brand reputation damage via AI misuse.
• Blocking inappropriate or harmful content generation.
• Continuously assessing and managing AI security posture.
• Integrating AI security testing into the development lifecycle (CI/CD).

Uptime Monitor

Uptime Monitor