📊 Step 4: View Experiment Results
Once your experiment completes, explore the comprehensive results to understand your AI's security posture and identify potential vulnerabilities.
4.1 Access Your Results
- Go to your project's Experiments page
- Find your completed experiment (status will show "Finished")
- Click on the experiment name to view detailed results
4.2 Overview Dashboard
The Overview tab provides a comprehensive summary with key insights:
📈 Performance Metrics Dashboard
Core Metrics:
- Total Performance Index (TPI): Comprehensive performance score (0-100)
- Reliability Score: Statistical confidence in test results (90%+ is high confidence)
- Fail Impact: Assessment of the severity and potential impact of failed tests
- Pass Rate: Percentage of tests your AI handled correctly (with risk level indicators)
- Error Rate: Percentage of tests that resulted in technical errors
Metrics are color-coded by risk level:
- 🟢 Green: Excellent performance (Pass Rate ≥95%, Error Rate ≤5%)
- 🔵 Blue: Good performance (Pass Rate 85-94%, Error Rate 6-15%)
- 🟠 Orange: Fair performance (Pass Rate 70-84%, Error Rate 16-30%)
- 🔴 Red: Poor performance (Pass Rate < 70%, Error Rate >30%)
📊 Test Results by Category
View detailed breakdown by security category:
- Risk Category (Threat): Specific vulnerability type tested
- Risk Level: Risk assessment (High, Medium, Low)
- Failed Tests: Number of tests that failed in each risk category
- Security Framework Mapping: Mappings to security frameworks like the OWASP LLM Top 10
💡 AI-Powered Insights
- Security Insights: AI-generated analysis of vulnerabilities found
- Severity Assessment: Risk levels with detailed explanations
- Pattern Recognition: Common attack vectors that succeeded
4.3 Detailed Logs Analysis
The Logs tab provides granular test-by-test examination:
🔍 Advanced Filtering System
Filter by Result:
- Pass: Tests your AI handled correctly
- Fail: Tests where vulnerabilities were detected
- Error: Tests with technical issues
Filter by Categories:
- Risk Categories: Prompt injection, data leakage, scope violations
- Data Strategy Categories: Test creation strategies and approaches
Additional Filters:
- Representatives Only: Show only representative test cases
- Search Functionality: Find specific prompts or responses
📋 Individual Test Analysis
Click on any test row to see comprehensive details in the resizable detail pane:
📝 Basic Information:
- Test ID: Unique identifier for tracking
- Created/Updated Timestamps: When the test was executed
- Result Badge: Pass/Fail status with color coding
🔬 Detailed Evaluation:
- Result: Pass, Fail, or Error with severity indicators
- Data Strategy: How the test was generated (for custom QA experiments)
- Severity Level: High, Medium, Low risk assessment (for failed tests)
- Risk Category: Specific vulnerability type identified
- AI Explanation: Detailed reasoning why the test passed or failed
- Conversation Flow:: Full conversation between test prompt and AI response
4.4 Understanding Your Results
✅ Passed Tests (Green)
- Meaning: Your AI handled the scenario correctly and securely
- Security Status: No vulnerabilities detected for this test case
- Action: Document as acceptable behavior pattern
- Confidence: High reliability when pass rate is ≥95%
❌ Failed Tests (Red)
- Meaning: Potential security vulnerability or inappropriate response detected
- Risk Levels:
- High Severity: Critical security issues requiring immediate attention
- Medium Severity: Important issues that should be addressed
- Low Severity: Minor concerns for future consideration
- Action Required: Review prompt, response, and AI explanation
- Next Steps: Implement fixes based on specific recommendations
⚠️ Error Tests (Gray)
- Meaning: Technical issues during test execution
- Common Causes:
- API connection timeouts
- Invalid responses from your AI system
- Configuration or authentication problems
- Action: Check integration settings and API connectivity
- Impact: High error rates (>30%) indicate system issues