# Tips and Tricks

Advanced techniques and best practices for getting the most out of Teev.

Start with High-Risk Scenarios

• Prioritize test scenarios based on your AI application's use case
• Focus on scenarios that could cause the most damage if exploited
• Consider your user base and potential attack vectors

Phased Testing Approach

1. Phase 1: Security Vulnerabilities + Jailbreaks (basic security)
2. Phase 2: Add Content Risks + Data Leakage (content safety)
3. Phase 3: Include specialized scenarios (coding, linguistic, etc.)
4. Phase 4: Comprehensive testing across all scenarios

Application-Specific Testing

• Customer Service Bots: Focus on jailbreaks and content risks
• Code Generation Tools: Emphasize coding-specific risks and security vulnerabilities
• Content Generators: Prioritize content risks and topic control
• Data Analysis Tools: Concentrate on data leakage and security vulnerabilities

User Persona Testing

• Test with different user types (customers, employees, partners)
• Consider varying levels of technical sophistication
• Include edge cases and unusual interaction patterns

Parallel Testing

• Run multiple test scenarios simultaneously when possible
• Use batch testing for multiple applications
• Schedule tests during off-peak hours for faster execution

Smart Scheduling

• Use automated scheduling for regular testing
• Stagger tests across different applications
• Configure notifications to avoid alert fatigue

Test Scope Optimization

• Start with quick tests for rapid feedback
• Use comprehensive tests for periodic deep assessment
• Customize test intensity based on application criticality

API Rate Limit Management

• Monitor API usage and quotas
• Implement backoff strategies for rate-limited APIs
• Consider dedicated test environments to avoid production impact

Intensity Levels

• Conservative: Safer prompts, lower false positive rate
• Standard: Balanced approach for most applications
• Aggressive: More sophisticated attacks, higher coverage

Timeout Configuration

• Adjust timeouts based on your AI application's response time
• Consider network latency and processing complexity
• Balance thoroughness with testing efficiency

CI/CD Pipeline Integration

# Example: Include Teev testing in your deployment pipeline
stages:
  - build
  - test
  - security_test  # Teev testing stage
  - deploy

security_test:
  script:
    - teev-cli run --application=$APP_ID --scenario=critical
    - teev-cli wait-for-completion
    - teev-cli get-results --format=junit

Webhook Configuration

• Set up webhooks for real-time notifications
• Filter notifications by severity level
• Integrate with your existing alerting systems

Understanding False Positives

• Review flagged items in context of your application
• Consider your specific use case and acceptable risk levels
• Document legitimate exceptions for future reference

Trend Analysis

• Track security scores over time
• Monitor improvement after implementing fixes
• Identify recurring vulnerability patterns

Executive Reporting

• Focus on high-level security trends and improvements
• Include risk reduction metrics and business impact
• Provide clear recommendations and next steps

Technical Reporting

• Share detailed findings with development teams
• Include specific examples and remediation guidance
• Track vulnerability resolution and verification

Access Control Strategy

• Admins: Full configuration and management access
• Security Team: All testing and reporting capabilities
• Developers: Application-specific testing and results
• Executives: High-level reporting and trends

Workflow Integration

• Create shared dashboards for team visibility
• Establish processes for vulnerability triage
• Define escalation procedures for critical findings

Regular Reviews

• Schedule monthly security reviews with stakeholders
• Share improvements and achievements
• Discuss emerging threats and testing updates

Documentation

• Maintain records of security decisions and exceptions
• Document remediation actions and verification
• Keep testing configurations and rationale updated

Slow Test Execution

• Check network connectivity and latency
• Verify API performance and availability
• Consider test scope and intensity settings

Unexpected Results

• Ensure AI application consistency between tests
• Review recent changes to your system
• Check for environmental differences

Test Environment Management

• Use dedicated test environments when possible
• Ensure test data consistency
• Monitor resource usage during testing

Result Quality

• Regularly review and validate findings
• Update test configurations based on learnings
• Provide feedback on false positives to improve accuracy

Programmatic Testing

# Example: Automated testing with Teev API
import teev

client = teev.Client(api_key="your-api-key")

# Run test
test_run = client.applications.test(
    application_id="app-123",
    scenarios=["security", "jailbreaks"],
    intensity="standard"
)

# Monitor progress
while test_run.status != "completed":
    time.sleep(30)
    test_run.refresh()

# Get results
results = test_run.get_results()
print(f"Security Score: {results.security_score}")

Bulk Operations

• Test multiple applications simultaneously
• Batch configuration updates
• Mass export of historical data

Monitoring Integration

• Connect with Datadog, New Relic, or similar platforms
• Create custom alerts based on security score changes
• Track testing metrics alongside application performance

Compliance Reporting

• Generate SOC 2 compliance reports
• Create audit trails for security testing
• Export data for regulatory requirements

Regular Updates

• Subscribe to Teev threat intelligence updates
• Review new test scenarios as they're released
• Adjust testing strategy based on emerging threats

Community Engagement

• Participate in Teev user community
• Share learnings and best practices
• Stay informed about AI security trends

Testing Evolution

• Regularly review and update test configurations
• Expand testing scope as your AI application evolves
• Incorporate lessons learned from production incidents

Team Training

• Keep team members updated on new features
• Provide regular training on AI security best practices
• Share industry insights and case studies

Remember: AI security is an ongoing process, not a one-time assessment. Regular testing, continuous monitoring, and proactive improvement are key to maintaining strong security posture.