🛡️ AI Assurance

ai+me provides end-to-end AI assurance that goes beyond simple runtime filtering. Our platform combines automated adversarial testing, behavioral QA, real-time firewalls, and post-production analysis to deliver comprehensive security coverage tailored to your AI's actual business context.

🎯 What is AI Assurance?

AI assurance is the systematic process of ensuring that AI systems are safe, reliable, secure, and compliant throughout their entire lifecycle. Unlike traditional security approaches that focus only on runtime protection, AI assurance encompasses:

  • Pre-production testing to identify vulnerabilities before deployment
  • Real-time monitoring to protect against threats during operation
  • Post-production analysis to detect emergent risks and improve security
  • Continuous improvement through feedback loops and iterative testing

🏗️ The AI Assurance Lifecycle

ai+me's AI assurance approach covers the entire AI lifecycle:

Pre-Production Assurance

Contextual Adversarial Testing

  • Business Context Integration: Tests grounded in your actual use cases
  • Custom Attack Generation: Creates attacks specific to your AI's scope
  • OWASP LLM Top 10 Coverage: Tests against industry-standard vulnerabilities
  • Automated Execution: Runs thousands of tests automatically

Behavioral QA Testing

  • User Interaction Patterns: Test common user workflows
  • Edge Case Handling: Validate responses to unusual inputs
  • Error Recovery: Ensure graceful handling of errors
  • Performance Validation: Verify response times and reliability

Production Protection

AI Firewall

  • Request Analysis: Evaluate every user request in real-time
  • Content Filtering: Block unsafe or inappropriate content
  • Policy Enforcement: Enforce business-specific security policies
  • Threat Detection: Identify and block malicious requests

Real-time Monitoring

  • Request Logging: Complete audit trail of all interactions
  • Performance Metrics: Track response times and reliability
  • Security Analytics: Identify attack patterns and trends
  • Alert System: Real-time notifications for security incidents

Post-Production Analysis

LLM-as-a-Judge

  • Safety Assessment: Identify potentially harmful responses
  • Accuracy Validation: Verify response correctness
  • Compliance Checking: Ensure adherence to policies
  • Quality Scoring: Provide quantitative quality metrics

Log Analysis and Risk Assessment

  • Pattern Recognition: Identify unusual behavior patterns
  • Risk Scoring: Quantify potential security risks
  • Trend Analysis: Track security metrics over time
  • Incident Investigation: Deep dive into security incidents

🔄 Continuous Improvement

Feedback Integration

  • Human Review: Expert analysis of AI responses
  • User Feedback: Real user experiences and concerns
  • Security Incidents: Learnings from actual security events
  • Performance Data: Insights from monitoring and analytics

Policy Management

  • Business Rules: Define acceptable AI behaviors
  • Security Boundaries: Establish limits and restrictions
  • Compliance Requirements: Ensure regulatory adherence
  • Risk Tolerance: Define acceptable risk levels
🎯 Next Steps⚔️ Adversarial Testing