AWS recently published an excellent blog post on "Secure a generative AI assistant with OWASP Top 10 mitigation" that provides comprehensive guidance on implementing infrastructure-level security controls for AI applications. Their approach demonstrates the shared responsibility model and shows how AWS services can mitigate many OWASP LLM Top 10 risks through proper configuration and architectural patterns.
Building on this excellent foundation, there's valuable opportunity to enhance AI security posture by adding application security testing alongside infrastructure security. While AWS provides robust foundational protections, proactive security testing helps validate that your mitigations perform effectively against evolving real-world AI attacks.
Complementary Security Layers
What AWS Infrastructure Security Provides
The AWS blog demonstrates excellent infrastructure-level protections:
- Perimeter Defense: AWS WAF, Shield Advanced, and API Gateway for filtering malicious traffic
- Identity & Access: Amazon Cognito with MFA and adaptive authentication
- Data Protection: Encryption at rest and in transit, KMS key management
- Output Sanitization: Amazon Bedrock Guardrails for content filtering
- Resource Controls: Rate limiting and consumption bounds
Where Additional Testing Adds Value
While infrastructure controls provide excellent foundational protection, sophisticated AI attacks can present additional challenges that benefit from complementary testing:
- 🎯 Dynamic Prompt Injection: AI attackers adapt their techniques faster than static guardrails can be updated
- 🔍 Context-Aware Attacks: Multi-turn conversations that gradually manipulate AI behavior over time
- 🧬 Application-Specific Vulnerabilities: Unique attack vectors based on your specific business logic and data
- 🤖 AI-Powered Social Engineering: Sophisticated manipulation techniques that bypass content filters
- 🛡️ Zero-Day AI Exploits: Novel attack patterns not yet covered by infrastructure protections
Proactive Testing: The Complementary Layer
This is where the VeriGen Red Team Platform adds significant value. While AWS secures your infrastructure with robust foundational controls, VeriGen helps validate whether your comprehensive defenses can withstand sophisticated, adaptive AI attack techniques.
Complete OWASP 2025 Validation with 42 AI Agents
Let's examine how VeriGen's proactive testing complements each AWS mitigation strategy:
LLM01: Prompt Injection
AWS Approach: Input validation, Bedrock Guardrails
VeriGen Testing: 14 specialized agents generate dynamic, context-aware injection attacks that adapt to your guardrails and find bypasses
LLM02: Sensitive Information Disclosure
AWS Approach: Data sanitization, user-based access controls
VeriGen Testing: 4 agents use sophisticated social engineering to extract sensitive data through indirect questioning and context manipulation
LLM05: Improper Output Handling
AWS Approach: Output encoding, content validation
VeriGen Testing: 5 agents generate creative output formats that test whether your encoding properly neutralizes all executable content
LLM06: Excessive Agency
AWS Approach: Least privilege IAM, action limits
VeriGen Testing: 4 agents attempt privilege escalation through AI agent manipulation and plugin exploitation
LLM08: Vector & Embedding Weaknesses
AWS Approach: Encryption, access controls, TLS
VeriGen Testing: Specialized agents test vector database poisoning, embedding manipulation, and RAG hijacking attacks
LLM10: Unbounded Consumption
AWS Approach: Request limits, resource quotas
VeriGen Testing: AI agents craft sophisticated resource exhaustion attacks that work within your limits but still cause degradation
The Adaptive Learning Advantage
The Value of Dynamic Testing
The AWS blog demonstrates comprehensive infrastructure protections, and dynamic testing adds another valuable layer by addressing AI threats that evolve continuously:
Traditional Approach (AWS Infrastructure):
- Configure guardrails based on known attack patterns
- Implement content filters for current threat landscape
- Set resource limits based on expected usage patterns
- Deploy access controls based on defined user roles
VeriGen's Adaptive Approach: - AI agents learn your specific vulnerabilities and refine attacks accordingly - Context-aware testing that understands your business logic and data patterns - Multi-turn attack sequences that build over multiple interactions - Continuous evolution as our agents discover new attack vectors unique to your application
Real-World Testing Scenarios
Consider these scenarios where additional testing complements infrastructure security:
Scenario 1: Financial Services AI Assistant
🛡️ AWS Infrastructure Protection
Bedrock Guardrails block obvious attempts to access account information
🎯 VeriGen Testing Validation
Agent gradually builds context over multiple conversations, eventually extracting account details through seemingly innocent financial advice requests
Scenario 2: Healthcare AI Chatbot
🛡️ AWS Infrastructure Protection
Data sanitization prevents direct PII exposure
🎯 VeriGen Testing Validation
Agent uses medical terminology and symptom discussions to infer patient identities and conditions
Scenario 3: HR AI Assistant
🛡️ AWS Infrastructure Protection
Role-based access controls limit data exposure
🎯 VeriGen Testing Validation
Agent exploits business logic to access employee information by posing as a manager with legitimate-seeming requests
Complementary Security Architecture
The most effective AI security strategy combines both approaches:
Foundation Layer: AWS Infrastructure Security
- Perimeter defense and identity management
- Data encryption and access controls
- Resource limits and monitoring
- Content filtering and output sanitization
Validation Layer: VeriGen Proactive Testing
- Dynamic vulnerability discovery
- Application-specific attack simulation
- Adaptive learning and continuous improvement
- Complete OWASP 2025 coverage validation
Continuous Improvement Cycle
- Deploy AWS security controls following best practices
- Test with VeriGen's 42 AI agents to find gaps
- Refine security configurations based on findings
- Repeat as your application evolves and new threats emerge
Beyond AWS: Universal AI Security Testing
While the AWS blog focuses on Amazon Bedrock deployments, VeriGen's platform provides universal compatibility:
Multi-Cloud & Hybrid Support
- Major Providers: OpenAI, Anthropic, Azure OpenAI, Google Gemini
- On-Premises: Ollama and custom deployments
- API Flexibility: REST endpoints with GraphQL and gRPC coming soon
Platform-Agnostic Security
Whether you're using AWS Bedrock, Azure OpenAI, or custom LLM deployments, the fundamental AI security challenges remain the same. VeriGen's testing approach works across all platforms to validate your security posture.
Measuring Security Effectiveness
⚠️ Infrastructure Security Alone
- Rely on infrastructure controls and assume complete protection
- React to security incidents after they occur
- Limited visibility into application-specific vulnerabilities
- Configuration compliance without validation testing
✅ Infrastructure + VeriGen Testing
- 95% Peak Detection Accuracy with adaptive learning
- High-precision threat identification with intelligent analysis
- Continuous validation of your security controls
- Proactive vulnerability discovery before attackers find them
Implementation Strategy
Phase 1: Baseline Assessment (Week 1)
- Deploy AWS security controls following the blog recommendations
- Run VeriGen's comprehensive assessment to validate effectiveness
- Identify gaps between infrastructure protection and real-world threats
Phase 2: Iterative Improvement (Weeks 2-4)
- Refine AWS configurations based on VeriGen findings
- Implement additional mitigations for discovered vulnerabilities
- Re-test with VeriGen to measure improvement
Phase 3: Continuous Validation (Ongoing)
- Integrate VeriGen testing into CI/CD pipeline
- Monitor for new vulnerabilities as application evolves
- Adapt security controls based on emerging threats
The Future of AI Security
The AWS blog represents excellent progress in infrastructure-level AI security. As the threat landscape evolves, organizations benefit from combining both approaches:
🛡️ Robust Infrastructure Foundation
AWS provides excellent foundational security with comprehensive infrastructure controls, guardrails, and architectural best practices
🎯 Proactive Testing Validation
VeriGen validates that your infrastructure protections work effectively against real-world AI attacks and application-specific threats
Emerging Threats Requiring Both Approaches
- AI-Powered Attack Tools: Attackers using AI to find vulnerabilities faster
- Multi-Modal Threats: Security challenges across text, image, and audio inputs
- Federated Learning Attacks: Threats to distributed AI systems
- Real-Time Manipulation: Live conversation hijacking and context switching
Conclusion: Defense in Depth for AI
The AWS blog demonstrates that infrastructure security provides a critical foundation for AI applications. Building on this strong foundation, VeriGen's Red Team Platform adds a valuable validation layer that helps ensure your AWS security controls perform effectively against sophisticated, real-world AI attacks. Our 42 specialized AI agents don't just test for known vulnerabilities—they discover the unique attack vectors specific to your application that no static security control can anticipate.
The Complete AI Security Strategy:
1. Implement AWS infrastructure security best practices
2. Validate with VeriGen's proactive AI security testing
3. Refine based on continuous testing feedback
4. Evolve as threats and your application change
Ready to validate whether your AWS security controls actually protect against real AI attacks? Our platform complements AWS infrastructure security with proactive testing that finds vulnerabilities before attackers do.
Experience complete AI security validation: Start your free assessment and see how VeriGen's 42 AI agents can validate and strengthen your AWS security posture.