After working with AI tools in production environments for some time, I’ve learned that the key to successful AI integration isn’t just about getting faster code generation – it’s about doing it safely. This article shares my practical approach to using AI throughout the development lifecycle while keeping security and code quality at the forefront.
Why I Never Trust AI 100%
Let me be honest: if you’re copy-pasting AI-generated code without review, you’re setting yourself up for trouble. I’ve seen too many cases where developers accept AI suggestions that look good on the surface but contain serious flaws.
Few examples which I’ve observed in real projects:
- AI suggesting authentication flows that bypass proper validation
- Generated code with hardcoded connection strings (yes, really)
- Performance bottlenecks from inefficient LINQ queries
- Memory leaks
- Missing input validation that opens XSS vulnerabilities
The worst part? These issues often make it to production because the code “looks right” and passes basic testing. Commonly known as “vibe coding” – accepting code based on gut feeling rather than proper analysis.
My Daily AI Workflow
I primarily use Perplexity with Claude models for most of my AI interactions. But the rules are the same everywhere.
Here’s how I typically structure my day:
Morning Architecture Sessions: When tackling complex features, I start by discussing the approach with AI. But I always frame it carefully – ever sharing proprietary business logic or domain-specific algorithms we’ve developed over years.
Implementation Support: During coding, I use AI for boilerplate generation and exploring different implementation patterns. However, I always ask follow-up questions like “Does it follow .NET 8 best practices?”, “Have you verified it against any potential exceptions?” or “Can this be made more performant?”
Code Review Assistant: I often paste sanitized code snippets and ask AI to review potential issues, security concerns, or cleaner implementations.
Protecting What Matters: Beyond Credentials
Everyone knows not to share passwords or API keys with AI tools. But there’s another layer most developers miss – protecting your company’s intellectual property and domain knowledge.
In my experience, this includes:
- Custom caching strategies and performance optimizations we’ve developed
- Proprietary algorithms for data processing
- Business-specific workflow implementations
- Integration patterns with third-party systems
- Domain-specific validation rules
Example of what NOT to share:
DON’T: “How do I optimize our customer loyalty point calculation system that processes 50k transactions per minute with our specific reward tiers?”
DO: “How do I optimize a high-throughput calculation system that processes numerical data with multiple conditional logic branches?”
The key is abstracting away the business context while keeping the technical challenge intact.
Security Risks I've Encountered
Design Phase Issues
AI can suggest architectures that seem elegant but have fundamental security problems. I once had AI recommend a microservices setup that would have exposed internal service communications without proper authentication. Always ask for security considerations in your prompts.
Implementation "Gotchas"
The biggest risks I see:
Input Validation Gaps: AI-generated controllers often miss edge cases in validation logic. I always add comprehensive validation using FluentValidation after getting the initial code structure.
Dependency Vulnerabilities: AI might suggest popular NuGet packages without considering their security posture. I run security scans on any new dependencies before adding them to production code.
Error Handling: AI tends to create generic error responses that can leak sensitive information. I always review error handling to ensure we’re not exposing internal system details.
Testing Blind Spots
AI-generated tests often miss security scenarios. I’ve learned to explicitly request tests for:
- Authentication bypass attempts
- Authorization boundary conditions
- Input validation edge cases
- Performance under load – I use k6 extensively for API testing, focusing on concurrent user simulations and response time metrics to catch security vulnerabilities that only appear under stress
My Prompting Strategy
After experimenting with various approaches, here’s what works best for me – role-based, context-rich and specific prompts:
Role-Based Prompting
You are a Senior .NET 8 Developer with expertise in clean architecture and security best practices. I need help with…
Context-Rich Requests
Instead of “Create a user service,” I provide:
Create a user management service for a .NET 8 Web API that:
– Uses Entity Framework Core with proper async patterns
– Implements comprehensive input validation
– Follows repository pattern with dependency injection
– Includes proper error handling and logging
– Follows SOLID principles and clean code practices
Iterative Refinement
I rarely accept the first response. My typical follow-up questions:
- “Can this be optimized for better performance?”
- “Are there any security concerns with this approach?”
- “How would you handle error scenarios in this code?”
- “Does this follow current .NET best practices?”
Tool Evaluation: What I Actually Use
Perplexity/Claude for Architecture
These excel at high-level discussions and design decisions. I use them when:
- Exploring different architectural approaches
- Getting explanations of complex patterns
- Reviewing design trade-offs
- Understanding new technologies or frameworks
IDE-Integrated Tools (VSCode AI, Rider AI - AI Assistant)
I’ve tested various IDE integrations extensively. They’re useful for:
- Boilerplate generation
- Quick code completions
- Refactoring suggestions
But I’m cautious about their whole-project generation features. I’ve seen them create projects with inconsistent patterns and missing security considerations – but this can be improved by specifying the expected architecture, creating copilot instructions.
Real-World Examples
API Development
When building APIs, the example AI prompt could look like:
Create a .NET 8 Web API controller that handles user registration with:
– Proper input validation using FluentValidation
– Password hashing with bcrypt
– Rate limiting to prevent brute force attacks
– Comprehensive error handling without information leakage
– Integration with Entity Framework Core using async patterns
Performance Optimization
For performance work, I focus on specific bottlenecks:
Optimize this LINQ query for better performance while maintaining readability and avoiding N+1 problems. The query processes large datasets and needs to be efficient for production use.
Performance optimization with Security Focus
When working on performance improvements, I always maintain security considerations. For example, when optimizing caching implementations:
Optimize this caching strategy for high-throughput scenarios while:
– Implementing proper cache invalidation to prevent stale data security issues
– Using techniques like micro-TTL and Compressed memory cache for memory efficiency
– Preventing cache poisoning attacks
– Maintaining thread safety in concurrent access scenarios
– Including proper monitoring for cache hit rates and security events
My approach: I use k6 to simulate realistic load patterns while monitoring both performance metrics and security boundaries. This helps identify scenarios where performance optimizations might inadvertently create security vulnerabilities.
My Security Checklist: What Actually Works
After some time of using AI in production environments, I’ve developed a practical checklist that helps me stay secure while maximizing productivity. This isn’t theoretical – it’s what I actually do on every project.
Before I Start Working with AI
Clean Up My Code Samples
- Strip out any connection strings, API keys, or credentials (obvious, but worth stating)
- Remove proprietary business logic – especially those custom algorithms we’ve spent years perfecting
- Replace domain-specific variable names with generic ones
- Abstract away company-specific workflows and processes
Set Clear Boundaries
- Define exactly what security standards I need to follow (OWASP, company policies, etc.)
- Identify which frameworks and patterns are mandatory for the project
- Establish performance requirements upfront (like those rate limiting strategies I use for API security)
- Clarify any compliance requirements
During My AI Sessions
Ask the Right Questions
- Always specify the AI’s role: “You are a Senior .NET Developer with security expertise”
- Request explanations for security decisions, not just code
- Ask for multiple approaches with trade-off analysis
- Push back when something doesn’t feel right – ask “Is there a more secure way to do this?”
Validate Everything
- Cross-reference suggestions with official documentations
- Check against established security frameworks
- Compare with patterns I know work in production
- When in doubt, ask for clarification on security implications
After Getting AI-Generated Code
Manual Review Process
- Focus on common vulnerability patterns (SQL injection, XSS, authentication bypass)
- Check for proper input validation – this is where AI often falls short
- Review error handling to ensure no sensitive information leaks
- Verify that the code follows our clean coding standards (reducing unnecessary if statements, keeping logic simple)
Automated Security Checks
- Run SonarQube or similar tools on generated code
- Use dependency scanning tools for any new NuGet packages
- Include security-focused unit tests in my test suites
- Test API endpoints with tools like Postman or custom scripts for authentication flows
Integration Testing
- Verify that AI-generated components work securely with existing systems
- Test authentication and authorization boundaries thoroughly
- Use k6 for performance testing with security focus – simulating concurrent users to identify race conditions or authentication bypass scenarios under load
- Check that rate limiting and timeout configurations are properly implemented
- Test caching implementations for both performance and security – ensuring cache invalidation works correctly and doesn’t expose sensitive data
- Ensure logging and monitoring capture security events appropriately
Documentation
- Document any security decisions and why I made them
- Note any deviations from AI suggestions and the reasoning
- Keep track of security patterns that work well for future reference
- Record any security issues found and how they were resolved
Red Flags That Trigger Extra Review
Through experience, I’ve learned to be extra cautious when AI suggests:
- Authentication flows that seem overly simple
- Error handling that returns detailed system information
- File upload functionality without proper validation
- API endpoints without rate limiting or input validation
What I've Learned
This isn’t just paperwork for the sake of it – trust me – following these steps has genuinely kept me out of trouble in production. The key is making it part of your natural workflow rather than an afterthought. When security becomes habitual, you catch problems before they become incidents.
The time invested in this process pays dividends in reduced security vulnerabilities, better code quality, and fewer late-night emergency fixes. Plus, it builds confidence that the AI-assisted code you’re deploying meets the same standards as code you’d write entirely by hand.
Building Team Standards
Based on my experience, here are the practices I can recommend for you:
Code Review Checklist for AI-Generated Code
- All inputs properly validated
- No hardcoded secrets or connection strings
- Error handling doesn’t expose sensitive information
- Performance considerations addressed
- Follows established team coding standards
- Security implications reviewed
AI Usage Guidelines
- Never share proprietary business logic or algorithms
- Always review generated code for security vulnerabilities
- Test AI-generated code thoroughly, especially edge cases
- Validate against official documentation and best practices
- Use AI as a starting point, not the final solution
Lessons Learned
After using AI tools extensively in production environments, here are my key takeaways:
AI is excellent for exploration and initial implementation, but human expertise is crucial for security, performance, and maintainability decisions.
The quality of AI output directly correlates with prompt quality. Generic prompts yield generic solutions. Specific, context-rich prompts with clear requirements produce much better results.
Security cannot be an afterthought. Including security requirements in initial prompts is far more effective than trying to retrofit security into AI-generated code.
Domain knowledge protection is as important as credential security. Be mindful of what intellectual property you’re sharing when seeking AI assistance.
Moving Forward
AI tools will continue evolving, but the fundamental principles remain the same: maintain critical thinking, prioritize security, and never stop learning. The developers who succeed with AI are those who use it to augment their expertise, not replace their judgment.
The way I see it? AI should make you a better developer, not replace your judgment. Use it to move faster, think broader, but always maintain those security standards and quality practices that separate professional code from hobby projects. This approach works across disciplines – whether you’re a designer using AI for mockups, a writer using it for research, or an analyst using it for data insights. The principle remains – enhance your skills, don’t surrender them.
Remember: AI is a powerful assistant, but you’re still the architect, security expert, and quality gatekeeper. Use it wisely.