Safe AI Code Assistants in Production

AI coding assistants like GitHub Copilot, Cursor, and ChatGPT promise incredible productivity gains — but they also introduce serious security risks that most teams aren't prepared for. Research shows that 48% of AI-generated code contains vulnerabilities, yet 80% of developers mistakenly believe AI code is more secure.

This course teaches engineering leaders, security teams, and developers how to enable AI coding tools safely in production environments. You'll learn to balance productivity with security, create effective policies, implement technical guardrails, and measure real outcomes.

Who this is for: CTOs, VPEs, security engineers, and senior developers responsible for enabling AI tools while maintaining code quality and security standards.

What you'll gain: A complete framework for safely deploying AI coding assistants, from policy creation to technical controls to measuring success.

Content Structure

Module 1: The AI Coding Reality Check

• The promise vs. reality: Research shows 48% of AI-generated code contains vulnerabilities
• Why banning AI tools doesn't work (shadow AI problem)
• The productivity paradox: Gartner predicts by 2027, 25% of software defects will stem from inadequate AI code oversight
• Real productivity gains: McKinsey study shows developers can complete tasks up to 2x faster on simple tasks, but experienced developers in complex projects actually took 19% longer when using AI tools
• Understanding when AI helps vs. hurts

Module 2: Understanding the Security Risks

• Data leakage risks: How coding assistants can transmit proprietary code to third-party providers for training
• Vulnerable code generation: AI tools trained on public code repositories reproduce vulnerable patterns without understanding security intent
• Supply chain vulnerabilities: AI assistants may suggest deprecated or vulnerable dependencies
• IP contamination: Risk of AI generating copyrighted code verbatim
• The false security assumption: 80% of developers mistakenly believe AI-generated code is more secure
• Prompt injection attacks on AI assistants (ChatGPT, Copilot, Cursor, Gemini)

Module 3: Building Your AI Coding Policy

• Creating usage guidelines: When AI tools are appropriate vs. prohibited
• Approved vs. prohibited tools list
• Cloud-based vs. self-hosted solutions (AWS Bedrock, Azure OpenAI, Google Vertex AI)
• Data handling requirements and zero-retention guarantees
• Security-critical code zones where AI is restricted
• Treating AI assistants as "junior developers" - all code must be reviewed by seniors
• Setting clear accountability: Developers own all code they commit, regardless of who/what wrote it

Module 4: Developer Training & Enablement

• Teaching secure prompting: How to guide AI with security specifications
• Understanding AI limitations and when NOT to use it
• How to actively iterate with AI tools to achieve quality
• Recognizing AI-generated verbose code and increased attack vectors
• Code review essentials for AI-generated code
• Common AI mistakes developers must catch

Module 5: Technical Controls & Guardrails

Automated Security Scanning:

• SAST, DAST, API Security, and SCA as foundational baseline protection
• AI Secure Coding Assistants (ASCA) that identify insecure patterns in real-time
• IDE scanning for instant feedback on code snippets before commit
• Software Composition Analysis to flag vulnerable dependencies AI suggests

CI/CD Integration:

• Shift-left security integrated directly into pipelines
• Security checkpoints at pull request stage (like Vorpal GitHub Action)
• Mandatory static analysis gates
• Ensuring every commit undergoes immediate security scanning

Monitoring & Detection:

• Deploying agentic AI to automatically scan AI-generated code against policies and security standards
• Tracking AI tool usage and code patterns
• Runtime checks and compliance monitoring (OWASP, PCI-DSS, SOC2)

Module 6: Code Review Process for AI-Generated Code

• Implementing secure coding practices and OWASP best practices
• What reviewers must look for in AI-generated code
• Checking for missing protections: validation steps, access checks, output encoding
• Architectural threat modeling before AI-assisted development begins
• Spotting hallucinated code and logic errors

Module 7: Setting Up Your Environment Securely

• Ensuring zero data retention with model providers
• Using enterprise agreements that prohibit training on your code
• Implementing context filtering to prevent sensitive code from reaching providers
• Self-hosted options for sensitive environments (Tabby, Azure AI, AWS Bedrock)
• Network controls and data isolation
• Access control and authentication

Module 8: Measuring Success (Without Getting Fooled)

• Why self-reported productivity gains are unreliable (developers overestimate by 20-24%)
• Real metrics that matter: DORA metrics (deployment frequency, lead time, change failure rate, time to restore)
• Tracking code quality: bugs, maintainability, readability
• Security metrics: vulnerability introduction rate, time to patch
• Developer satisfaction vs. actual productivity
• When AI helps most: Junior developers and new hires benefit most

Module 9: Real-World War Stories & Case Studies

• Production incidents caused by AI-generated code
• Case study: The eval() disaster - when AI suggests dangerous shortcuts
• Supply chain compromise through AI-suggested vulnerable libraries
• Data leaks from prompting AI with sensitive context
• Success stories: Teams using AI safely at scale
• What works, what doesn't in different contexts

Module 10: Creating Your Rollout Plan

• Treating AI code generation as a process challenge, not just a technology challenge
• Pilot programs: Starting with junior developers or specific use cases
• High-ROI starting points: Stack trace analysis, code refactoring, test generation
• Change management: Getting buy-in from skeptical senior developers
• Structured training programs on AI prompting (60% productivity loss without proper training)
• Continuous feedback loops and iteration