Code Security Reinvented: Navigating the era of AI by Joseph Katsioloudes

AI can help bridge the significant gap between security specialists and developers (currently 1:100 ratio) by democratizing security knowledge

60% of code committed to GitHub in 2023 was AI-generated, showing rapid adoption of AI coding tools by developers

Four key ways to leverage AI for security:

• Writing safer code through AI pair programming
• Finding security issues with AI-powered code scanning
• Generating security testing content (fuzzing strings, exploits)
• Getting targeted security guidance for specific vulnerabilities

GitHub Copilot’s security filter helps prevent vulnerable code suggestions, though it shouldn’t be relied on as the only security measure

Static Application Security Testing (SAST) detects approximately 50% of security vulnerabilities early in development

SQL injection and stolen credentials remain major security threats, responsible for 49% and 17% of breaches respectively

AI can improve security workflows through:

• Automated vulnerability detection
• Pattern recognition in malware analysis
• Threat intelligence processing
• Customized security training

Organizations like Mercado Libre have seen 50% time savings in development by using AI coding tools

CodeQL combined with AI enables powerful code analysis across 10 supported languages through natural language queries

Security best practices still essential - AI assists but doesn’t replace proper security testing and review processes