Key Highlights

• Why prompt injection and data leakage have become critical security risks for AI applications
• Where traditional security controls like WAFs, DLP, regex filters, and LLM moderation APIs fall short
• How to detect and block prompt injection, jailbreaks, and sensitive data exposure in real time
• Best practices for protecting both AI prompts and model responses with bidirectional security
• How runtime policy enforcement helps prevent PII, PHI, PCI, and confidential data leaks
• What an AI-native security architecture looks like, from threat detection to enforcement
• How to deploy AI application security with minimal code changes and low latency
• Lessons from a real-world enterprise deployment protecting production AI applications from prompt injection and data exposure