Developed an AI threat detection system designed to identify and mitigate risks in Large Language Model (LLM) environments. The platform analyzes user inputs in real time to detect prompt injection, jailbreak attempts, and malicious patterns using rule-based and heuristic techniques. It integrates automated testing workflows to simulate adversarial scenarios and continuously evaluate model behavior. The system includes logging and monitoring capabilities to track suspicious activity and measure performance metrics such as detection accuracy and latency. This solution demonstrates practical implementation of AI security principles, enabling safer deployment of AI systems by proactively identifying vulnerabilities and enforcing guardrails against misuse.