Pallma is an AI security platform focused on protecting agentic AI systems at the outcome level rather than the prompt level. The core premise is that agentic systems fail not at individual prompts, but when conversations accumulate access, context, and authority over time. The page is a comparison page (Pallma vs Google Cloud Model Armor), positioning Pallma as a solution that goes beyond prompt-level filtering to address the broader risks of agentic AI workflows. Key focus areas: - Runtime protection for agentic AI systems - Defense against prompt injection attacks - Prevention of data exfiltration via AI agents - Monitoring of how context and authority accumulate across multi-turn conversations The product also offers an interactive AI Hack Challenge platform (challenges.pallma.ai) where users can practice real-world exploits against live AI applications, including prompt injections and data exfiltration scenarios. This serves as both a demonstration of the threat landscape and a training/validation tool. Pallma is positioned as a commercial AI security product targeting organizations deploying agentic AI systems, differentiating itself from prompt-filtering tools like Google Cloud Model Armor by claiming to protect at the system behavior and outcome level rather than individual input/output filtering.