Enterprise applications utilizing large language models (LLMs) are evolving, presenting fresh security challenges that outstrip traditional application security methods. While AppSec focuses on securing source code and third-party dependencies, LLMs introduce unique data privacy risks due to their complex runtime environments. Traditional tools like SAST and DAST are vital, but organizations also require enhanced strategies to safeguard proprietary information and ensure responsible AI outputs. As organizations navigate the security landscape, modern frameworks adapting to AI/ML risks are increasingly necessary to protect sensitive data effectively.
AI/ML applications add layers of complexity to security, requiring both traditional app security measures and innovative strategies to mitigate emerging risks.
Traditional approaches insufficiently address new risks posed by LLMs, prompting the need for modernized security frameworks to protect applications in cloud-native environments.
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