AI security is an emerging cybersecurity domain focused on protecting how organizations build, use, and run artificial intelligence. As businesses rapidly adopt GenAI tools and agents across complex infrastructure, traditional security programs fall short. Organizations face unique risks like shadow AI, data exposure, prompt injection, and pipeline attacks. This section covers the core concepts, risks, and controls needed to mitigate these threats, govern AI use, and enable secure innovation.
AI security is the discipline of protecting artificial intelligence systems from threats that compromise their integrity, confidentiality, or reliability.
AI model security is the protection of machine learning models from unauthorized access, manipulation, or misuse.
LLM security is the practice of protecting large language models and dependent systems from unauthorized access, misuse, and other exploitation.
A prompt injection attack is a GenAI security threat where an attacker deliberately crafts and inputs deceptive text into a large language model (LLM) to manipulate its outputs.
AI prompt security (AKA secure prompt engineering), is the practice of protecting AI systems from unintended behavior or exploitation through prompts.
AI red teaming is a structured, adversarial testing process designed to uncover vulnerabilities in AI systems before attackers do.
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