AI Security
The discipline of protecting AI models, applications, and data from attacks, misuse, unauthorized access, manipulation, and other security threats throughout their lifecycle.
What is AI Security?
AI security focuses on identifying and mitigating vulnerabilities specific to artificial intelligence systems. These can include prompt injection, jailbreaks, sensitive data leakage, model manipulation, adversarial attacks, and unauthorized tool access. It also covers traditional cybersecurity risks affecting the infrastructure, APIs, data, and services supporting AI applications. Effective AI security combines technical controls, monitoring, testing, access management, and governance.
Why is AI Security Important?
As organizations integrate AI into business-critical workflows, security failures can expose sensitive data, enable unauthorized actions, or compromise system integrity. AI security helps organizations reduce these risks while supporting safer deployment, regulatory compliance, and greater trust in AI systems.
Common use cases
AI security is used to protect LLM applications, AI agents, enterprise chatbots, AI coding tools, RAG systems, and other production AI deployments.