What Is AI Security? A Practical Definition for Security Teams

AI security encompasses three related areas: protecting AI models, training data, and deployment pipelines from threats such as data poisoning, prompt injection, adversarial inputs, model theft, and unapproved “shadow AI” use; applying AI to cybersecurity activities such as alert triage and detection; and securing code and dependencies generated by AI assistants. The third area is emphasized as an increasingly significant concern because AI-generated code may contain logic flaws, vulnerabilities, outdated packages, or nonexistent “hallucinated” dependencies that attackers could register maliciously. The text cites industry reports and benchmarks suggesting rapid growth in AI-related cyber risk, source-code exposure to generative AI tools, and insecure outcomes in AI-generated software. It recommends organizations inventory approved and unofficial AI tools, establish data-use and approval policies, align controls with frameworks such as the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework, scan AI-written code with static application security testing and software composition analysis, review dependencies, and prioritize vulnerabilities through reachability analysis. It also presents Endor Labs as a platform focused on securing AI-generated code through code, dependency, secret, and container scanning with context-based prioritization.

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