Generative AI Security

This paper takes the AI security threat matrix as the core framework, and based on the mature ATT&CK methodology, it systematically elaborates on the full lifecycle security threats faced by AI systems, including key attack techniques such as data poisoning, model extraction, privacy leakage, confrontation samples, and cue word injection, etc., and puts forward the corresponding defense strategies and enterprise landing solutions, providing AI engineers, security engineers, and CSOs with professional technical Reference.

Future-oriented AI security architecture is not only a technical issue, but also a strategic shift. From "tool-driven" to "intelligence-driven", from "after-the-fact response" to "before-the-fact governance", from "artificial dependence" to "human-machine collaboration" - these shifts will profoundly change the face of the security industry. From "artificial dependence" to "human-machine collaboration" - these changes will profoundly change the appearance of the security industry.

Those enterprises that take the lead in building AI-native security systems will gain a competitive advantage in multiple dimensions such as threat detection, operational efficiency, cost control, and talent retention. And those enterprises that are stuck in traditional tool stacking and rule writing will eventually be eliminated by the times.

The development of AI is irreversible. Security decision makers should take immediate action to seize this historic opportunity by launching the construction of AI security platforms in four dimensions: strategy, organization, technology and investment.