What Is Generative AI Security? Risks and Enterprise Controls

Generative AI security encompasses protections for AI models, training and runtime data, prompts, outputs, AI-built applications, and governance processes, addressing risks that traditional application security tools may not detect, such as prompt injection, data poisoning, model theft, data leakage, insecure generated code, hallucinations, and unsanctioned “shadow AI.” The material argues that visibility into all AI tools and applications is foundational, since organizations cannot govern or audit systems they do not know exist, and cites survey findings suggesting widespread sensitive-data exposure and employee use of unapproved AI tools. It describes a layered approach involving prompt inspection, access controls tied to individual users, secret redaction, hardened infrastructure, human review for sensitive deployments, audit logging, monitoring, and adversarial testing. Recommended frameworks include the OWASP Top 10 for LLM Applications for threat prioritization, NIST’s AI Risk Management Framework for governance, MITRE ATLAS for adversarial testing, and Gartner’s AI TRiSM model for executive-level risk management. The text promotes beginning with a concise policy, an inventory of existing AI applications, risk-scaled reviews, per-user data permissions, production monitoring, and low-risk pilots, while presenting Superblocks as a platform intended to provide centralized guardrails, visibility, audit records, and reviewable AI-generated code.

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