AI
Generative AI security: risks that do not have names yet
Generative AI security needs attention to prompts, data, access, and automated decisions. Risk changes when the system answers and acts.
July 22, 2026|6 min read
The risk does not fit the old checklist
Generative AI changes the attack surface because the system no longer only stores or processes data. It interprets instructions, combines sources, summarizes context, and may trigger tools. That creates risks traditional controls do not describe well.
The issue is not that AI can "be wrong". The issue is allowing a wrong answer, context leak, or malicious instruction to enter a real decision flow.
The prompt became an attack interface
The OWASP GenAI Security Project organizes risks for LLM applications and agentic AI systems, including threats that cross prompts, context, plugins, data, and supply chain. Its value is naming what many companies still treat as a curiosity.
Prompt injection, sensitive data leakage, excessive tool permissions, and unverified output are symptoms of the same mistake: putting AI into production without boundaries.
Control starts before the model
Before discussing the model, define allowed data, prohibited data, users, logs, retention, sources of truth, available tools, and autonomy limits. If the assistant can read CRM, open tickets, or send messages, it needs access policy like any other critical system.
Create adversarial tests too. It is not enough to ask whether the bot answers well. Teams need to try to make it ignore rules, reveal data, invent sources, and execute actions outside the flow.
The governance question
Every generative AI application should answer three things: who can ask, what the system can know, and what it can do. Without those answers, security becomes late review over automation the business already wants.
Where Diglion comes in
Diglion helps design AI applications with data architecture, access controls, observability, and usage criteria. The goal is real gain without turning every prompt into a side door into operations.
Sources consulted
- OWASP, Top 10 for Large Language Model Applications, retrieved 2026-07-22.
- OWASP, GenAI Security Project, retrieved 2026-07-22.
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