[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"raw-en-use-cases\u002Fsecurity-ciso":3},"---\ntitle: IQ:NS for Security Teams\ndescription: How security teams use IQ:NS ontologies to map AI threat models across frameworks and integrate with existing security tooling.\nlang: en\nnavigation:\n  section: use-cases\n  label: Security \u002F CISO\n  order: 10\n---\n\n# IQ:NS for Security Teams\n\n## The challenge\n\nAI security risk spans multiple frameworks — OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, EU AI Act security provisions — each with different taxonomies and overlapping concepts.\n\n## What the ontologies provide\n\n- **Unified threat model** — adversarial risk, prompt injection, data poisoning, model theft mapped across all relevant frameworks in one structured vocabulary\n- **Cross-framework coverage** — see which controls each standard requires and where they overlap\n- **Integration-ready** — SPARQL queries feed into SIEM, observability, and monitoring tools\n- **Vendor assessment structure** — evaluate managed AI services (ChatGPT, Claude, Bedrock) against a consistent vocabulary\n\n## How it fits\n\nThe ontologies provide the semantic layer. Your existing SOC processes, SIEM tools, and incident response playbooks stay in place — they just get structured AI-specific context.\n\n[Explore the ontologies](https:\u002F\u002Fgithub.com\u002Fiqns-org\u002Fontologies) · [Get started](\u002Fgetting-started)\n",1776235632373]