+# CAC Ontology Modeling — Activity
+
+Slides: 9-10
+Time: 3-5 minutes
+
+## Learning Objective
+Understand what the CAC Ontology captures that CASE/UCO doesn't, and how AI agents can produce compliant investigation graphs from raw text.
+
+---
+
+## Activity: What Does CAC Ontology Add?
+
+Scenario:
+A CyberTip describes grooming behavior where an adult posing as a minor made sexual contact through video chat on three different platforms over two weeks. The victim was identified through a school liaison officer. A multi-jurisdictional task force (VICN) led the investigation with support from NCMEC and local FBI field offices.
+
+### Your Task (think aloud)
+
+1. What concepts would CASE/UCO already cover?
+> Answer: Investigations, evidence files, devices, hash analysis, chain of custody, forensic tools, actions, provenance
+
+2. What concepts would the CAC Ontology add?
+> Answer:
+> - CyberTip processing and NCMEC integration
+> - Grooming behaviors and enticement patterns
+> - Platform-specific evidence (video chat logs)
+> - Victim identification through school liaison
+> - Multi-jurisdictional task force coordination
+> - Operational timelines across investigators
+
+3. Why does the CAC Ontology matter for AI agents?
+> Answer: Because it gives the AI a vocabulary for concepts specific to crimes against children investigations. When you tell an AI to "model this grooming investigation," it doesn't know how to structure the output unless you give it a domain-specific ontology — the CAC Ontology.
+
+---
+
+## Discussion
+
+The CAC Ontology is 35+ specialized modules with 2,154 classes and 97 ontology files. It's the most comprehensive forensic investigation vocabulary ever created.
+
+> "In the lab, you'll use an AI agent to take raw text (like a press release) and convert it into a CAC Ontology knowledge graph. The agent reads the ontology definition, understands the concepts, and produces structured, machine-readable investigation data."
+
+This is how we've democratized interoperable investigation data — any investigator who can describe their case can now produce standardized data using AI.
+# CAC Ontology Modeling — Activity
+
+Slides: 9-10
+Time: 3-5 minutes
+
+## Learning Objective
+Understand what the CAC Ontology captures that CASE/UCO doesn't, and how AI agents can produce compliant investigation graphs from raw text.
+
+---
+
+## Activity: What Does CAC Ontology Add?
+
+Scenario:
+A CyberTip describes grooming behavior where an adult posing as a minor made sexual contact through video chat on three different platforms over two weeks. The victim was identified through a school liaison officer. A multi-jurisdictional task force (VICN) led the investigation with support from NCMEC and local FBI field offices.
+
+### Your Task (think aloud)
+
+1. What concepts would CASE/UCO already cover?
+> Answer: Investigations, evidence files, devices, hash analysis, chain of custody, forensic tools, actions, provenance
+
+2. What concepts would the CAC Ontology add?
+> Answer:
+> - CyberTip processing and NCMEC integration
+> - Grooming behaviors and enticement patterns
+> - Platform-specific evidence (video chat logs)
+> - Victim identification through school liaison
+> - Multi-jurisdictional task force coordination
+> - Operational timelines across investigators
+
+3. Why does the CAC Ontology matter for AI agents?
+> Answer: Because it gives the AI a vocabulary for concepts specific to crimes against children investigations. When you tell an AI to "model this grooming investigation," it doesn't know how to structure the output unless you give it a domain-specific ontology — the CAC Ontology.
+
+---
+
+## Discussion
+
+The CAC Ontology is 35+ specialized modules with 2,154 classes and 97 ontology files. It's the most comprehensive forensic investigation vocabulary ever created.
+
+> "In the lab, you'll use an AI agent to take raw text (like a press release) and convert it into a CAC Ontology knowledge graph. The agent reads the ontology definition, understands the concepts, and produces structured, machine-readable investigation data."
+
+This is how we've democratized interoperable investigation data — any investigator who can describe their case can now produce standardized data using AI.