Most organizations treating AI as a cyber capability are solving the wrong problem. They are focused on models when the real challenge is infrastructure, governance, and operationalization. Prompt-heavy, API-dependent architectures are fragile by design. They do not scale, they cannot run in disconnected or classified environments, and they hand data sovereignty to someone else's cloud.
This session makes the case for a different approach: moving from prompt engineering to platform engineering, built around Models-as-a-Service delivered as shared, governed infrastructure that runs anywhere, including air-gapped and classified environments.
Attendees will walk away with a practical understanding of:
- Why smaller, domain-tuned models consistently outperform frontier models in mission settings
- How decomposed architectures using router models, orchestrators, and guardrails outperform monolithic deployments
- How agentic workflows accelerate SOC operations, threat detection, and software assurance without sacrificing control
- What a deployable, auditable, reproducible AI infrastructure looks like with Red Hat AI
The session closes with a decision framework for evaluating where AI belongs in your cyber environment and, equally important, where it does not.