{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"AI Security Ops","title":"AI Cost Saving Tips | Episode 55","html":"","width":"100%","height":180,"duration":1774,"description":"In this episode of BHIS Presents: AI Security Ops, the team digs into a problem every AI-enabled SOC eventually hits:The demo looked great — until the inference bill showed up!AI in SecOps gets expensive because security data is huge, repetitive, and constant. Logs, alerts, runbooks, tool definitions, and historical context all get pushed into models again and again. That burns money, slows systems down, and often makes answers worse.The fix is not exotic. It is basic engineering: use smaller models where they work, cache what repeats, stop dumping raw logs, and save expensive reasoning for the cases that actually need it.We dig into:• Why AI SecOps workloads get expensive fast  • When smaller models are good enough  • Where frontier models still make sense  • How grouping alerts into cases reduces waste  • Using strong models to judge cheaper models  • Why prompt caching can be a major cost lever  • How small prompt changes can break caching  • Batch APIs for non-urgent security work  • Why raw logs make prompts noisy and expensive  • RAG, deduplication, and cached verdicts  • Budget caps, circuit breakers, and stolen-key risk  • When deterministic code beats another model call  AI cost control is not just a budgeting exercise. It is a security architecture issue. If every alert goes to the biggest model with no caching, no limits, and no measurement, the system is not just expensive — it is uncontrolled. Good AI SecOps design means scoping the model, reducing unnecessary context, measuring spend, and putting guardrails around how AI is allowed to operate.⸻📚 Key Concepts & TopicsAI Cost Architecture  • SecOps cost comes from large inputs, repeated context, and high alert volume  • Model selection should match task difficulty  • Routine triage can often use smaller models  • Hard correlation and judgment may justify stronger models  Model Evaluation  • Test smaller models against real historical cases  • Use stronger models as judges when appropriate  • Compare...","thumbnail_url":"https://img.transistorcdn.com/mN9_Xu9UJwoaajIvIvLd-Yygv-Vh_nJwEDItjPY09kA/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8zYjBm/MzE1MWI2YmE4ZGJh/MDQ3MmJkMTkxZGNl/MjBjNS5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}