PRIAM CYBER AI
Blog
Field notes on AI-driven SOC operations, threat intelligence, and the economics of modern security.
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When "Plausible" Isn't Good Enough: Bringing Mathematical Certainty to AI-Driven Incident Response
LLMs are built for conversation, not statistical truth — and their false certainty is dangerous in a SOC. Here's how PEBRE grounds AI verdicts in mathematical rigor, not linguistic guesswork.
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The Third Verdict: Why "Inconclusive" Is the Most Honest Output an AI SOC Can Produce
Most AI SOC tools force a verdict to close tickets. But 'inconclusive' — with explicit evidence gaps named — is often the most honest, useful, and defensible output a system can produce.
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Benchmarking AI Playbooks: The Ultimate Guide to Public AI SOC Datasets
A practical guide to the public datasets and frameworks — CyberSecEval, ExCyTIn-Bench, SEC-bench, CybORG and more — for benchmarking and stress-testing your AI SOC playbooks before production.
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The Agentic Optimization Loop: Tuning the SOC with SFT, GRPO, and LoRA
Generic models lack the institutional instinct for true defense. Here is how SFT, GRPO, and LoRA turn a vanilla LLM into a specialized investigator that learns your SOC.
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Tokenomics in the SOC: A CISO's Guide to LLM Costs vs. Headcount
When AI agents can investigate incidents like analysts, capacity planning stops being about headcount and starts being about token economics. Where the math breaks even, and why elasticity wins.