Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
How AI agents plan with ReAct, plan and execute, reflection, tree search and multi-agent patterns: what each costs, how each fails and what bounds it.
Ohm's law says V = I × R; Kirchhoff's laws say current into a node equals current out and voltages round a loop sum to zero. Worked examples inside.
NetFlow and IPFIX turn traffic into flow records: who talked to whom, when, on which ports and how much. How export, collection, security and privacy work.
You cannot list a closed model's training data, but you can record each model ID, endpoint, data flow and retention setting you use, then ask vendors.
Article 53 makes GPAI providers publish a narrative summary of training content, not a dataset-level AIBOM. What the template asks for and what it omits.
Closed AI vendors describe training data in prose, not per-dataset lists. What stays unknown, the enterprise risks it creates, and how to manage them.
Anthropic and OpenAI publish system cards and prose training data descriptions, not machine readable AIBOMs. Here is the gap, field by field, and what to do.
A PCB starts as glass epoxy clad in copper, is imaged, etched, laminated, drilled and plated, then gets parts soldered on, tested and its first firmware.
A hard drive is sputtered platters, wafer built heads flying nanometres above them, a spindle motor and firmware. Here is each step, substrate to box.
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