Defensive AI Security & Governance
Building the discipline of autonomous defense — detecting drift, governing intent, and containing AI threats at machine speed, with the human kept where judgment matters.
Research-grade analysis of AI security — grounded in public, authoritative sources. Watch the narrated walkthrough of every topic.
Building the discipline of autonomous defense — detecting drift, governing intent, and containing AI threats at machine speed, with the human kept where judgment matters.
Attack & defend AI systems — threat labs, primitives and chains, every offensive step paired with its defense.
Most security education recycles the same dozen attacks. This threat lab covers the ones that break an assumption you never knew you were making — starting with the deepest of all: that two systems agree on what the same bytes mean. When a guardrail, a language model, and a tool each parse one input differently, the exploit lives in the disagreement, not in any single component. Each class is taught by the assumption it violates, the mechanism that makes it work, and the assumption-free defense — grounded in the seminal paper that named it, and tied back to the AI-agent stack every time.
The post-quantum transition beyond the lattice — the code-based and multivariate alternatives, what they cost, and where the standards stand.
The algorithms behind a model's answer — attention, the KV cache, speculative decoding, quantization, and vector recall — derived properly, to where each one breaks.
When one engineer holds the full context, plan, and intent for a feature, driving it end-to-end with an AI coding assistant can beat splitting the work across a team. This series builds the argument from evidence: why shared development pays a coordination tax that grows with team size, why context and cognitive load — not typing speed — are the real bottleneck, what AI assistants actually change by compressing the design-implement-test-refactor loop into a single continuous flow, when the single-conductor model delivers faster and more consistent results, and where genuine collaboration still wins. Grounded in Brooks' communication-overhead law, Conway's Law, cognitive-load and flow research, the GitHub Copilot productivity randomized trial, and the DORA delivery-performance findings.
Static analysis can find real defects in an agent's software substrate, but a clean report cannot prove that model-mediated intent, framework semantics, delegated authority, and multi-step behavior are secure. This series measures that boundary and develops an honest assurance envelope for reporting what was checked, what was missed, and what remains unknown.