AI
AI touches security twice: as a system to be secured, with new data, supply chain and agent risks, and as a tool for defenders and attackers alike. The common thread is that models, plugins and agents act with real access, so they need the same ownership, limits and monitoring as any other privileged actor.
New to this? The simple version
Field notes
- The AI supply chain: models, plugins and tool servers as third parties
An AI feature is assembled from parts other people made: a hosted model, downloaded weights, editor plugins, tool servers and SaaS connectors. Each is a supplier with access, and the incidents of the last two years came through exactly those parts. Most third-party registers list only the first.
- Jev, the model that only decides: where it fits in security work
Jev is a new kind of AI model that writes no text. It reads something and answers typed questions with a choice, a score or a yes/no probability, in a fraction of a second and for a fraction of a cent. That makes it a good first sorter for alerts, data labels and agent actions, and a poor final judge.
- Which language model for which job
Large language models now come in four kinds, not one: frontier models for the hardest work, workhorses for most of it, small fast models for volume, and open-weight models you run yourself. The names change every month and the kinds do not, so choose by job, test one size down, and write policy about kinds rather than names.
- Agents are the new insiders, with API keys
An AI agent that can read mail, call APIs and change records is a privileged insider that can be talked into things. Treat it like one: an owner, the least access that works, and a record of what it did.
Tools
- DLP Framework
Includes a chapter on AI and data loss.
Personal work and my own view, not my employer's. Sources are public.