Countermeasures
Countermeasures for AI security must be compared across layers rather than discussed as isolated techniques.
Overview
This page is intended as the defense hub of the website. The most useful view is not a long flat list of defenses, but a structured comparison of which protections help at which layer and under what assumptions.
Threat model
Different defenses respond to different problems: software robustness for adversarial inputs, governance and filtering for generative misuse, runtime controls for agents, and hardware-level protections for edge and physical deployment.
Countermeasures
A strong countermeasure strategy often combines robust learning, system isolation, policy enforcement, monitoring, secure deployment, and platform-specific hardening. In real products, defense-in-depth matters more than any single technique.
Open challenges
The main challenge is composability. A defense that works in one layer may fail when combined with retrieval systems, agents, accelerators, or physical control loops. Building integrated, measurable security remains an open research direction.