Adapting Threat Modeling for AI Applications at Scale
🛡️ The Microsoft Security Blog explains why threat modeling must be retooled for AI systems, noting that probabilistic behavior and complex input spaces require reasoning about ranges of likely outcomes rather than single execution paths. It identifies three core drivers — nondeterminism, instruction‑following bias, and system expansion through tools and memory — which widen attack surfaces and surface human‑centered risks like erosion of trust. The post advises starting from assets, mapping untrusted inputs, setting clear 'never do' boundaries, and embedding architectural mitigations, observability, and response plans to limit blast radius and sustain trust.
