ML//deployment risk

Deployment risk is the expected harm created by putting a system into a particular operating environment. It depends on capability, probability of failure or misuse, exposure, permissions, affected assets and the controls that limit consequences; agent containment lowers it by bounding consequences even while model behavior remains imperfect.

Deployment risk is the expected harm created by putting a system into a particular operating environment. It depends on capability, probability of failure or misuse, exposure, permissions, affected assets and the controls that limit consequences; agent containment lowers it by bounding consequences even while model behavior remains imperfect.

This makes risk contextual. The same model inside an offline read-only analysis tool and an autonomous production administrator does not create the same risk. Capability evaluations matter, but they describe only part of that product.

Risk does not live inside the weights. It emerges where capability meets opportunity and consequence.