Likelihood
An evidence-grounded estimate of how easily a given attack path could be walked, evaluated against the deployment's current, actual controls rather than a controls-free baseline.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Agentic AI Governance: Applied Mastery.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
In Bayes' theorem, the likelihood p(z | x) is the probability of observing the sensor reading z given that the true state is x. The likelihood function describes the sensor model: how probable each reading is for each possible true value. Peaking the likelihood at x means the sensor reading z strongly supports the true state being x.
In the risk matrix, an ordinal rating of how probable it is that an identified risk materializes in normal operation of the system as currently built, informed by evidence such as measured filter failure rates or red-team success rates rather than an unjustified numeric probability.
An evidence-grounded estimate of how easily a given attack path could be walked, evaluated against the deployment's current, actual controls rather than a controls-free baseline.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- Dossier Workshop E, Control Architecture and Threat Model · Technical Controls and Threat Modeling, Agentic AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.