We develop a novel mathematical framework for detecting deception in networkedartificial intelligence systems by applying Alan Turing's 1952 morphogenesis theoryto computational trust dynamics. The core contribution is the Velado Bound(D×I≥κ≈0.23), a fundamental theorem proving that perfect deception in distributedsystems is mathematically impossible. We derive this bound from Fisher-Rao geometricprinciples and establish critical phase transitions in trust distributions. Ourframework predicts non-associative trust composition and reveals unexpected symmetrywith the exceptional Lie group G₂, whose properties yield the Byzantine consensusthreshold of 2/3. Empirical validation across hundreds of trials confirms thedetection bound. These results establish irreducible mathematical limits on whatadversarial machines can achieve, regardless of sophistication, bridging fundamentalmathematics with AI governance.
Rafael Velado (Sun,) studied this question.