Summary: Let \(\{X_1,\dots , X_n\}\) be a collection of binary-valued random variables and let \(f : \{0, 1\}^n \to \mathbb{R}\) be a Lipschitz function. Under a negative dependence hypothesis known as the strong Rayleigh condition, we show that \(f-\mathbb{E}f\) satisfies a concentration inequality. The class of strong Rayleigh measures includes determinantal measures, weighted uniform matroids and exclusion measures; some familiar examples from these classes are generalized negative binomials and spanning tree measures. For instance, any Lipschitz-1 function of the edges of a uniform spanning tree on a vertex set \(V\) (e.g. the number of leaves) satisfies the Gaussian concentration inequality \[ \mathbb{P}(f-\mathbb{E}f\geq a)\leq \exp\bigg(-\frac{a^2}{8|V|}\bigg). \] We also prove a continuous version for concentration of Lipschitz functionals of a determinantal point process.