We derive an Itô stochastic differential equation for entropy production in nonequilibrium Langevin processes. Introducing a random-time transformation, entropy production obeys a onedimensional drift-diffusion equation, independent of the underlying physical model. This transformation allows us to identify generic properties of entropy production. It also leads to an exact uncertainty equality relating the Fano factor of entropy production and the Fano factor of the random time, which we also generalize to non steady-state conditions. The laws of thermodynamics can be extended to mesoscopic systems [1][2][3][4][5]. For such systems, energy changes on the order of the thermal energy k B T are relevant. Here, k B is the Boltzmann constant and T the temperature. Therefore, thermodynamic observables associated with mesoscopic degrees of freedom are stochastic. A key example of such thermodynamics observables is the stochastic entropy production in nonequilibrium processes. Recent experimental advances in micromanipulation techniques permit the measurement of stochastic entropy production in the laboratory [6][7][8][9][10].Certain statistical properties of stochastic entropy production are generic, i.e., they are independent of the physical details of a system. Examples of such generic properties are the celebrated fluctuation theorems, for reviews see [2,4,5]. Recently, it was shown that infima and passage probabilities of entropy production are also generic [11]. Other statistical properties of entropy production are system-dependent, such as the mean value [12][13][14][15], the variance [16,17], the first-passage times of entropy production [18][19][20] and the large deviation function [21,22]. Nevertheless, these properties are sometimes constrained by universal bounds [11,14,16,17,[23][24][25][26][27]. It remains unclear which statistical properties of stochastic entropy production are generic, and why.In this Letter, we introduce a theoretical framework which addresses this question for nonequilibrium Langevin processes. We identify generic properties of entropy production by their independence of a stochastic variable τ which we call entropic time. We find that the evolution of steady-state entropy production as a function of τ is governed by a simple one-dimensional driftdiffusion process, independent of the underlying model. This allows us to identify a set of generic properties of entropy production and obtain exact results characterizing entropy production fluctuations.We consider a mesoscopic system described by n slow degrees of freedom X = (X 1 (t), X 2 (t), . . . , X n (t)) T . The system is in contact with a thermostat at temperature T . The stochastic dynamics of the system can be described by the probability distribution P ( X, t) to find the system in a configuration X at time t . This probability distribution satisfies the Smoluchowski equationwhere the probability current is given byHere we have introduced the force at time t, F = − ∇U ( X(t), t) + f ( X(t), t), where U is a potential and f is a...