Validated on these results, we applied this optical model to fit maps of the passive layer's normalized optical intensities from Period 1, focusing on areas where two pits later formed during CA in Period 2 (Figure ). A bilayer structure consisting of Cr 2 O 3 and α-Fe 2 O 3 was modeled, with each layer being 0.2 nm in thickness. In regions where chromium depletion occurred, iron oxides served as replacement compounds. The experimental normalized intensities (shown in Figure and) exhibit very slight variations, ranging from 98.8% to 99.4%. Areas where pits eventually formed, at approximately 10 µm and 30 µm, show a slightly lower intensity of 98.8% compared to the rest of the profile. It is important to note that these are not yet pits, as identical values of 98.8% are also observed in other regions, visible as dark blue areas in Figure , where no pits appear. According to the fit in Figure , these slight variations can be attributed to a depletion of Cr 2 O 3 (as shown in Figure ), which may explain the susceptibility of these regions to pitting, in agreement with the literature. In spite of the plausibility of this explanation, it is noteworthy to mention that only a small fraction of all areas, identified as PSZ (Figure ) ultimately evolve into sites of pit formation, suggesting at least two possibilities. The first is that the entirety of PSZ is actually Cr-depleted passive layer regions, with the areas where pits form experiencing a more extreme deficiency not detectable with the current setup. The second possibility is that the change in normalized optical intensities may not solely be attributable to chromium deficiency, but also to other factors unrelated to the chromium layer. These may include the presence of trace amounts of Mo and Ni, as reported in the literature , among other factors. While these avenues suggest compelling future research directions, we emphasize that the presented ML-enhanced optical methodology has demonstrated efficacy in identifying regions susceptible to pitting corrosion, correlated to nanoscale heterogeneities within the passive layer. Furthermore, the described analysis of optical signatures is, in principle, scalable and applicable to metallic surfaces employed in structural engineering-for example, in bridges and aircraft-to facilitate the development of predictive nanoscopic corrosion monitoring systems.