We revisit classic string problems considered in the area of parameterized complexity, and study them through the lens of dynamic data structures. That is, instead of asking for a static algorithm that solves the given instance efficiently, our goal is to design a data structure that efficiently maintains a solution, or reports a lack thereof, upon updates in the instance.We first consider the Closest String problem, for which we design randomized dynamic data structures with amortized update times d O(d) and |Σ| O(d) , respectively, where Σ is the alphabet and d is the assumed bound on the maximum distance. These are obtained by combining known static approaches to Closest String with color-coding.Next, we note that from a result of Frandsen et al. [J. ACM'97] one can easily infer a metatheorem that provides dynamic data structures for parameterized string problems with worstcase update time of the form O k (log log n), where k is the parameter in question and n is the length of the string. We showcase the utility of this meta-theorem by giving such data structures for problems Disjoint Factors and Edit Distance. We also give explicit data structures for these problems, with worst-case update times O(k2 k log log n) and O(k 2 log log n), respectively. Finally, we discuss how a lower bound methodology introduced by Amarilli et al. [ICALP'21] can be used to show that obtaining update time O(f (k)) for Disjoint Factors and Edit Distance is unlikely already for a constant value of the parameter k.