data = RandomVariate[BinormalDistribution[.5], 200];
    skdPDF = PDF[SmoothKernelDistribution[data]];

Define  multivariate "quantiles" based on the height of the kernel density function. The function `volume[z]` gives the total probability of the set of points where density exceeds `z`:

    volume[z_?NumericQ] := Quiet @ NIntegrate[skdPDF[{s, t}]Boole[skdPDF[{s, t}] >= z],
      {s, -∞, ∞}, {t, -∞, ∞}]

Find the density threshold levels corresponding to desired probability coverages (this is very slow):

    {t99, t95, t68} = Quiet[FindRoot[volume[z] - # == 0, {z, 0, 1}]]& /@ {.99, .95, .68}
>    {{z -> 0.002508}, {z -> 0.008514}, {z -> 0.045498}}

    
    SmoothDensityHistogram[data, MeshFunctions -> {skdPDF[{#, #2}] &}, 
      Mesh -> {{{z /. t99, Red}, {z /. t95, Green}, {z /. t68, Purple}}},
      MeshStyle -> Thick, PlotRange -> {{-4, 4}, {-4, 4}},
      Epilog -> {Black, PointSize[Medium], Point @ data}]
[![enter image description here][1]][1]


  [1]: https://i.sstatic.net/fYn6R.png