mysticmomba/AirlockTools
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Added Preflights

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modified:   AirlockTools.py
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2025-08-26 17:12:06 -04:00
committed by brotoskyj
parent 83593b9caa
commit 8ace912788
1 changed files with 30 additions and 3 deletions
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AirlockTools.py
+30 -3
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@@ -211,7 +211,7 @@ def menu_prepare_to_enforce():
print(ct.colorText(" Remove the rows containing path exclusions you do not approve of" , "cyan"))
print(ct.colorText(" When complete, save the csv file to the directory 'manuallyapproved' and choose this option to generate the preflight lists", "cyan"))
if os.path.isfile(f"manuallyapproved\\paths_needing_review_{first_policy}_{second_policy}.csv") and os.path.isfile("dataframe_csv\\hashdestination_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\addtochildpolicy_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\addtobaseline_{first_policy}_{second_policy}.csv"):
if os.path.isfile(f"manuallyapproved\\df_paths_needing_review_{first_policy}_{second_policy}.csv") and os.path.isfile("dataframe_csv\\df_hashdestination_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\df_addtochildpolicy_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\f_addtobaseline_{first_policy}_{second_policy}.csv"):
print(ct.colorText(" [✓] This step has been completed","green"))
else:
print(ct.colorText(" [✗] This step has not been completed","red"))
@@ -332,8 +332,35 @@ def menu_prepare_to_enforce():
print(ct.colorText(f"Please manually approve hashes prior to this step","red"))
elif choice == "4":
pass
elif choice == "7":
if os.path.exists(f"manuallyapproved\\df_paths_needing_review_{first_policy}_{second_policy}.csv"):
df_approved = tryToReadCSV(f"manuallyapproved\\df_paths_needing_review_{first_policy}_{second_policy}.csv")
df_eligible = tryToReadCSV(f"dataframe_csv\\df_paths_needing_review_{first_policy}_{second_policy}.csv")
df_ineligible = tryToReadCSV(f"dataframe_csv\\df_path_ineligible_{first_policy}_{second_policy}.csv")
approved_set = set([tuple(map(tuple, row)) for row in df_approved.values])
# Identify rows in eligible that are not in approved
not_approved_rows = df_eligible[~df_eligible.apply(lambda row: tuple(map(tuple, row)) in approved_set, axis=1)]
# Append these rows to ineligible
df_ineligible= pd.concat([df_ineligible, not_approved_rows], ignore_index=True)
df_approved.to_csv(f"preflight\\Approved_Path_Exclusions_{first_policy}_{second_policy}.csv")
ct.style_dataframe_dark(df_approved, f"preflight\\Approved_Path_Exclusions_{first_policy}_{second_policy}.html")
#Seperate out what we arent excluding by path into those that will go into the baseline, and those that will b added to the child.
df_addtobaseline = df_ineligible[df_ineligible['reputation status'] == 'KNOWN']
df_addtobaseline.to_csv(f"preflight\\Add_to_Baseline_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(df_addtobaseline, f"preflight\\Add_to_Baseline_{first_policy}_{second_policy}.html")
df_addtochildpolicy = df_ineligible[df_ineligible['reputation status'] == 'UNKNOWN']
df_addtochildpolicy.to_csv(f"preflight\\Add_to_Child_Policy_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(df_addtochildpolicy, f"preflight\\Add_to_Child_Policy_{first_policy}_{second_policy}.html")
else:
print(ct.colorText(f"Please manually approve suggested paths prior to this step","red"))