/
Azure_AD_Graph_User_Attribute_Lookup.json
277 lines (277 loc) · 12.6 KB
/
Azure_AD_Graph_User_Attribute_Lookup.json
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
{
"blockly": false,
"blockly_xml": "<xml></xml>",
"category": "Attribute Lookup",
"coa": {
"data": {
"description": "Accepts a user or device and looks up the most recent attributes and groups for that user or device. This playbook produces a normalized output for each user and device.",
"edges": [
{
"id": "port_3_to_port_1",
"sourceNode": "3",
"sourcePort": "3_out",
"targetNode": "1",
"targetPort": "1_in"
},
{
"id": "port_0_to_port_14",
"sourceNode": "0",
"sourcePort": "0_out",
"targetNode": "14",
"targetPort": "14_in"
},
{
"conditions": [
{
"index": 0
}
],
"id": "port_14_to_port_2",
"sourceNode": "14",
"sourcePort": "14_out",
"targetNode": "2",
"targetPort": "2_in"
},
{
"id": "port_2_to_port_15",
"sourceNode": "2",
"sourcePort": "2_out",
"targetNode": "15",
"targetPort": "15_in"
},
{
"conditions": [
{
"index": 0
}
],
"id": "port_15_to_port_3",
"sourceNode": "15",
"sourcePort": "15_out",
"targetNode": "3",
"targetPort": "3_in"
}
],
"hash": "be0b1202e1571a6a8a4d345c6d14d30afcef8422",
"nodes": {
"0": {
"data": {
"advanced": {
"join": []
},
"functionName": "on_start",
"id": "0",
"type": "start"
},
"errors": {},
"id": "0",
"type": "start",
"warnings": {},
"x": 20,
"y": 0
},
"1": {
"data": {
"advanced": {
"join": []
},
"functionId": 1,
"functionName": "on_finish",
"id": "1",
"type": "end"
},
"errors": {},
"id": "1",
"type": "end",
"userCode": "\n # This function is called after all actions are completed.\n # summary of all the action and/or all details of actions\n # can be collected here.\n\n # summary_json = phantom.get_summary()\n # if 'result' in summary_json:\n # for action_result in summary_json['result']:\n # if 'action_run_id' in action_result:\n # action_results = phantom.get_action_results(action_run_id=action_result['action_run_id'], result_data=False, flatten=False)\n # phantom.debug(action_results)\n\n",
"warnings": {},
"x": 20,
"y": 864
},
"14": {
"data": {
"advanced": {
"customName": "playbook inputs filter",
"customNameId": 0,
"description": "Filter inputs to route inputs to appropriate actions.",
"join": [],
"note": "Filter inputs to route inputs to appropriate actions."
},
"conditions": [
{
"comparisons": [
{
"conditionIndex": 0,
"op": "!=",
"param": "playbook_input:user",
"value": "None"
}
],
"conditionIndex": 0,
"customName": "user",
"logic": "and"
}
],
"functionId": 1,
"functionName": "playbook_inputs_filter",
"id": "14",
"type": "filter"
},
"errors": {},
"id": "14",
"type": "filter",
"warnings": {
"config": [
"Reconfigure invalid datapath."
]
},
"x": 60,
"y": 148
},
"15": {
"data": {
"advanced": {
"customName": "user results filter",
"customNameId": 0,
"description": "Determine if a user was found from the preceding action",
"join": [],
"note": "Determine if a user was found from the preceding action"
},
"conditions": [
{
"comparisons": [
{
"conditionIndex": 0,
"op": "==",
"param": "get_user_attributes:action_result.status",
"value": "success"
}
],
"conditionIndex": 0,
"customName": "user exists",
"logic": "and"
}
],
"functionId": 2,
"functionName": "user_results_filter",
"id": "15",
"type": "filter"
},
"errors": {},
"id": "15",
"type": "filter",
"warnings": {},
"x": 60,
"y": 506
},
"2": {
"data": {
"action": "list user attributes",
"actionType": "investigate",
"advanced": {
"customName": "get user attributes",
"customNameId": 0,
"description": "Query for the user attributes for filtered playbook inputs.",
"join": [],
"note": "Query for the user attributes for filtered playbook inputs."
},
"connector": "Azure AD Graph",
"connectorConfigs": [
"azure_ad_graph"
],
"connectorId": "c6d3b801-5c26-4abd-9e89-6d8007e2778f",
"connectorVersion": "v1",
"functionId": 1,
"functionName": "get_user_attributes",
"id": "2",
"parameters": {
"user_id": "filtered-data:playbook_inputs_filter:condition_1:playbook_input:user"
},
"requiredParameters": [],
"type": "action"
},
"errors": {},
"id": "2",
"type": "action",
"warnings": {},
"x": 0,
"y": 328
},
"3": {
"data": {
"advanced": {
"customName": "format user outputs",
"customNameId": 0,
"description": "Format a normalized output for each user.",
"join": [],
"note": "Format a normalized output for each user."
},
"functionId": 1,
"functionName": "format_user_outputs",
"id": "3",
"inputParameters": [
"filtered-data:user_results_filter:condition_1:get_user_attributes:action_result.data.0",
"filtered-data:user_results_filter:condition_1:get_user_attributes:action_result.parameter.user_id"
],
"outputVariables": [
"observable_array"
],
"type": "code"
},
"errors": {},
"id": "3",
"type": "code",
"userCode": " \n format_user_outputs__observable_array = []\n \n # create normalized output dictionary\n for pb_input, user in zip(filtered_result_0_parameter_user_id, filtered_result_0_data_0):\n user_dict = {\n \"account_type\": \"Azure AD Account\",\n \"account_type_id\": 6,\n \"account_uid\": user['userPrincipalName'],\n \"domain\": user['userPrincipalName'].split('@')[1],\n \"email_addr\": user['mail'],\n \"full_name\": user['displayName'],\n \"name\": user['userPrincipalName'].split('@')[0],\n \"start_date\": user['createdDateTime'],\n \"type\": \"User\",\n \"type_id\": 1,\n \"uid\": user['objectId']\n }\n phones = [\n user['telephoneNumber'],\n user['facsimileTelephoneNumber']\n ]\n labels = [\n user['jobTitle'],\n user['department']\n ]\n \n \n # clean up phone numbers\n phones = [phone for phone in phones if phone]\n user_dict['phones'] = phones\n \n # clean up labels\n labels = [label for label in labels if label]\n user_dict['labels'] = labels\n \n final_output = {\n \"type\": \"user\",\n \"value\": pb_input,\n \"attributes\": user_dict,\n \"source\": \"Azure AD Graph\"\n }\n format_user_outputs__observable_array.append(final_output)\n \n # phantom.debug(format_user_outputs__observable_array)\n \n\n",
"warnings": {},
"x": 0,
"y": 686
}
},
"notes": "Inputs: user, device\nInteractions: AD LDAP\nActions: get attributes, run query\nOutputs: report, observables"
},
"input_spec": [
{
"contains": [
"user name"
],
"description": "A user identity in Active Directory such as sAMAccountName, userPrincipalName, or distinguishedName",
"name": "user"
},
{
"contains": [
"host name"
],
"description": "A device in Active Directory.",
"name": "device"
}
],
"output_spec": [
{
"contains": [],
"datapaths": [
"format_user_outputs:custom_function:observable_array",
"format_device_outputs:custom_function:observable_array"
],
"deduplicate": false,
"description": "An array of observable dictionaries ",
"metadata": {},
"name": "observable"
}
],
"playbook_type": "data",
"python_version": "3",
"schema": "5.0.9",
"version": "6.0.0.114895"
},
"create_time": "2023-03-02T14:17:17.726698+00:00",
"draft_mode": false,
"labels": [
"*"
],
"tags": [
"attributes",
"user",
"device",
"azure_ad_graph"
]
}