How to Use Cortex4py

This document is a usage guide of the Cortex4py library for writing custom scripts that interact with the Cortex 2 APIs.

Cortex4py 2 requires Python 3. It does not work with Cortex 1.x.

Introduction

Cortex4py 2 is a new version of the library, that is only compatible with Cortex 2.x. It supports authentication and covers almost all the available APIs, including administration calls.

Library Architecture

Cortex4py 2 has the following structure:

├── cortex4py
│   ├── api
│   ├── controllers
│   │   ├── abstract
│   │   ├── analyzers
│   │   ├── jobs
│   │   ├── organizations
│   │   └── users
│   ├── exceptions
│   ├── models
│   │   ├── analyzer
│   │   ├── analyzer_definition
│   │   ├── job
│   │   ├── job_artifact
│   │   ├── model
│   │   ├── organization
│   │   └── user
│   └── query

The model classes represent the data objects and extend the cortex4py.models.Model that provides json() methods returning a JSON dict from every model object.
The controllers classes wrap the available methods that call Cortex APIs.
The api class is the main class giving access to the different controllers.
query.* are utility methods that allow building search queries.
exceptions.* are supported exceptions

Migration

If you have already written scripts using cortex4py 1.x (for Cortex 1), we tried to keep the already available methods. However, we recommend you adapt your code to leverage the new cortex4py 2 classes and methods as soon as feasible. Moreover, the existing scripts must be updated to support authentication if you intend to use them with Cortex 2.

To instantiate a Cortex 1 API object, developers used to write the following code:

from cortex4py.api import CortexApi

api = CortexApi('http://CORTEX_APP_URL.1:9000')

This code must be replaced with something like the example below for Cortex 2:

from cortex4py.api import Api

api = Api('http://CORTEX_APP_URL:9001', '**API_KEY**')

Proxy and Certificate Verification

Cortex4py 2 allows specifying a proxy configuration should your program requires one. The library also adds an option to enable or disable certificate verification.

from cortex4py.api import Api

api = Api('http://CORTEX_APP_URL:9001', '**API_KEY**', proxies={
    'http': 'http://PROXY_URL:PROXY_PORT',
    'https': 'http://PROXY_URL:PROXY_PORT'
}, verify_cert=False)

verify_cert can be:

True
False
String representing the path to the certificate file.

Note: verify_cert replaces the Cortex4py 1 cert argument which has been deprecated.

Backward Compatibility

Cortex4py 2 implements the methods that were available in the old version of the library:

from cortex4py.api import Api

api = Api('http://CORTEX_APP_URL:9001', '**API_KEY**')

analyzers = api.get_analyzers('ip')
job = api.run_analyzer('Abuse_Finder_2_0', 'domain', 2, 'google.com')
job_with_report = api.get_job_report('XXXXXX', timeout='Inf')        
job_deleted = api.delete_job('XXXXXX')

Note: These methods are now deprecated.

Exception Handling

All the operations supported by the library can raise errors that inherit a cortex4py.exceptions.CortexException exception class.

Possible errors are listed below:

Error Exception	Error message	Description
`cortex.exceptions.NotFoundError`	`Resource not found`	A 404 error occurred
`cortex.exceptions.AuthenticationError`	`Authentication error`	A 401 error occurred
`cortex.exceptions.AuthorizationError`	`Authorization error`	A 403 error occurred
`cortex.exceptions.InvalidInputError`	`Invalid input exception`	A 400 error occurred
`cortex.exceptions.ServiceUnavailableError`	`Cortex service is unavailable`	Connection issue. Cortex is not available
`cortex.exceptions.ServerError`	`Cortex request exception`	A 500 error occurred
`cortex.exceptions.CortexError`	`Unexpected exception`	An unhandled error occurred

Organization Operations

The OrganizationController class provides a set of methods to deal with Cortex organizations.

Model

An organization is represented by the following model class:

Field	Description	Type
`id`	Organization's identifier	readonly
`name`	Organization's name, can be specified during creation only.	readonly
`description`	Organization's description	writable
`status`	Organization's status, `Active` or `Locked`	writable
`createdAt`	Creation date	computed
`createdBy`	User who created the org	computed
`updatedAt`	Last update	computed
`updatedBy`	User who last updated the org	computed

Methods

Method	Description	Return type
`count(query)`	Requires `superadmin` role, Returns the number of organizations corresponding to the `query`	Number
`find_all(query,**kwargs)`	Requires `superadmin` role, returns a list of `Organization` objects, based on `query`, `range` and `sort` parameters	List[Organization]
`find_one_by(query,**kwargs)`	Requires `superadmin` role, returns the first `Organization` object, based on `query` and `sort` parameters	Organization
`get_by_id(org_id)`	Requires `orgadmin` or `superadmin` roles, returns an `Organization` by its `id`	Organization
`get_users(org_id,query,**kwargs)`	Requires `orgadmin` role, returns the list of `User` objects remaining to the `Organization` identified by `org_id`	List[User]
`get_analyzers()`	Requires `orgadmin` role, returns the list of enabled `Analyzer` objects remaining to the `Organization` of the current user	List[Analyzer]
`create(data)`	Requires `superadmin` role, returns the create `Organization` object. `data` could be a JSON or `Organization` objects	Organization
`update(org_id,data,fields)`	Requires `superadmin` role, returns the updated `Organization` object. `data` can be a JSON or `Organization` object. `fields` parameter is an array of field names to update	Organization
`delete(org_id)`	Requires `superadmin` role, returns `true` if the delete completes successfully	Boolean

Examples

The following example shows how to manipulate organizations as a superadmin user:

from cortex4py.api import Api
from cortex4py.query import *
from cortex4py.models import Organization

api = Api('http://CORTEX_APP_URL:9001', '**API_KEY**')

# Find how many locked organizations exist
count = api.organizations.count(Eq('status', 'Locked'))

# Fetch the last 10 created organizations
locked_orgs = api.organizations.find_all({}, range='0-10', sort='-createdAt')

# Display the name of the locked organizations
for org in locked_orgs:
  print('Organization {} is {}'.format(org.name, org.status))

# Create a new organization
new_org = api.organizations.create(Organization({
    "name": "demo",
    "description": "This is a demo organization",
    "status": "Active"
}))

# Display its id
print(new_org.id)

# Update the newly created org
new_org = api.organizations.update(new_org.id, {
  'description': 'This is an disabled organization'
})

# Delete the newly created org
api.organizations.delete(new_org.id)

The following example shows how to manipulate organizations as a orgadmin user:

import json

from cortex4py.api import Api
from cortex4py.query import *

api = Api('http://CORTEX_APP_URL:9001', '**API_KEY**')

# Get details of an organization
org = api.organizations.get_by_id('demo')

# Print the json representation of the Organization object
print(json.dumps(org.json(), indent=2))

# Fetch the last 5 created and active users
users = api.organizations.get_users(org.id, Eq('status', 'Ok'), range='0-5', sort='-createdAt')

# Display the usernames
for user in users:
  print('User {} has roles {}'.format(user.name, user.roles))

# Fetch the organization analyzers
for a in api.organizations.get_analyzers():
  print(a.name)

User Operations

The UserController class provides a set of methods to handle users.

Model

A user is represented by the following model class:

Field	Description	Type
`id`	Users's identifier	readonly
`login`	User's login, can be specified during creation only.	readonly
`name`	User's full name	writable
`organization`	Users's organization. Can be specified during the creation of the user, or updated only by `superadmin` users	writable
`status`	User's status, `Ok` or `Locked`	writable
`createdAt`	Creation date	computed
`createdBy`	User who created the org	computed
`updatedAt`	Last update	computed
`updatedBy`	User who last updated the org	computed
`hasKey`	true when the user has an API key	computed
`hasPassword`	true if the user has a password	computed

Methods

Method	Description	Return type
`find_all(query,**kwargs)`	Returns a list of `User` objects, based on `query`, `range` and `sort` parameters	List[User]
`find_one_by(query,**kwargs)`	Returns the first `User` object, based on `query` and `sort` parameters	User
`get_by_id(user_id)`	Returns a `User` by its `user_id`	User
`create(data)`	Returns the create `User` object. `data` could be a JSON or `User` objects	User
`update(user_id,data,fields)`	Returns the updated `User` object. `data` can be a JSON or `User` object. `fields` parameter is an array of field names to update	User
`lock(user_id)`	Returns the locked user after setting its status to `Locked`	User
`set_password(user_id,password)`	Returns `true` if the update completes successfully	Boolean
`change_password(user_id,current,newpass)`	Returns `true` if the update completes successfully. Needs to be called by the user itself.	Boolean
`set_key(user_id)`	Returns the created API key	String
`renew_key(user_id)`	Returns the renewed API key	String
`get_key(user_id)`	Returns the API key of the user identified by `user_id`	String
`revoke_key(user_id)`	Returns `true` if the API key is revoked successfully	Boolean

Examples

The following example shows how to manipulate users:

import json
import uuid

from cortex4py.api import Api
from cortex4py.query import *

api = Api('http://CORTEX_APP_URL:9001', '**API_KEY**')

# List the latest 10 active users
users = api.users.find_all(Eq('status', 'Ok'), range='0-10', sort='-createdAt')

# Display the users' logins and roles
for user in users:
  print('User {} has roles {}'.format(user.name, user.roles))

# Create a new user
rand = str(uuid.uuid4())[:6]
new_user = api.users.create(User({
    'login': 'User-{}'.format(rand),
    'name': 'User {}'.format(rand),
    'roles': ['read', 'analyze'],
    'status': 'Ok',
    'organization': 'demo'
}))

# Display the created user details
print(json.dumps(new_user.json(), indent=2))

# Update the user's name and roles
api.users.update(new_user.id, {
  'name': 'New User',
  'roles': ['read']
})

user_id = new_user.id

# Set user's password
api.users.set_password(user_id, 'password')

# Set user's API Key
key1 = api.users.set_key(user_id)
print(key1)

# Get user's API Key
key = api.users.get_key(user_id)
print(key)

# Renew user's API key
key2 = api.users.renew_key(user_id)
print(key2)

# Compare keys
print(key1 == key2)

# Revoke the user's API key
api.users.revoke_key(user_id)

# Lock the user
api.users.lock(user_id)

# Get the user details
user = api.users.get_by_id(user_id)

# check some assertions
print(user.hasKey == False)
print(user.hasPassword == True)
print(user.status == 'Locked')

Analyzer Operations

The AnalyzersController class provides a set of methods to handle analyzers.

Model

An analyzer is an instance of an analyzer definition, and both models share the same fields.

An analyzer definition is represented by the following model class:

Field	Description	Type
`id`	Analyzer ID once enabled within an organization	readonly
`analyzerDefinitionId`	Analyzer definition name	readonly
`name`	Name of the analyzer	readonly
`version`	Version of the analyzer	readonly
`description`	Description of the analyzer	readonly
`author`	Author of the analyzer	readonly
`url`	URL where the analyzer has been published	readonly
`license`	License of the analyzer	readonly
`dataTypeList`	Allowed datatypes	readonly
`configurationItems`	A list that describes the configuration options of the analyzer	readonly
`baseConfig`	Base configuration name. This identifies the shared set of configuration with all the analyzer's flavors	readonly
`createdBy`	User who enabled the analyzer	computed
`updatedAt`	Last update date	computed
`updatedBy`	User who last updated the analyzer	computed

An analyzer is represented by the following model class:

Field	Description	Type
`id`	Analyzer ID once enabled within an organization	readonly
`analyzerDefinitionId`	Analyzer definition name	readonly
`name`	Name of the analyzer	readonly
`version`	Version of the analyzer	readonly
`description`	Description of the analyzer	readonly
`author`	Author of the analyzer	readonly
`url`	URL where the analyzer has been published	readonly
`license`	License of the analyzer	readonly
`dataTypeList`	Allowed datatypes	readonly
`baseConfig`	Base configuration name. This identifies the shared set of configuration with all the analyzer's flavors	readonly
`jobCache`	Report cache timeout in minutes, visible for `orgAdmin` users only	writable
`jobTimeout`	Job timeout in minutes, visible for `orgAdmin` users only	writable
`rate`	Numeric amount of analyzer calls authorized for the specified `rateUnit`, visible for `orgAdmin` users only	writable
`rateUnit`	Period of availability of the rate limite: `Day` or `Month`, visible for `orgAdmin` users only	writable
`configuration`	A JSON object where key/value pairs represent the config names, and their values. It includes the default properties `proxy_http`, `proxy_https`, `auto_extract_artifacts`, `check_tlp`, and `max_tlp`, visible for `orgAdmin` users only	writable
`createdBy`	User who enabled the analyzer	computed
`updatedAt`	Last update date	computed
`updatedBy`	User who last updated the analyzer	computed

Methods

Method	Description	Return type
`find_all(query,**kwargs)`	Returns a list of `Analyzer` objects, based on `query`, `range` and `sort` parameters	List[Analyzer]
`find_one_by(query,**kwargs)`	Returns the first `Analyzer` object, based on `query` and `sort` parameters	Analyzer
`get_by_id(analyzer_id)`	Returns a `Analyzer` by its `id`	Analyzer
`get_by_name(name)`	Returns a `Analyzer` by its `name`	Analyzer
`get_by_type(data_type)`	Returns a list of available `Analyzer` applicable to the given `data_type`	List[Analyzer]
`enable(analyzer_name,config)`	Activate an analyzer and returns its `Analyzer` object	Analyzer
`update(analyzer_id)`	Update the configuration of an `Analyzer` and returns the updated version	Analyzer
`disable(analyzer_id)`	Removes an analyzer from an organization and returns `true` if it completes successfully	Boolean
`run_by_id(analyzer_id,observable,**kwargs)`	Returns a `Job` by its `name`	Job
`run_by_name(analyzer_name,observable,**kwargs)`	Runs an analyzer by its name and returns the resulting `Job`	Job
`definitions()`	Returns the list of all the analyzer definitions including the enabled and disabled analyzers	List[AnalyzerDefinition]

Examples

The following example shows how to manipulate analyzers:

import json

from cortex4py.api import Api
from cortex4py.query import *

api = Api('http://CORTEX_APP_URL:9001', '**API_KEY**')

# Get enabled analyzers
analyzers = api.analyzers.find_all({}, range='all')

# Display enabled analyzers' names
for analyzer in analyzers:
  print('Analyzer {} is enabled'.format(analyzer.name))

# Get enabled analyzers that can run against a domain
domain_analyzers = api.analyzers.get_by_type('domain')

# Enable the analyzer called Test_1_0
analyzer = api.analyzers.enable('Test_1_0', {
  "configuration": {
    "api_key": "XXXXXXXXXXXXXx",
    "proxy_http": "http://localhost:9999",
    "proxy_https": "http://localhost:9999",
    "auto_extract_artifacts": False,
    "check_tlp": True,
    "max_tlp": 2,
    "max_pap": 2
  },
  "jobCache": 10,
  "jobTimeout": 30,
  "rate": 1000,
  "rateUnit": "Day"
})

# Print the details of the enaled analyzer
print(json.dumps(analyzer.json(), indent=2))
print(analyzer.workerDefinitionId == 'Test_1_0')

# Update the configuration
analyzer_id = analyzer.id
analyzer = api.analyzers.update(analyzer.id, {
  "rate": 100,
  "rateUnit": "Day",
  "jobCache": null,
  "configuration": {
    "api_key": "YYYYYYYYYYY",
    "proxy_http": null,
    "proxy_https": null,
    "auto_extract_artifacts": True,
    "check_tlp": false,
    "max_tlp": null,
    "max_pap": 2
  }
})

# Run an analyzer against a domain
job1 = api.analyzers.run_by_name('Test_1_0', {
    'data': 'google.com',
    'dataType': 'domain',
    'tlp': 1,
    'message': 'custom message sent to analyzer',
    'parameters': {
        'key1': 'value1',
        'key2': True,
        'key3': 10
    }
}, force=1)
print(json.dumps(job1.json(), indent=2))

# Run an analyzer against a file
job2 = api.analyzers.run_by_name('File_Info_2_0', {
    'data': '/tmp/sample.txt',
    'dataType': 'file',
    'tlp': 1
}, force=1)
print(json.dumps(job2.json(), indent=2))

# Disable an analyzer
api.analyzers.disable(analyzer_id)

Responder Operations

The RespondersController class provides a set of methods to handle responders.

Model

A responder is an instance of a responder definition, and both models share the same fields.

A responder definition is represented by the following model class:

Field	Description	Type
`id`	Responder ID once enabled within an organization	readonly
`workerDefinitionId`	Responder definition name	readonly
`name`	Name of the responder	readonly
`version`	Version of the responder	readonly
`description`	Description of the responder	readonly
`author`	Author of the responder	readonly
`url`	URL where the responder has been published	readonly
`license`	License of the responder	readonly
`dataTypeList`	Allowed datatypes	readonly
`configurationItems`	A list that describes the configuration options of the responder	readonly
`baseConfig`	Base configuration name. This identifies the shared set of configuration with all the responder's flavors	readonly
`createdBy`	User who enabled the responder	computed
`updatedAt`	Last update date	computed
`updatedBy`	User who last updated the responder	computed

A responder is represented by the following model class:

Field	Description	Type
`id`	Responder ID once enabled within an organization	readonly
`workerDefinitionId`	Responder definition name	readonly
`name`	Name of the responder	readonly
`version`	Version of the responder	readonly
`description`	Description of the responder	readonly
`author`	Author of the responder	readonly
`url`	URL where the responder has been published	readonly
`license`	License of the responder	readonly
`dataTypeList`	Allowed datatypes	readonly
`baseConfig`	Base configuration name. This identifies the shared set of configuration with all the responder's flavors	readonly
`jobCache`	Report cache timeout in minutes, visible for `orgAdmin` users only	writable
`rate`	Numeric amount of responder calls authorized for the specified `rateUnit`, visible for `orgAdmin` users only	writable
`rateUnit`	Period of availability of the rate limite: `Day` or `Month`, visible for `orgAdmin` users only	writable
`configuration`	A JSON object where key/value pairs represent the config names, and their values. It includes the default properties `proxy_http`, `proxy_https`, `auto_extract_artifacts`, `check_tlp`, and `max_tlp`, visible for `orgAdmin` users only	writable
`createdBy`	User who enabled the analyzer	computed
`updatedAt`	Last update date	computed
`updatedBy`	User who last updated the analyzer	computed

Methods

Method	Description	Return type
`find_all(query,**kwargs)`	Returns a list of `Responder` objects, based on `query`, `range` and `sort` parameters	List[Responder]
`find_one_by(query,**kwargs)`	Returns the first `Responder` object, based on `query` and `sort` parameters	Responder
`get_by_id(worker_id)`	Returns a `Responder` by its `id`	Responder
`get_by_name(name)`	Returns a `Responder` by its `name`	Responder
`get_by_type(data_type)`	Returns a list of available `Responder` applicable to the given `data_type`	List[Responder]
`enable(responder_name,config)`	Activate an responder and returns its `Responder` object	Responder
`update(worker_id)`	Update the configuration of an `Responder` and returns the updated version	Responder
`disable(worker_id)`	Removes a responder from an organization and returns `true` if it completes successfully	Boolean
`run_by_id(worker_id, data,**kwargs)`	Returns a `Job` by its `name`	Job
`run_by_name(responder_name, data,**kwargs)`	Runs a responder by its name and returns the resulting `Job`	Job
`definitions()`	Returns the list of all the responder definitions including the enabled and disabled responders	List[ResponderDefinition]

Examples

The following example shows how to manipulate responders:

import json

from cortex4py.api import Api
from cortex4py.query import *

api = Api('http://CORTEX_APP_URL:9001', '**API_KEY**')

# Get enabled responders
responders = api.responders.find_all({}, range='all')

# Display enabled responders' names
for responder in responders:
  print('Responder {} is enabled'.format(responder.name))

# Get enabled responders that available for TheHive cases
case_responders = api.responders.get_by_type('thehive:case')

# Display responders details
for responder in case_responders:  
  print(json.dumps(responder.json(), indent=2))

# Enable the responder called Test_1_0
responder = api.responders.enable('Test_1_0', {
  "configuration": {
    "api_key": "XXXXXXXXXXXXXx",
    "proxy_http": "http://localhost:9999",
    "proxy_https": "http://localhost:9999",    
    "check_tlp": True,
    "max_tlp": 2,
    "max_pap": 2
  },  
  "jobTimeout": 30,
  "rate": 1000,
  "rateUnit": "Day"
})

# Print the details of the enaled responder
print(json.dumps(responder.json(), indent=2))
print(responder.workerDefinitionId == 'Test_1_0')

# Run a responder
job = api.responders.run_by_name('File_Info_2_0', {
    'data': {
      'title': 'Sample case',
      'description': 'This is a sample case',
      ...
    },
    'dataType': 'thehive:case',
    'tlp': 1
})
print(json.dumps(job.json(), indent=2))

# Disable a responder
api.responders.disable(responder.id)

Job Operations

The JobsController class provides a set of methods to handle jobs. A job is the execution of a specific worker (analyzer or responder).

Model

A job is represented by the following model class:

Attribute	Description	Type
`id`	Job ID	computed
`type`	Job type: `responder` or `analyzer`	computed
`workerDefinitionId`	Worker definition name	readonly
`workerId`	Instance ID of the worker to which the job is associated	readonly
`workerName`	Name of the worker to which the job is associated	readonly
`organization`	Organization to which the user belongs (set upon account creation)	readonly
`dataType`	the datatype of the worker's input data	readonly
`status`	Status of the job (`Waiting`, `InProgress`, `Success`, `Failure`, `Deleted`)	computed
`data`	Value of the worker's input (does not apply to `file` observables). Contains all the data of a `Case` if the job is a result of a case responder.	readonly
`attachment`	JSON object representing `file` observables (does not apply to non-`file` observables). It defines the`name`, `hashes`, `size`, `contentType` and `id` of the `file` observable	readonly
`parameters`	JSON object of key/value pairs set during job creation	readonly
`message`	A free text field to set additional text/context for a job	readonly
`tlp`	The TLP of the analyzed observable	readonly
`report`	The analysis report as a JSON object including `success`, `full`, `summary` and `artifacts` peoperties. In case of failure, the resport contains a `errorMessage` property	readonly
`startDate`	Start date	computed
`endDate`	End date	computed
`createdAt`	Creation date. Please note that a job can be requested but not immediately honored. The actual time at which it is started is the value of `startDate`	computed
`createdBy`	User who created the job	computed
`updatedAt`	Last update date (only Cortex updates a job when it finishes)	computed
`updatedBy`	User who submitted the job and which identity is used by Cortex to update the job once it is finished	computed

A JobArtifact is represented by the following model class:

Attribute	Description	Type
`id`	Artifact ID	computed
`dataType`	Artifact data type	readonly
`data`	Artifact value	readonly
`createdAt`	Creation date.	computed
`createdBy`	User who created the job that generated the artifact	computed

Methods

Method	Description	Return type
`find_all(query,**kwargs)`	Returns a list of `Job` objects, based on `query`, `range` and `sort` parameters	List[Job]
`find_one_by(query,**kwargs)`	Returns the first `Job` object, based on `query` and `sort` parameters	Job
`get_by_id(job_id)`	Returns a `Job` by its `id`	Job
`get_report(job_id)`	Returns synchronously the `Job` object including its analysis report even if the job is still running	Job
`get_report_async(job_id)`	Waits and returns the `Job` object including its analysis report	Job
`get_artifacts(job_id)`	Returns a list of the observables that have been extracted from the analysis report	List[JobArtifact]
`delete(job_id)`	Requires `superadmin` role, returns `true` if the delete completes successfully	Boolean

Examples

import json

from cortex4py.api import Api
from cortex4py.query import *

api = Api('http://CORTEX_APP_URL:9001', '**API_KEY**')

# Fetch the last 10 successful jobs that have been executed against domain names
query = And(Eq('status', 'Success'), Eq('dataType', 'domain'))
jobs = api.jobs.find_all(query, range='0-10', sort='-createdAt')

# Display summaries of the jobs above
for job in jobs:
  report = api.jobs.get_report(job.id).report
  print('Job summary is {}'.format(json.dumps(report.get('summary', {}))))

  print('Job {} has generated the following artifacts:'.format(job.id))
  artifacts = api.jobs.get_artifacts(job.id)
  for a in artifacts:
    print('- [{}]: {}'.format(a.dataType, a.data))

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Usage.md

Usage.md

How to Use Cortex4py

Table of Contents

Introduction

Library Architecture

Migration

Proxy and Certificate Verification

Backward Compatibility

Exception Handling

Organization Operations

Model

Methods

Examples

User Operations

Model

Methods

Examples

Analyzer Operations

Model

Methods

Examples

Responder Operations

Model

Methods

Examples

Job Operations

Model

Methods

Examples

Files

Usage.md

Latest commit

History

Usage.md

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How to Use Cortex4py

Table of Contents

Introduction

Library Architecture

Migration

Proxy and Certificate Verification

Backward Compatibility

Exception Handling

Organization Operations

Model

Methods

Examples

User Operations

Model

Methods

Examples

Analyzer Operations

Model

Methods

Examples

Responder Operations

Model

Methods

Examples

Job Operations

Model

Methods

Examples