policy_match_rules¶

The following methods allow for interaction with the Zscaler AI Guard Policy Match Rules API endpoints. Includes listing, creating, updating, and deleting policy match rules, and retrieving a rule by ID or name.

Methods are accessible via aiguard.policy_match_rules

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class PolicyMatchRulesAPI¶

Bases: APIClient

A Client object for the AI Guard Policy Match Rules resource.

add_rule(**kwargs)¶

Creates a new policy match rule.

Parameters:

name (str) – The name of the policy match rule.
**kwargs – Optional keyword args.

Keyword Arguments:

policy_id (str) – The policy id for this policy match rule.
enabled (bool) – Indicates whether the policy match rule is enabled.
rule_order (str) – The rule order for this policy match rule.
version (str) – The version for this policy match rule.
match_criteria (str) – The match criteria for this policy match rule.

Returns:

A tuple containing the newly added PolicyMatchRules instance, response, and error.

Return type:

tuple

Examples

Add a new policy match rule. policyId, applicationId and applicationCredentialsIds must reference existing resources – create the detection policy, the LLM application and the application credential first and reuse the ids returned by those calls:

>>> added_rule, _, error = client.aiguard.policy_match_rules.add_rule(
...     policyId=2916,
...     name="PolicyRule01",
...     enabled=True,
...     ruleOrder=2,
...     matchCriteria={
...         "llmApplications": [
...             {
...                 "applicationId": 647,
...                 "applicationCredentialsIds": [1075],
...             }
...         ],
...         "type": "DAS_APPLICATION",
...     },
... )
>>> if error:
...     print(f"Error adding policy match rule: {error}")
...     return
... print(f"Policy match rule added successfully: {added_rule.as_dict()}")

delete_rule(rule_id)¶

Deletes the specified policy match rule.

Parameters:: rule_id (int) – The unique identifier for the policy match rule.
Returns:: A tuple containing the response object and error (if any).
Return type:: tuple

Examples

Delete a policy match rule:

>>> _, _, error = client.aiguard.policy_match_rules.delete_rule(1013)
>>> if error:
...     print(f"Error deleting policy match rule: {error}")
...     return
... print(f"Policy match rule deleted successfully.")

get_rule(rule_id)¶

Fetches a specific policy match rule by ID.

Parameters:: rule_id (int) – The unique identifier for the policy match rule.
Returns:: A tuple containing (PolicyMatchRules instance, Response, error).
Return type:: tuple

Examples

Print a specific policy match rule:

>>> fetched_rule, _, error = client.aiguard.policy_match_rules.get_rule(1013)
>>> if error:
...     print(f"Error fetching policy match rule by ID: {error}")
...     return
... print(f"Fetched policy match rule by ID: {fetched_rule.as_dict()}")

get_rule_by_name(name)¶

Fetches a specific policy match rule by name.

Parameters:: name (str) – The name of the policy match rule.
Returns:: A tuple containing (PolicyMatchRules instance, Response, error).
Return type:: tuple

Examples

Print a specific policy match rule by name:

>>> fetched_rule, _, error = client.aiguard.policy_match_rules.get_rule_by_name('Rule01')
>>> if error:
...     print(f"Error fetching policy match rule by name: {error}")
...     return
... print(f"Fetched policy match rule by name: {fetched_rule.as_dict()}")

list_rules(query_params=None)¶

Lists the policy match rules configured in your organization.

Parameters:: {dict} (query_params) – Map of query parameters for the request.
Returns:: A tuple containing (list of PolicyMatchRules instances, Response, error)
Return type:: tuple

Examples

List policy match rules:

>>> rule_list, _, error = client.aiguard.policy_match_rules.list_rules()
>>> if error:
...     print(f"Error listing policy match rules: {error}")
...     return
... print(f"Total policy match rules found: {len(rule_list)}")
... for rule in rule_list:
...     print(rule.as_dict())

Client-side filtering with JMESPath:

The response object supports client-side filtering and projection via resp.search(expression). See the JMESPath documentation for expression syntax.

update_rule(rule_id, **kwargs)¶

Updates information for the specified policy match rule.

Parameters:

rule_id (int) – The unique identifier for the policy match rule.

Keyword Arguments:

name (str) – The name of the policy match rule.
policy_id (str) – The policy id for this policy match rule.
enabled (bool) – Indicates whether the policy match rule is enabled.
rule_order (str) – The rule order for this policy match rule.
version (str) – The version for this policy match rule.
match_criteria (str) – The match criteria for this policy match rule.

Returns:

A tuple containing the updated PolicyMatchRules instance, response, and error.

Return type:

tuple

Examples

Update an existing policy match rule. The update replaces the rule, so the match criteria are sent in full:

>>> updated_rule, _, error = client.aiguard.policy_match_rules.update_rule(
...     rule_id=1013,
...     policyId=2916,
...     name="PolicyRule01_Updated",
...     enabled=True,
...     ruleOrder=2,
...     matchCriteria={
...         "llmApplications": [
...             {
...                 "applicationId": 647,
...                 "applicationCredentialsIds": [1075],
...             }
...         ],
...         "type": "DAS_APPLICATION",
...     },
... )
>>> if error:
...     print(f"Error updating policy match rule: {error}")
...     return
... print(f"Policy match rule updated successfully: {updated_rule.as_dict()}")