Reference guide for supported transformation functions and parameters for attribute transformers
This page includes a comprehensive list of all supported transformer functions and parameters. Some commonly used transformation functions include:
Replacing a character with a different one
Removing domains from email addresses
Transforming to upper, lower, camel, or snake case
Using a substring from the original value
See for more information about using attribute transformers to update or create attributes in downstream systems based on changes in your source of identity.
Input:
{firstname | ASCII, "Łukasz Gruba"}
Output:
Lukasz Gruba
Input:
{COUNTRY_CODE_ISO3166, "US", alpha3}
Output:
USA
{start_date | DATE_FORMAT, "01/02/2006"}
Output:
This transformer formats date as MM/DD/YYYY
{FIRST_N, "world", 4}
Output:
worl (This transformer takes the first four characters of the string.)
Usage Example
Input:
{FROM_ENTITY_ATTRIBUTE, "Employee", "ID", "ManagerID"}
Output:
Manager ID (for the employee)
Usage Example
Input:
{LANGUAGE_RFC5646, "Spanish"}
Output:
es
Output:
world
Output:
00123
{LOOKUP, "IL001", "locationTable", "location_code", "city"}
Output:
Chicago
Output:
hello
{LOWER_SNAKE_CASE, "Hello World"}
Output:
hello_world
Output:
"", "2", "3", "4"
Note: This transformer can also be used within a IF/ELSE conditional transformer for intelligent username generation with automatic fallback strategies.
{PHONE_NUMBER_E164, "+1-800-555-1212"}
Output:
+18005551212
{RANDOM_ALPHANUMERIC_GENERATOR, 8}
Output:
a1B2c3D4
Note: This transformer generated a alphanumeric string with eight characters.
{RANDOM_NUMBER_GENERATOR, 4}
Output:
4829
Note: This transformer generated a random numeric string with four characters.
{RANDOM_STRING_GENERATOR, 6}
Output:
uFkLxw
Note: This transformer generated a random alpha string with six characters.
{REMOVE_CHARS, "[email protected]", "@", "."}
Output:
FirstLastexamplecom
{REMOVE_DIACRITICS, "José"}
Output:
Jose
Output:
pennylane
{REMOVE_WHITESPACE, "First Last"}
Output:
FirstLast
{REPLACE_ALL, "hello world", " ", "_"}
Output:
hello_world
{RIGHT_PAD, "123", 5, "0"}
Output:
12300
Output:
first.last
Note: This transformer generated the results where the index starts at zero (0).
Output:
hel
Output:
hello
{TRIM_CHARS, "....first.last----", ".-"}
Output:
first.last
{TRIM_CHARS_LEFT, "....first.last----", ".-"}
Output:
first.last----
{TRIM_CHARS_RIGHT, "....first.last----", ".-"}
Output:
....first.last
{UPPER, "hello"}
Output:
HELLO
Input:
{UPPER_CAMEL_CASE, "hello world"}
Output:
Hello World
Input:
{UPPER_SNAKE_CASE, "hello world"}
Output:
HELLO_WORLD
Input:
{UUID_GENERATOR}
Output:
123e4567-e89b-12d3-a456-426614174000
Integer - month
Integer-year
String (Optional) - Format
Usage Example
Input:
{activation_date | DATE_ADJUST,+1,2,3,-1}
{activation_date | DATE_ADJUST,+1,2,3,-1, "RFC3339"}
{activation_date | DATE_ADJUST,+1,2,3,-1, "2006-01-02T15:04:05Z07:00"}
{activation_date | DATE_ADJUST_DAY,+1}
{activation_date | DATE_ADJUST_DAY,+1,"RFC3339"}
{activation_date | DATE_ADJUST_DAY,+1,"2006-01-02T15:04:05Z07:00"}
{NOW, | "RFC3339"}
{NOW, "RFC3339"}
{NOW, "2006-01-02T15:04:05Z07:00"}
Input:
{activation_date | ASSUME_TIME_ZONE, "America/Los_Angeles"}
{activation_date | ASSUME_TIME_ZONE, "America/Los_Angeles", "RFC3339"}
{activation_date | ASSUME_TIME_ZONE, "-07:00"}
{activation_date | ASSUME_TIME_ZONE, "-07:00", "RFC3339"}
Usage Example
Input:
{activation_date | UTC_TO_TIME_ZONE, "America/Los_Angeles"}
{activation_date | UTC_TO_TIME_ZONE, "America/Los_Angeles", "RFC3339"}
{activation_date | UTC_TO_TIME_ZONE, "-07:00"}
{activation_date | UTC_TO_TIME_ZONE, "-07:00", "RFC3339"}
Configure fallback formatters for uniquely identifying attributes during identity synchronization
Fallback formatters can help resolve conflicts when provisioning identities with unique attributes. This is particularly useful when automated provisioning requires unique identifiers, but the standard generated values are already in use.
When provisioning new identities through Lifecycle Management, unique attributes like usernames, login IDs, or email addresses must not conflict with existing values. Fallback formatters provide an automated way to generate alternative values when conflicts arise, ensuring provisioning can proceed without manual intervention.
You can configure fallback formatters when configuring a to ensure new users can be onboarded efficiently, regardless of naming conflicts.
The most common use case for fallback formatters is handling username conflicts. For example:
Your organization uses a standard username format of first initial + last name (e.g., jsmith for John Smith).
When multiple employees have similar names, this can lead to conflicts:
John Smith already has jsmith
Jane Smith already has jsmith1
James Smith already has jsmith2
When Jennifer Smith joins, the fallback formatter automatically assigns jsmith3, maintaining your naming convention while ensuring uniqueness.
Fallback formatters can be configured as part of the "Sync Identities" action within a Lifecycle Management workflow:
Edit or create a Lifecycle Management policy
Edit the workflow containing the Sync Identities action
In the Sync Identities action configuration, click Add Fallback
Configure the to use as a fallback pattern for the unique attribute that might experience conflicts
Several transformers can be used for implementing fallback formatters depending on your specific use case.
A typical approach is to use the NEXT_NUMBER transformer, which is specifically designed to generate sequential numerical alternatives when naming conflicts occur.
The NEXT_NUMBER transformer:
Generates a set of sequential integers as strings
Takes two parameters: BeginInteger (starting number) and Length (how many numbers to generate)
Is unique among transformers in that it returns multiple values, making it ideal for fallback scenarios
In addition to NEXT_NUMBER, other transformers can be valuable for creating fallback formatters:
Using Random Alphanumeric for Unique Usernames:
This could generate usernames like jsmith8f3d instead of sequential jsmith1, jsmith2, etc.
Using UUID for Guaranteed Uniqueness:
This would append the first 8 characters of a UUID, creating identifiers like jsmith-a7f3e9c2.
When configuring a fallback formatter with the NEXT_NUMBER transformer:
Select the attribute that requires uniqueness (e.g., username, email)
Configure the primary pattern (e.g., {first_initial}{last_name})
Add a fallback using the NEXT_NUMBER transformer to generate sequential alternatives:
This will generate up to 10 alternatives: jsmith1, jsmith2, ... jsmith10
Here are some commonly used fallback patterns:
When Lifecycle Management attempts to provision a new identity with a unique attribute value that already exists:
The system first tries the primary format (e.g., jsmith)
If a conflict is detected, it automatically tries the first alternative using the NEXT_NUMBER transformer (e.g., jsmith1)
If that value also exists, it tries the next alternative (e.g., jsmith2)
This automated conflict resolution ensures provisioning can proceed without manual intervention, even when your standard naming conventions result in conflicts.
Close the action sidebar and save your changes to the policy.
This process continues until either:
A unique value is found
All alternatives from the NEXT_NUMBER range are exhausted (in which case an error would be reported)
{first_initial}{last_name}{RANDOM_ALPHANUMERIC_GENERATOR(4)}{first_initial}{last_name}-{UUID_GENERATOR() | SUB_STRING,0,8}{first_initial}{last_name}{NEXT_NUMBER(1, 10)}{first_initial}{last_name}
{first_initial}{last_name}{NEXT_NUMBER(1, 10)}
jsmith, jsmith1, jsmith2, etc.
{first_name}.{last_name}
{first_name}.{last_name}{NEXT_NUMBER(1, 10)}
john.smith, john.smith1, john.smith2
{username}@domain.com
{username}{NEXT_NUMBER(1, 10)}@domain.com
{first_name}{last_initial}
{first_name}{last_initial}{NEXT_NUMBER(1, 10)}
johns, johns1, johns2
Use lookup tables to transform identity attributes for target systems
You can use Lookup transformers to convert identity attributes from a source system into appropriate values for target systems based on CSV reference tables. This is particularly useful when mapping values between systems that use different naming conventions, codes, or formats for the same conceptual data.
For example, you might need to transform a "Location" attribute from Workday (which might be stored as location codes like "MN001") into corresponding values for country, country code, or city names in a target system.
Use Table Lookup Transformers when:
You need to map source attribute values to different values in target systems
You have standardized reference data that must be consistent across applications
You need to extract different pieces of information from a single attribute value
You have complex mapping requirements that built-in transformers cannot support
Geographic Information:
Transform location codes to country, region, city, or timezone information
Map office codes to physical addresses or facility types
Organizational Mapping:
The Table Lookup Transformer references CSV-based mappings between source and destination values. When synchronizing user attributes, Veza:
Takes the source attribute value
Looks up this value in the specified lookup table
Returns the corresponding value from the designated return column
Applies this value to the target attribute
Lookup tables are CSV files with columns that map values from a source of identity to destination values. Each row represents a mapping entry. The first row must contain the column headers.
For example, a location mapping table might look like:
To create a new lookup table:
Navigate to the Lookup Tables tab within your policy configuration
Click Edit mode to enable policy changes
Click Add New to create a new lookup table
Provide a Name and optional Description
From the Lookup Tables tab, you can:
Edit table descriptions or upload a new CSV
Delete tables that are no longer needed
To use a Table Lookup Transformer in a common or action-synced attribute:
In Destination Attribute, choose the attribute on the target entity that will be updated
In Formatter, choose the source attribute to transform
In Pipeline Functions, specify the lookup table name, the column to match against, and the column containing values to return.
The full syntax for using lookup table transformers is:
Where:
<value> is the source attribute to transform (e.g., {location})
<table_name> is the name of the lookup table to use
<column_name> is the column in the table to match against
Assuming a user has "location": "IL001" and a lookup table named locationTable structured as shown earlier:
You can combine lookup transformations with other transformation functions in a pipeline:
This would look up the state_code corresponding to the location value and convert it to lowercase.
When a lookup value is not found in the table, the transformation will fail for that specific attribute.
For full coverage, ensure your lookup table includes entries for all possible source values that may be encountered during provisioning.
To ensure robust provisioning workflows, it's important to include all expected values in your lookup table, validate source data before implementing lookup transformations, and test transformations with representative data sets.
Lookup tables are immutable and automatically deleted when no longer referenced by any policy version
Multiple policy versions can reference the same lookup table (e.g., an active version and a draft version)
Lookup tables are defined at the policy level and can be referenced by any transformer within the policy
Lookup tables can have multiple columns to support different transformations from the same reference data
Standardize Naming: To use a lookup-based transformer, you will reference the table by file name. Apply consistent conventions for both the table and columns.
Document Mappings: Add descriptions for each lookup table to explain its purpose
Validate Data: Ensure lookup tables are complete and accurate before using them in transformers. Consider how lookup tables will be maintained over time, especially for values expected to change.
Convert department codes to department names or business units
Map cost centers to budget codes or accounting categories
System-Specific Configurations:
Transform job titles to role designations in target systems
Convert skill codes to certification requirements or training needs
Drag a CSV file or click Browse to upload your reference data
Review the automatically detected column names
Click Save to store the lookup table
<return_column_name> is the column containing the value to return
location_code,state_code,state,city
MN001,MN,Minnesota,Minneapolis
CA001,CA,California,Los Angeles
TX001,TX,Texas,Houston
TX002,TX,Texas,Austin{<value> | LOOKUP <table_name>, <column_name>, <return_column_name>}{location} | LOOKUP locationTable, location_code, city
"Chicago"
{location} | LOOKUP locationTable, location_code, state
"Illinois"
{location} | LOOKUP locationTable, location_code, state_code
"IL"
{location | LOOKUP locationTable, location_code, state_code | LOWER}Value not found in lookup table
Add the missing mapping to the lookup table with the correct source value
Incorrect column name referenced
Check the column names in your lookup table (they are case-sensitive)
Unexpected transformation results
Verify the lookup table content and ensure the correct columns are specified
Configure how user attributes from a source of identity are transformed and synchronized for target user accounts
When creating workflows in Lifecycle Management policies to create, sync, or deprovision identities, you will use attribute transformers to specify how user attributes for target accounts should be structured. The target attributes to create or update are typically mapped and optionally transformed from user metadata from the source of identity, such as an identity provider, HR system, or CSV upload. Attributes can be synchronized once or kept in continuous sync as changes occur over the user’s employment lifecycle.
For example, attribute mapping and transformation can be used across Joiner, Mover, and Leaver scenarios:
Joiner: Set new Azure AD User Principal Name to {source username}@your-email-domain.com. This is an example of mapping multiple attributes and performing a transformation. More specifically, you use the attribute transformer to generate an email address for new joiners. Use the source username (user_principal_name) from the source of identity (Azure AD UPN) for the first part (user attribute), while your-email-domain.com is used for the last part (target attribute).
Mover: Always update a user’s “Manager” and “Department” attributes in Okta to match the user’s manager and department in Workday, a source of identity, whenever a department change or other employee mobility event occurs. This is an example of attribute mapping with continuous synchronization.
Leaver: Move a user’s Active Directory account to an Organizational Unit (OU) reserved for terminated accounts.
When synchronizing a user’s attributes, Veza may apply many transformations to convert the source attribute values into a more suitable format intended for the target application as a user account attribute.
For example, a transformer might remove the domain from an email address, replace special characters, or convert a string with uppercase letters to lowercase letters.
See the following sections for more information about formatting destination attributes and possible transformations:
Continuous Sync ensures that identity attributes in target systems remain up-to-date with the corresponding attributes residing at the source of truth. It has three configuration levels that work together to determine how attributes are synchronized:
Workflow Level
The workflow's continuous sync setting controls change detection:
When enabled: The workflow monitors for any changes in the source system
When disabled: The workflow only runs during initial provisioning
Action Level
For Sync Identity actions, this controls whether existing entities can be updated:
When enabled: The action can update existing entities
When disabled: The action only sets attributes during initial creation
Attribute Level
Individual attributes can be configured for continuous sync:
When enabled: The attribute will be updated when changes are detected
When disabled: The attribute is only set during initial creation
You may not want to enable continuous sync at the Attribute level when there is a change in the user principal name, such as a change in marital status or legal name correction.
All three levels must be enabled for an attribute to be continuously synchronized. For example, if you want to keep an employee's department updated:
Enable continuous sync on the workflow to monitor for changes
Enable continuous sync on the sync action to allow updates
Enable continuous sync on the department attribute transformer
Recommended Settings
Enable continuous sync for attributes that change during employment:
First Name Surname
Department
Title
Manager
Disable continuous sync for stable identifiers:
Active Directory sAMAccountName
Email Addresses (for Email Write-Back action)
This configuration ensures that dynamic attributes stay updated while preserving stable identifiers.
Common transformers define one or more rules to apply when synchronizing the attributes of a target identity. Use them at the Policy level where you want to create or update attributes using the same conventions across multiple sync or deprovision actions. When you need to configure a one-time individual action in a workflow, such as a specific attribute, then you use the transformer at the Action level.
At the Policy level, you configure a transformer with basic details, including how to source the value of each attribute:
Assign a name and description to the transformer, and specify the data source to which it applies.
Entity Type: Choose the target entity type in the destination system.
Click Add Attribute. The Destination Attribute dropdown will list available attributes for the chosen entity type.
After creating a common transformer, you can select it when editing a workflow action. You can edit or delete common transformers on the Edit Policy > Common Transformers tab. Remember that “Sync Identity” and “De-Provision Identity” actions can have action-level transformers override common transformers. If the same destination attribute is defined in both, the action-level transformer will take precedence.
Formatters specify the actual value of the attribute to synchronize. The target attribute can be set to a specific value, synchronized with a source attribute, transformed using a function, or a combination of these.
To create a destination attribute with a fixed value, enter the desired value when configuring the formatter. For setting the creator attribute:
For activating a re-hired employee:
To set empty values (common for de-provisioning flows):
Target attributes can be updated based on attributes belonging to the source of identity. To reference the value of a source entity attribute, use the format {\<source\_attribute\_name>}.
Examples:
Based on the user metadata available from your source of identity (SOI), you may need to convert a full email address to a valid username, standardize a date, or generate a unique identifier for users provisioned by Veza. Suppose an attribute value needs to be altered for compatibility with the target system. In that case, you can transform the value of a source attribute or apply a range of other functions to generate the target value.
Formatter expressions use the following syntax: {\<source\_attribute\_name> | \<FUNCTION\_NAME>,\<param1>,\<param2>}
For example:
Refer to the page for a comprehensive list of all supported functions and parameters. Contact Veza if you require additional transformations for your use case. Some commonly used transformation functions include:
Replacing a character with a different one
Removing domains from email addresses
Transforming to upper, lower, camel, or snake case
Using a substring from the original value
The ASCII transformer is particularly useful when working with localized user data and legacy systems that have strict character set limitations (such as Active Directory sAMAccountName restrictions). This transformer performs two primary operations:
Removes all non-printable ASCII characters (including control codes, zero-width spaces, tabs, and newlines)
Converts non-ASCII characters to their closest ASCII equivalents
Whereas the REMOVE_DIACRITICS transformer only removes accent marks while preserving the basic character, the ASCII transformer performs a more comprehensive conversion, replacing characters like “Ł” with “L” and handling a wider range of non-ASCII characters, including the following:
Accented Letters: Letters with diacritics like é, à, ö, ñ, ç, etc., commonly used in languages like French, Spanish, German, and Portuguese. Non-Latin Alphabets: Characters from various alphabets such as Cyrillic (e.g., ж, б), Greek (e.g., α, β), Arabic (e.g., ح, ص), Hebrew, and Chinese (e.g., 汉, 字). Mathematical and Technical Symbols: Symbols like infinity (∞), integral (∫), summation (∑), Ohm (Ω), degree (°). Currency Symbols: Symbols like €, £, ¥. Emoji: Various emoticons like 😊, 😞, etc. Other Symbols: Symbols like ©, ®, ™, etc.
The DATE_FORMAT transformer formats date strings using Go time package layout syntax. This transformer helps convert between different date formats, such as transforming dates for LDAP integration or standardizing date formats across systems.
Go time layouts use a specific reference time: Monday, January 2, 15:04:05 MST 2006, which is Unix time 1136239445. All layout strings must use the exact digits and format from this reference time. For more information about Go time layouts, refer to the . Date Components:
2006 = 4-digit year
06 = 2-digit year
01 = 2-digit month (01-12)
1 = 1-digit month (1-12)
Time Components:
15 = 24-hour format hour (00-23)
03 = 12-hour format hour (01-12)
3 = 12-hour format hour without leading zero (1-12)
04 = minute (00-59)
Weekday Components:
Mon = 3-letter weekday abbreviation
Monday = full weekday name
Time Zone Components:
MST = time zone abbreviation
Z0700 = RFC3339 time zone format
Z07:00 = RFC3339 time zone format with colon
RFC Components:
RFC822 = “02 Jan 06 15:04 MST”
RFC822Z = “02 Jan 06 15:04 -0700” // RFC822 with numeric zone
RFC850 = “Monday, 02-Jan-06 15:04:05 MST”
RFC1123 = “Mon, 02 Jan 2006 15:04:05 MST”
Other Constant Components:
Layout = “01/02 03:04:05PM '06 -0700” // The reference time, in numerical order.
ANSIC = “Mon Jan _2 15:04:05 2006”
UnixDate = “Mon Jan _2 15:04:05 MST 2006”
RubyDate = “Mon Jan 02 15:04:05 -0700 2006”
Basic Date Formatting:
Formats any recognized date input into MM/DD/YYYY format.
Converting Between Specific Formats:
Parses input in MM/DD/YYYY format and outputs in YYYY-MM-DD format.
LDAP Integration Example: The DATE_FORMAT transformer was specifically enhanced to support LDAP Z and Win32 time format requirements.
The Z time format (20060102150405Z) is commonly used in LDAP directories and represents timestamps in UTC with a ‘Z’ suffix indicating zero UTC offset.
Converts a date to LDAP Z time format for directory integration. Example for LDAP account expiration:
Human-Readable Format:
Outputs a full, human-readable date format. Time Zone Handling:
Includes time zone information in the output.
Additionally, the DATE_FORMAT transformer supports LDAP time format requirements using the format Win32 in LDAP directories:
Converts a date to LDAP time format for directory integration.
Input Format: When the second parameter (input layout) is omitted, the transformer attempts to parse the input using common date formats automatically
Case Sensitivity: Layout components are case-sensitive (e.g., PM vs pm)
Leading Zeros: Use 01, 02, etc. for zero-padded values, and 1, 2, etc. for non-padded values
The DATE_FORMAT transformer is used for formatting time using a time string to store time values in Generalized-Time format, which is required for Active Directory and other Microsoft-based target applications.
The Generalized-Time syntax is “YYYYMMDDHHMMSS.0Z”. Time String Format Example:
The “Z” indicates no time differential. Active Directory stores date/time as Greenwich Mean Time (GMT). If no time differential is specified, GMT is the default.
If the time is specified in a time zone other than GMT, the differential between the time zone and GMT is appended to the string instead of “Z” in the form “YYYYMMDDHHMMSS.0[+/-]HHMM”. Time Zone Format Example:
The differential is based on the formula: GMT = Local + Differential.
These transformers enable the cleaning, formatting, and standardization of string data from your source systems.
The REMOVE_CHARS transformer removes all instances of specified characters from a string. This is useful for cleaning up data by removing unwanted punctuation, special characters, or formatting elements. Use Cases:
User ID creation: {email | REMOVE_CHARS, “-”}
If email is ", the result is “johndoe@examplecom”
Phone number formatting: {phone_number | REMOVE_CHARS, "()- "}
The REMOVE_WHITESPACE transformer removes all whitespace characters (spaces, tabs, newlines) from a string. It can help create compact identifiers or ensure data consistency. Use Cases:
Username generation: {display_name | REMOVE_WHITESPACE}
If display_name is “John A. Doe”, the result is “JohnA.Doe”
Tag creation: {department | REMOVE_WHITESPACE | LOWER}
These transformers help clean up strings by removing unwanted characters from the beginning and/or end of strings. This is essential for data hygiene and ensuring consistent formatting. TRIM: Removes leading and trailing whitespace
Basic cleanup: {display_name | TRIM}
If display_name is " John Doe ", the result is “John Doe”
TRIM_CHARS: Removes specified characters from both ends
Cleaning employee IDs: {employee_id | TRIM_CHARS, “0.”}
If employee_id is “000.123.000”, the result is “123”
Removing padding characters: {code | TRIM_CHARS, “-_”}
TRIM_CHARS_LEFT: Removes specified characters from the beginning only
Removing leading zeros: {cost_center | TRIM_CHARS_LEFT, “0”}
If cost_center is “00012345”, the result is “12345”
Cleaning prefixes: {identifier | TRIM_CHARS_LEFT, “x”}
TRIM_CHARS_RIGHT: Removes specified characters from the end only
Removing trailing characters: {office_code | TRIM_CHARS_RIGHT, “0”}
If office_code is “ABC12300”, the result is “ABC123”
Cleaning suffixes: {code | TRIM_CHARS_RIGHT, “temp”}
The NEXT_NUMBER transformer can be combined with IF/ELSE conditional logic to create intelligent username generation with automatic fallback strategies. This is particularly useful for handling length constraints and ensuring unique usernames in Lifecycle Management attribute transformers.
Only one NEXT_NUMBER transformer can be used per transformation expression
The first alternative uses an empty string (""), followed by numbered alternatives (“2”, “3”, etc.)
Alternative values are automatically generated to ensure username uniqueness
This example creates usernames that adapt based on length constraints, using the sys_attr__would_be_value_len system attribute to evaluate the length of the generated value:
Example outputs: For a user named “John Whitaker” (short enough for the first condition):
Base value: john.whitaker
Alternatives: john.whitaker2, john.whitaker3, john.whitaker4
For a user named “Leonevenkataramanathan Foster” (requires truncation to meet length limits):
Base value: l.f
Alternatives: l.f2, l.f3, l.f4
This approach ensures that username generation adapts to different name lengths while maintaining consistency and uniqueness across your identity management system.
You can pipeline multiple transformation functions together, separated by a vertical bar (|). Each will apply in sequence, allowing for complex attribute formatters that use the output of one function as the input of another.
{name | UPPER}
If name = Smith, the result is SMITH.
{first\_name | SUB\_STRING,0,1 | LOWER}.{last\_name | LOWER}
As part of implementing Lifecycle Management (LCM) processes with Veza, you should create sets of common transformers to define how values such as username, login, or ID are sourced for each LCM Policy. These transformers can then be reused across all identity sync and deprovision policy workflows. As part of implementing Lifecycle Management (LCM) processes with Veza, you should create sets of common transformers to define how values such as username, login, or ID are sourced for each LCM Policy. These transformers can then be reused across all identity sync and deprovision policy workflows.
For example, defining a common synced attribute to describe how to format Azure AD account names {username}@evergreentrucks.com enables reuse across multiple workflow actions. You can also define synced attributes at the action level when they are used only once within a policy, such as setting the primary group DN and OU of de-provisioned identities to a group reserved for terminated accounts.
Common Transformer Examples:
The $Target attribute transformer function is used when a value consists of one or more attributes that require an operation(s), making it too complex to transform, but it needs to be reused.
Important: The $Target function can only be used within the same Action.
For example, an email address consists of [email protected]. However, you are required to use the format, . By using the $Target function, you reuse only one attribute, username, while not changing the other two attributes (firstname_lastname).
Example:
Destination Attribute
Formatter
Formatter
The Custom Attribute Transformer function allows you to define a custom transformer that acts as an alias for applying one or more transformer functions.
For example, you can define a custom function named $CLEAN, which is used as {first_name | $CLEAN}. This function can consist of a series of transformer functions such as | ASCII | LOWER | REMOVE_CHAR |.
To define a custom attribute transformer, use the following guidelines:
Policy Version Definitions
Custom functions must be defined as part of the policy version.
These definitions are structured similarly to hard-coded definitions and are returned in the same format, allowing the Veza UI to handle them without modification.
The API for updating and retrieving a policy version must also support these custom function definitions.
Custom Attribute Transformer Limitations
The following custom definitions are not supported:
Transformer functions with included transformer parameters
Nested transformer functions
Transformer functions with parameters
AD Distinguished Name (DN)
AD User Principal Name (UPN)
AD Email
Formatter: Choose how the destination attribute should be formatted. Specify the value, a {source\_attribute}, or apply Transformation Functions.
Pipeline Functions: Combines a series of attribute formatters with the pipe ( | ) character that runs the value of an attribute in sequential order, where the output of one formatter becomes the input of the following formatter, thus the name, pipeline.
See Pipeline Functions for more examples.
Continuous Sync: Enabling this option ensures that the attribute is always synced, while applying any defined transformations. By default, attributes will not be synced if the target identity already exists.
Jan = 3-letter month abbreviation
January = full month name
02 = 2-digit day (01-31)
2 = 1-digit day (1-31)
_2 = space-padded day
DateTime = “2006-01-02 15:04:05”
DateOnly = “2006-01-02”
4 = minute without leading zero (0-59)
05 = second (00-59)
5 = second without leading zero (0-59)
PM = AM/PM indicator
pm = am/pm indicator (lowercase)
TimeOnly = “15:04:05”
RFC1123Z = “Mon, 02 Jan 2006 15:04:05 -0700” // RFC1123 with numeric zone
RFC3339 = “2006-01-02T15:04:05Z07:00”
RFC3339Nano = “2006-01-02T15:04:05.999999999Z07:00”
Kitchen = “3:04PM” // Handy time stamps.
Stamp = “Jan _2 15:04:05”
StampMilli = “Jan _2 15:04:05.000”
StampMicro = “Jan _2 15:04:05.000000”
StampNano = “Jan _2 15:04:05.000000000”
Monday, January 2, 2006
Full date
Wednesday, March 15, 2023
2006-01-02 15:04:05
Full timestamp
2023-03-15 14:30:25
03:04:05 PM
12-hour time
02:30:25 PM
15:04
24-hour time
14:30
20060102150405Z
LDAP Z time format
20230315143025Z
Cleaning account names: {account_name | REMOVE_CHARS, “!@#$%”}
Removes special characters that might cause issues in target systems
Creating system identifiers: {cost_center | REMOVE_WHITESPACE}
Ensures cost center codes have no embedded spaces
If first_name = John and last_name = Smith, the result is j.smith.
{email | REMOVE\_DOMAIN}
If email = [email protected], the result is john.smith.
{email | REPLACE\_ALL, " ", "."}
If email = john [email protected], the result is [email protected].
{location | LOOKUP locationTable, location\_code, city}
If location = IL001, the result is Chicago (using a lookup table named locationTable).
{start\_date | DATE\_FORMAT, "01/02/2006" | UPPER}
If start_date = 2023-03-15, the result is 03/15/2023 (DATE_FORMAT doesn't typically need UPPER, but shows pipeline capability).
{hire\_date | DATE\_FORMAT, "Jan 2, 2006" | REPLACE\_ALL, " ", "\_"}
If hire_date = 2023-03-15, the result is Mar_15,_2023.
{office\_code | TRIM\_CHARS\_LEFT, ".0" | TRIM\_CHARS\_RIGHT, ".USCA"}
If office_code = 000.8675309.USCA, the result is 8675309.
{username | REMOVE\_CHARS, ".-\_" | TRIM | UPPER}
If username = "–john.doe_–", the result is JOHNDOE.
{employee\_id | REMOVE\_CHARS, "#" | TRIM\_CHARS, "0" | LEFT\_PAD, 6, "0"}
If employee_id = "##001234##", the result is 001234.
{department | REMOVE\_WHITESPACE | LOWER | REPLACE\_ALL, "&", "and"}
If department = "Sales & Marketing", the result is salesandmarketing.
user_principal_name
{first_name | SUB_STRING,0,1 | LOWER}.{last_name | LOWER}
SUB_STRING,0,1
LOWER}.{last_name
{first_name}{last_name}@company.com
Yes
Email address
OktaAccountTransformer OktaUser
login
{first_name | SUB_STRING,0,1 | LOWER}.{last_name | LOWER}
SUB_STRING,0,1
LOWER}{last_name
{first_name}{last_name}@company.com
Yes
Email address
username_prefix
{first_name | SUB_STRING,0,1 | LOWER}.{last_name | LOWER}
SUB_STRING,0,1
LOWER}{last_name
AzureADTransformer AzureADUser
principal_name
{first_name}{last_name}
No
Primary identifier
mail_nickname
{first_name | SUB_STRING,0,1 | LOWER}{last_name | LOWER}
SUB_STRING,0,1
LOWER}{last_name
display_name
{first_name} {last_name}
Yes
Display name
GoogleAccountTransformer GoogleWorkspaceUser
{first_name}{last_name}@company.com
No
Primary email
email_addresses
{username}@company.com
No
Email list
recovery_email
{personal_email}
Yes
Backup email
ContractorTransformer ActiveDirectoryUser
account_name
c-{username}
No
Contractor prefix
distinguished_name
CN={first_name} {last_name},OU=Contractors,OU={department},DC=company,DC=local
Yes
Contractor OU
description
Contractor - {vendor_company} - Start Date: {start_date}
Yes
Metadata
RegionalEmailTransformer ExchangeUser
email_address
{username}@{region}.company.com
No
Regional email
alias
{first_name}.{last_name}@{region}.company.com
Yes
Regional alias
created_by
“Veza”
Disabled
isActive
true
Enabled
manager_id
" "
Enabled
isActive
false
Enabled
first_name
{first\_name}
Enabled
last_name
{last\_name}
Enabled
{first_name}.{last_name}@domain.com {first_name}_{last_name}@domain.com {last_name}@domain.com {firstname_initial}{last_name}@domain.com {firstname_initial}-{last_name}@domain.com {firstname_initial}{middlename_initial}{last_name}@domain.com {firstname_initial}-{last_name}[email protected] {last_name}-{firstname_initial}@domain.com
-
username
`{email | REMOVE_DOMAIN}`
Removes the domain from the email to create username
"jsmith" is the output derived from [email protected]
user_id
`f{id | UPPER}`
Converts ID to uppercase
JSMITH" is the output derived from the userid, "jsmith"
01/02/2006
MM/DD/YYYY
03/15/2023
2006-01-02
YYYY-MM-DD
2023-03-15
02-Jan-2006
DD-MMM-YYYY
15-Mar-2023
Jan 2, 2006
MMM D, YYYY
`{start_date | DATE_FORMAT, "01/02/2006"}``{hire_date | DATE_FORMAT, "2006-01-02", "01/02/2006"}``{timestamp | DATE_FORMAT, "20060102150405Z"}``{account_expires | DATE_FORMAT, "20060102150405Z"}``{event_date | DATE_FORMAT, "Monday, January 2, 2006"}`{utc_time | DATE_FORMAT, "2006-01-02 15:04:05 MST"}{timestamp | DATE_FORMAT, "Win32"}{date | DATE_FORMAT, "20010928060000.0Z"}{date | DATE_FORMAT, "20010928060000.0+0200"}IF sys_attr__would_be_value_len le 20
{first_name | LOWER}.{last_name | LOWER | NEXT_NUMBER, 2, 3}
ELSE IF sys_attr__would_be_value_len le 30
{first_name | LOWER}.{last_name | LOWER | FIRST_N, 1 | NEXT_NUMBER, 2, 3}
ELSE
{first_name | LOWER | FIRST_N, 1}.{last_name | LOWER | FIRST_N, 1 | NEXT_NUMBER, 2, 3}ADAccountTransformer ActiveDirectoryUser
account_name
{display_full_name}
No
Basic account name
distinguished_name
CN={first_name} {last_name},OU={department},OU={location},DC=company,DC=local
Yes
Full AD path
username`{firstname}{lastname}``{$Target.username}@sample.com`Mar 15, 2023