Product

Yasas DevX Suite

Objective

Yasas DevX Suite introduces an AI-powered SailPoint Rule Generator designed to translate simple natural language prompts directly into production-ready rules.

However, merely generating raw XML or BeanShell code is not enough of a solution. The true innovation lies in solving a much bigger challenge: How do you ensure AI-generated rules behave exactly like rules built manually in the UI?. Yasas DevX Suite bridges this gap, ensuring that AI-generated rules can be seamlessly saved, edited, and reopened within the Rule Builder without ever breaking

Details

Yasas DevX Suite Approach

Instead of just spitting out a block of XML code, Yasas DevX Suite takes a comprehensive, multi-layered approach to rule generation.

To ensure the AI-generated rule is structurally identical to a manually created one, the system generates five distinct components:

• XML Rule Code
• Data Elements JSON
• Flow Conditions JSON
• Custom Functions JSON
• Metadata for Reconstruction

Here is a step-by-step breakdown of how the workflow operates:

Natural Language Input & Intent Extraction

The process begins when a user enters a plain-text prompt, such as: “Generate a correlation rule using email, convert to lowercase, trim spaces, and map to identity attribute employeeNumber”.

This prompt is sent to an n8n workflow where an Intent Extraction Agent (the first LLM node) parses the natural language into a strictly enforced, structured JSON intent to guarantee predictable parsing.

Smart Validation

Before generating any code, the workflow validates whether all mandatory fields (like the Source Attribute and Identity Attribute) are present.

If critical information is missing, the system intelligently returns a clarification response asking the user for the missing details, effectively preventing invalid rule generation attempts

Building Internal Rule Plan

Once validated, the system builds the core internal structures that mirror the manual UI builder:

A. Data Elements

These represent each visual node you would typically see in the manual rule builder, successfully replicating transformation steps

Example:

[

{

“name”: “rawValue1”,

“sourceType”: “SourceAttributes”,

“sourceAttribute”: “email”

},

{

“name”: “transformed”,

“sourceType”: “Added_Parameter”,

“selectedFunction”: “toLowerCase”,

“selectedDataElement”: “rawValue1”

}

]

This mirrors how manual UI-created rules store transformation steps.

B. Flow Conditions JSON

This represents the visual IF/ELSE logic, allowing the system to reconstruct the rule-builder’s flowchart

Example:

[

{

“type”: “if”,

“conditions”: [

{

“dataElement”: “rawValue1”,

“comparison”: “isNotNull”

}

],

“trueBranch”: [

{ “name”: “transformed” }

]

}

]

Built via buildFlow() to reconstruct rule-builder flowchart.

C. Metadata

This acts as the ultimate “single source of truth”. XML alone cannot reconstruct a visual UI state; the metadata stores the exact details needed for the edit screen, such as selected dropdowns, chosen operations, substring indices, function ordering, and concatenation chains

Example:

{

“sourceAttribute”: [“email”],

“identityAttribute”: “employeeNumber”,

“transformChain”: [

{ “op”: “toLowerCase” },

{ “op”: “trim” }

],

“finalValue”: “transformed”

}

Generation and Seamless Editing

Finally, once the internal model is built, the AI/Template engine generates the final SailPoint XML. Because the AI generates the exact same JSON structures as the manual UI, the magic happens when a user wants to make a change.

When a user clicks “Edit,” the UI reads the stored JSON and perfectly reconstructs the builder. The edit screen loads flawlessly with every dropdown pre-selected, every function chain restored, and the entire flowchart rebuilt—allowing the user to continue tweaking the rule normally

Reference

Author

Sricharan KT