The Fine Print

from typing import TypedDict, List
from pydantic import BaseModel, Field
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
class CSFEvaluation(BaseModel):
    subcategory: str = Field(description="NIST CSF Subcategory ID, e.g., GV.RM-01")
    tier_score: int = Field(description="Maturity Tier from 1 (Partial) to 4 (Adaptive)")
    justification: str = Field(description="Audit justification for assigned tier")
class CSFState(TypedDict):
    subcategory: str
    evidence_inputs: List[str]
    evaluation: dict
llm = ChatOpenAI(model="gpt-4o", temperature=0)
def evaluate_csf_subcategory(state: CSFState) -> dict:
    structured_llm = llm.with_structured_output(CSFEvaluation)
    prompt = f"""
Evaluate NIST CSF 2.0 Subcategory: {state['subcategory']}
Evidence Provided:
{chr(10).join(state['evidence_inputs'])}
Assign a maturity Tier (1 to 4) and provide justification.
"""
    res = structured_llm.invoke(prompt)
    return {"evaluation": res.model_dump()}
workflow = StateGraph(CSFState)
workflow.add_node("evaluate", evaluate_csf_subcategory)
workflow.set_entry_point("evaluate")
workflow.add_edge("evaluate", END)
app = workflow.compile()
if __name__ == "__main__":
    data = {
        "subcategory": "GV.RM-01: Risk management objectives are established and agreed to by organizational stakeholders.",
        "evidence_inputs": [
            "Executive board approved Risk Tolerance Statement dated Jan 2026.",
            "Quarterly risk reviews conducted with documented sign-offs."
        ],
        "evaluation": {}
    }
    out = app.invoke(data)
    print("NIST CSF Evaluation Result:")
    print(out["evaluation"])