palantir-data-handling

name	palantir-data-handling
description	Implement Palantir Foundry data handling with PII protection, markings, and GDPR compliance. Use when handling sensitive data in Foundry, implementing data classifications, or ensuring compliance with privacy regulations. Trigger with phrases like "palantir data", "foundry PII", "palantir GDPR", "foundry data protection", "palantir markings".
allowed-tools	Read, Write, Edit
version	1.5.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","palantir","foundry","data","privacy","compliance"]
compatibility	Designed for Claude Code

Palantir Data Handling

Overview

Handle sensitive data in Foundry using markings (data classifications), column-level security, PII redaction in transforms, and GDPR/CCPA deletion workflows.

Prerequisites

• Foundry enrollment with Markings enabled
• Understanding of your organization's data classification policy
• Familiarity with transforms (palantir-core-workflow-a)

Instructions

Step 1: Data Classification with Markings

Foundry Markings control who can access data at the dataset, column, or row level.

Marking	Access	Use Case
PUBLIC	All users	Aggregated reports, reference data
INTERNAL	Employees only	Business metrics, operational data
CONFIDENTIAL	Specific groups	Customer PII, financial data
RESTRICTED	Named individuals	Compensation, legal, M&A

Step 2: PII Redaction in Transforms

from transforms.api import transform_df, Input, Output
from pyspark.sql import functions as F

@transform_df(
    Output("/Company/datasets/customers_safe"),
    customers=Input("/Company/datasets/raw_customers"),
)
def redact_pii(customers):
    """Create an analytics-safe view with PII removed."""
    return (
        customers
        .withColumn("email", F.sha2(F.col("email"), 256))           # Hash email
        .withColumn("phone", F.lit("***-***-****"))                   
        .withColumn(, F.lit().cast())                
        .withColumn(, F.concat(
            F.substring(, , ), F.lit()            
        ))
        .drop(, , , )
    )