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("***-***-****"))                   # Mask phone
        .withColumn("ssn", F.lit(None).cast("string"))                # Remove SSN
        .withColumn("name", F.concat(
            F.substring("first_name", 1, 1), F.lit("***")            # First initial only
        ))
        .drop("first_name", "last_name", "address", "date_of_birth")
    )

Step 3: GDPR Right to Erasure

def delete_user_data(client, user_id: str):
    """GDPR Article 17: delete all data for a specific user."""
    datasets_with_pii = [
        "/Company/datasets/raw_customers",
        "/Company/datasets/raw_orders",
        "/Company/datasets/customer_communications",
    ]
    for dataset_path in datasets_with_pii:
        # Trigger a transform that filters out the user
        client.ontologies.Action.apply(
            ontology="my-company",
            action_type="gdprDeleteUser",
            parameters={"userId": user_id, "datasetPath": dataset_path},
        )
    # Log the deletion for compliance
    client.ontologies.Action.apply(
        ontology="my-company",
        action_type="logDeletionRequest",
        parameters={
            "userId": user_id,
            "requestedAt": datetime.utcnow().isoformat(),
            "status": "completed",
        },
    )

Step 4: Column-Level Security in Ontology

# Define object type with restricted properties
# In Ontology Manager:
# - fullName: marking = CONFIDENTIAL
# - email: marking = CONFIDENTIAL  
# - department: marking = INTERNAL
# - employeeId: marking = INTERNAL

# Users without CONFIDENTIAL marking see:
# employeeId, department (but NOT fullName, email)

Step 5: Data Retention Policy

@transform_df(
    Output("/Company/datasets/events_retained"),
    events=Input("/Company/datasets/raw_events"),
)
def apply_retention(events):
    """Keep only last 2 years of data per retention policy."""
    from pyspark.sql import functions as F
    from datetime import datetime, timedelta

    cutoff = (datetime.utcnow() - timedelta(days=730)).strftime("%Y-%m-%d")
    return events.filter(F.col("event_date") >= cutoff)

Output

• PII-redacted datasets safe for analytics
• GDPR deletion workflow with audit trail
• Column-level security via Foundry Markings
• Automated data retention enforcement

Error Handling

Compliance Risk	Detection	Mitigation
PII in analytics dataset	Column scan	Apply redaction transform
Stale data beyond retention	Date filter	Schedule retention transforms
Missing deletion audit	Log review	Always log GDPR actions
Over-permissive markings	Access audit	Review marking assignments quarterly

Resources

• Foundry Markings
• Transforms Guide

Next Steps

For access control, see palantir-enterprise-rbac.