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Data Classification

Data Classification

Data Classification

Data Classification

Data Classification

Data Classification

Data Classification

Data Classification

Data Classification

Why Classify Data?

Data classification ensures sensitive information receives appropriate protection. Without classification, you either over-protect everything (wasting resources) or under-protect critical data (inviting breaches).

Classification Levels

Define clear tiers:

| Level | Label | Examples | Controls | |-------|-------|----------|----------| | 4 | Restricted | PII, trade secrets | Encryption, MFA, DLP | | 3 | Confidential | Financial reports | Encryption at rest | | 2 | Internal | HR policies | Access control | | 1 | Public | Marketing materials | No restrictions |

Automated Classification

Use content inspection to classify data automatically:

import re

import hashlib

class DataClassifier:

def init(self):

self.patterns = {

"ssn": r"\d{3}-\d{2}-\d{4}",

"email": r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\.[a-zA-Z]{2,}",

"credit_card": r"\b(?:\d[ -]*?){13,16}\b"

}

def classify_document(self, content, metadata):

score = 0

findings = []

for label, pattern in self.patterns.items():

matches = re.findall(pattern, content)

if matches:

score += len(matches) * 10

findings.append({"type": label, "count": len(matches)})

if score > 50:

return "restricted", findings

elif score > 10:

return "confidential", findings

elif metadata.get("internal"):

return "internal", findings

return "public", findings

Handling Procedures

Define procedures for each classification level:

handling-policies.yaml

restricted:

storage: encrypted_bucket_kms

transmission: require_tls_1.3

retention: 7_years

destruction: shred_and_degauss

sharing: require_nda_and_approval

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