Zero › Module 7 › Lesson 1
Data Classification
Data-centric ZT literacy — classify data, label sensitivity, map to access policies — classification matrix on YOUR $ZT_LAB data folder.
Visual · t40_data_classification
Data classification. $ZT_LAB. Original Cyberlium.
Opening
ZT protects data — not just networks — label Public/Internal/Confidential/Restricted rows on LAB-ZT-001 data inventory.
Data classification tiers drive encryption, DLP, sharing, and microseg rules. Map fictional datasets: customer PII placeholder, finance reports, source code, public marketing — to tiers and handling rules. No real customer data in lab. Write classification matrix with owner and control column. Next: App Wrapping.
1. Classification tiers (named)
Public: no harm if disclosed. Internal: org-only. Confidential: limited need-to-know. Restricted: regulated/high impact (PII/PCI placeholders literacy).
Each tier links to encryption, sharing, retention checklist rows.
Command guide
Try these commands — Classification tiers (named)
═══ LINUX / macOS (Terminal Practice) ═══
Check system state and user context
Command — copy this
id whoami uname -a
Inspect network sockets listening for connections
Command — copy this
ss -tuln 2>/dev/null || netstat -tuln
Audit active processes
Command — copy this
ps aux | grep -v "\[" | head -15
═══ WINDOWS (POWERSHELL) ═══ Query user identity and system information
Command — copy this
whoami /all Get-ComputerInfo | Select-Object CsName, OsName, OsVersion
Primary tools to practice this lesson: grep, python3. Reference sites: CISA ZTMM Data (https://www.cisa.gov/zero-trust-maturity-model); NIST SP 800-207 (https://csrc.nist.gov/publications/detail/sp/800-207/final). Run every command in the box — install first, then the usage lines — only on YOUR lab / program scope.
2. Labeling literacy
Manual labels, MIP/AIP sensitivity labels, metadata tags in cloud storage literacy. Labels feed DLP and CASB policies in M7 L3.
Unlabeled data = policy gap on stub — document remediation.
3. Lab matrix
Ten fictional datasets with tier, owner, storage location stub, required controls.
Ship: data classification matrix. Next: App Wrapping.
4. What you ship: data classification matrix
Ten fictional datasets with tiers and controls. $ZT_LAB placeholders only. chmod 600.
5. What you record before the next lesson
Date. Classification matrix. $ZT_LAB named. File t40-m07-l01-data-classification.txt chmod 600.
6. Wrong vs right: bypass cookbooks vs YOUR ZT design
Worked failure — same MSF word, opposite target. Right never needs a café Wi-Fi or classmate laptop.
Wrong
Import real customer CSV into lab matrix. Mark everything Public for simplicity.
Right
Write classification matrix with Restricted tier example. Next: App Wrapping.
Mission: data classification matrix
1) Define four classification tiers. 2) List ten fictional datasets. 3) Map control requirements per tier. 4) chmod 600.
Stuck? Ask Cyberlium AI Mentor
Restricted tier row forces encryption + DLP link — do not skip.
Knowledge Check
APPLY: Data classification drives:
Multiple choice
Knowledge Check
APPLY: True or False: Unlabeled sensitive data is a ZT policy gap.
True or False
Knowledge Check
APPLY: Classification matrix uses:
Multiple choice