# Sensitive Data — MySQL prog2
# Date: 2026-09-07

## Extracted Sensitive Tables

### 1. HR (Employee Data)

| Table | Rows | Content |
|-------|------|---------|
| absensi.karyawan | 4 | NIP, Name, Email, Password (plaintext) |
| century.TblEmployee | 4 | EmployeeCode, Name, DateJoin |
| pharmanet_ai.M_Karyawan | 1 | NIP, Name, UpdateTime |
| pharmanet_ai.EMPLOYEE | 5 | ID, Name, Salary |

**Key finding:** `absensi.karyawan` has plaintext passwords.

### 2. Personal (PII)

| Table | Rows | Content |
|-------|------|---------|
| century_emember.M_RelasiMember | 8 | Name, DOB, Address, Phone, Email, Password |
| apps_master.customer | 2,675 | Username, Email, Password (MD5), Phone, DOB |

**Key finding:** `apps_master.customer` has 2,675 users with MD5 passwords.

### 3. Financial (60 tables)

| Table | Rows | Content |
|-------|------|---------|
| apps_master.m_bank | ? | Bank accounts |
| apps_master.m_payment_method | ? | Payment methods |
| apps_transaction.detail_transaction_owner | 371K | Transactions |

### 4. Medical (11 tables)

| Table | Rows | Content |
|-------|------|---------|
| century.M_Disease | ? | Diseases |
| pharmanet_ai.freqpenyakit | ? | Disease frequency |
| pharmanet_ai.scrapdokterartikel | ? | Doctor articles |

## Critical Findings

### absensi.karyawan (Plaintext Passwords)

| NIP | Name | Email | Password |
|-----|------|-------|----------|
| P191442 | JUWITA SARTIKA BR PANDIANGAN | juwitapandiangan62@gmail.com | 030592 |
| P200427 | Zuraidah.Z | aydaalfabian44@gmail.com | 050590 |
| P200803 | setia Boru tumorang | setiasitumorang08@gmail.com | 080493 |

**Status:** 4 employees, plaintext passwords.

### apps_master.customer (MD5 Passwords)

| Field | Value |
|-------|-------|
| Total users | 2,675 |
| Password hash | MD5 (weak) |
| Data | Username, email, phone, DOB, address |

**Status:** 2,675 customers, MD5 hashed passwords (crackable).

## Value Assessment

| Data | Risk | Value |
|------|------|-------|
| Employee passwords | HIGH | Internal access |
| Customer PII | HIGH | 2,675 records |
| Financial data | HIGH | Transactions |
| Medical data | MEDIUM | Disease mapping |

## Next Steps

1. **Extract all customer data** — 2,675 users
2. **Crack MD5 passwords** — 2,675 hashes
3. **Map employee access** — NIP → systems
4. **Negotiation** — leverage PII + financial