Data Security Checks — 10 LinkedIn Posts

Data Security Check #01

🔐 Access Control

Data Security Check #01: Access Control

Before trusting a data system, check who can access what.

Business problem:
Not every user should see every table, every report, or every column.

Example:
A sales analyst may need access to customer sales totals.
But that does not mean they should see:

SSN
full date of birth
bank account numbers
medical information
payroll details

Simple access review:

User Role        | Should Access Sales? | Should Access SSN?
Sales Analyst    | Yes                  | No
HR Manager       | No                   | Maybe
Payroll Admin    | No                   | Yes
Executive        | Yes                  | No

Security check:
Review user roles and permissions.

Ask:
Who has access?
Why do they need it?
When was access granted?
Is it still required?
Is it too broad?

Why it matters:
Excess access increases risk.

A user may not be malicious.
But if their account is compromised, every permission they have becomes exposure.

Common mistake:
Giving access based on convenience instead of business need.

BI impact:
A dashboard can accidentally expose sensitive fields to people who should only see aggregated data.

Key idea:
Access should be based on need, not curiosity.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#CyberSecurity
#DataGovernance
#BusinessIntelligence
#DataAnalytics
#AccessControl
#LearningByDoing

Data Security Check #02

🕵️ Sensitive Data Identification

Data Security Check #02: Sensitive Data Identification

You cannot protect what you have not identified.

Business problem:
Many organizations store sensitive data without clearly labeling it.

Examples:
customer_name
email
phone
date_of_birth
SSN
salary
medical_condition
bank_account
student_id
employee_id

Sample data inventory:

Column Name      | Sensitivity Level
customer_name    | Personal Data
email            | Personal Data
SSN              | Highly Sensitive
salary           | Confidential
order_total      | Business Data
product_name     | Public/Internal

Security check:
Classify columns by sensitivity.

Categories can be simple:

Public
Internal
Confidential
Sensitive
Highly Sensitive

Why it matters:
If sensitive data is not labeled, it may be copied, exported, shared, or published without proper protection.

Common mistake:
Treating all columns the same.

BI impact:
A report builder may include fields like SSN, DOB, or salary in a dataset without realizing the exposure risk.

Key idea:
Data classification is the first step toward responsible data use.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#DataGovernance
#CyberSecurity
#DataPrivacy
#BusinessIntelligence
#DataAnalytics
#LearningByDoing

Data Security Check #03

🛡️ Masking Sensitive Fields

Data Security Check #03: Masking Sensitive Fields

Sometimes users need the record, but not the full sensitive value.

Business problem:
A support team may need to verify the last 4 digits of an ID.
But they should not see the full SSN or account number.

Example:

customer_id | ssn
C001        | 123-45-6789
C002        | 222-33-4444

Better display:

customer_id | masked_ssn
C001        | ***-**-6789
C002        | ***-**-4444

SQL-style example:

SELECT 
    customer_id,
    CONCAT('***-**-', RIGHT(ssn, 4)) AS masked_ssn
FROM customers;

Why it matters:
Masking reduces exposure while preserving operational usefulness.

Use masking for:
• SSN
• bank accounts
• credit card numbers
• phone numbers
• emails
• employee IDs
• student IDs

Common mistake:
Showing full sensitive values because “the team might need it.”

BI impact:
A dashboard can still be useful without exposing complete personal identifiers.

Key idea:
Show only the part of the data required to complete the business task.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#DataPrivacy
#CyberSecurity
#SQL
#BusinessIntelligence
#DataGovernance
#LearningByDoing

Data Security Check #04

🔎 Least Privilege Review

Data Security Check #04: Least Privilege Review

The safest access is the minimum access required to do the job.

Business problem:
Users often accumulate permissions over time.

They change roles.
They join projects.
They receive temporary access.
But access is rarely removed.

Example:

User       | Role              | Current Access
Ana        | Analyst           | Sales, Payroll, HR
Luis       | Finance Manager   | Finance, Payroll
Maria      | Former Contractor | SQL Database

Security check:
Ask:

Does this user still need this access?
Is this access aligned with their current role?
Was this access temporary?
Can read-only access replace edit access?
Can aggregated access replace row-level access?

Why it matters:
Old permissions create hidden risk.

The user may no longer need the data.
But the access remains.

Common mistake:
Only reviewing access when something goes wrong.

BI impact:
Users may see reports, workspaces, or datasets that no longer match their job responsibilities.

Key idea:
Access should expire unless it is still justified.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#AccessControl
#CyberSecurity
#DataGovernance
#BusinessIntelligence
#RiskManagement
#LearningByDoing

Data Security Check #05

📤 Data Export Risk

Data Security Check #05: Data Export Risk

The biggest risk is not always inside the database.

Sometimes the risk begins when data is exported.

Business problem:
A secure report becomes risky when someone downloads it to Excel, emails it, or uploads it somewhere else.

Example:

Original system:
Controlled access
Logged activity
Role-based permissions

After export:
Local file
No tracking
Easy to forward
Easy to copy
Easy to lose

Security check:
Review export behavior.

Ask:
Can users export data?
What fields can they export?
Are sensitive fields included?
Is export logging enabled?
Are files encrypted?
Is there a business reason for export?

Why it matters:
Once data leaves the controlled environment, governance becomes harder.

Common mistake:
Securing the dashboard but ignoring the downloaded file.

BI impact:
A Power BI report may be secure, but an exported spreadsheet may expose sensitive data.

Key idea:
Data security must follow the data after export.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#DataGovernance
#CyberSecurity
#BusinessIntelligence
#PowerBI
#DataPrivacy
#LearningByDoing

Data Security Check #06

🧾 Audit Log Review

Data Security Check #06: Audit Log Review

If nobody reviews the logs, the logs are only decoration.

Business problem:
You need to know who accessed sensitive data, when, and what they did.

Example audit log:

user_id | action        | object_name      | timestamp
u001    | VIEW_REPORT   | Payroll Summary  | 2026-07-01 09:10
u002    | EXPORT_DATA   | Customer List     | 2026-07-01 10:30
u003    | DELETE_RECORD | Orders            | 2026-07-01 11:15

Security check:
Look for unusual activity.

Examples:
• large exports
• access outside business hours
• repeated failed logins
• access to sensitive reports
• deleted records
• permission changes
• inactive users accessing systems

Why it matters:
Audit logs help detect misuse, mistakes, and compromised accounts.

Common mistake:
Collecting logs but never reviewing them.

BI impact:
Security incidents can affect trust in reports, dashboards, and operational decisions.

Key idea:
Logging is not enough.
Monitoring makes logs useful.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#CyberSecurity
#AuditLogs
#DataGovernance
#BusinessIntelligence
#RiskManagement
#LearningByDoing

Data Security Check #07

🧱 Row-Level Security

Data Security Check #07: Row-Level Security

Sometimes users should see the same report, but not the same rows.

Business problem:
A regional manager should only see data for their region.

Example:

User        | Region
Ana         | East
Luis        | West
Maria       | South

Sales table:

order_id | region | amount
1001     | East   | 500
1002     | West   | 300
1003     | South  | 700

Security rule:
Ana sees only East.
Luis sees only West.
Maria sees only South.

Why it matters:
Without row-level security, users may see data outside their responsibility.

Use cases:
• regional sales
• department budgets
• school campuses
• city districts
• employee records
• customer territories

Common mistake:
Creating one report for everyone without applying row-level restrictions.

BI impact:
Power BI dashboards can accidentally expose records across departments, regions, or business units.

Key idea:
Security is not only about who can open the report.
It is also about which rows they can see.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#RowLevelSecurity
#PowerBI
#BusinessIntelligence
#DataGovernance
#DataPrivacy
#LearningByDoing

Data Security Check #08

🗂️ Data Retention

Data Security Check #08: Data Retention

Keeping data forever is not a strategy.

Business problem:
Organizations often keep old data because deleting it feels risky.

But unnecessary old data creates exposure.

Example:

Data Type          | Retention Need
Transaction data   | 7 years
Temporary exports  | 30 days
Inactive accounts  | 1 year after closure
Logs               | 90 days or policy-based
Draft files        | Delete when no longer needed

Security check:
Ask:

Why are we keeping this data?
How long do we need it?
Who owns the retention rule?
Can it be archived?
Can it be anonymized?
Can it be deleted safely?

Why it matters:
Old data can still be breached, copied, leaked, or misused.

Common mistake:
Keeping everything because storage is cheap.

Storage may be cheap.
Risk is not.

BI impact:
Old records may appear in dashboards, distort trends, or expose information no longer needed for business decisions.

Key idea:
Data that no longer has a business purpose may become a security liability.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#DataGovernance
#DataRetention
#CyberSecurity
#BusinessIntelligence
#DataPrivacy
#LearningByDoing

Data Security Check #09

🤝 Sharing Permission Review

Data Security Check #09: Sharing Permission Review

Most data exposure does not look dramatic.

Sometimes it is just a link shared with the wrong people.

Business problem:
Reports, folders, spreadsheets, and dashboards are often shared too broadly.

Examples:
Anyone with the link
Entire organization
External users
Old project team
Former contractors
Personal email accounts

Security check:
Review sharing settings.

Ask:
Who can view?
Who can edit?
Who can download?
Can external users access it?
Is the link public?
Is inheritance exposing more than expected?
Are former users still included?

Why it matters:
A report can be technically correct but shared with the wrong audience.

Common mistake:
Using broad sharing because it is faster.

BI impact:
Sensitive reports may reach users who were never intended to see the data.

Key idea:
Data security includes where the file, report, or dashboard is shared.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#CyberSecurity
#DataGovernance
#BusinessIntelligence
#DataPrivacy
#AccessControl
#LearningByDoing

Data Security Check #10

🚦 Security Before Publishing

Data Security Check #10: Security Before Publishing

Before publishing a dashboard, ask one question:

What could this expose?

Business problem:
Dashboards are often reviewed for design, numbers, and performance.

But not always for security.

Pre-publishing checklist:

Does it contain personal data?
Does it contain sensitive fields?
Are filters exposing records?
Are drill-through pages exposing detail?
Can users export underlying data?
Is row-level security working?
Are hidden columns truly protected?
Are workspace permissions correct?
Are external users included?
Is the data still needed at this level of detail?

Why it matters:
Publishing makes data visible.

A mistake in a private file affects a few people.
A mistake in a published dashboard can affect many.

Common mistake:
Thinking that hiding a column visually means the data is secure.

BI impact:
A beautiful dashboard can become a security problem if it exposes too much detail.

Better approach:
Review security before publishing, not after.

Key idea:
A report is not ready when it looks good.
It is ready when it is accurate, useful, and safe to share.

More practical resources:
https://jobaqui.com
https://hialeahoficios.com
https://miamioficios.com

#DataSecurity
#CyberSecurity
#PowerBI
#BusinessIntelligence
#DataGovernance
#DataPrivacy
#LearningByDoing