About Me
CompTIA PenTest+
1 Threats, Attacks, and Vulnerabilities
1-1 Common Threat Actors
1-2 Threat Intelligence Sources
1-3 Threat Actors and Motives
1-4 Threat Actor Tactics, Techniques, and Procedures (TTPs)
1-5 Vulnerability Types
1-6 Exploit Types
1-7 Attack Types
1-8 Threat Detection and Monitoring
1-9 Threat Hunting
1-10 Incident Response
2 Architecture and Design
2-1 Security Controls
2-2 Network Architecture
2-3 Cloud and Virtualization
2-4 Web Application Security
2-5 Wireless Security
2-6 Mobile Security
2-7 IoT Security
2-8 Industrial Control Systems (ICS) Security
2-9 Physical Security
2-10 Secure Software Development
3 Tools and Code
3-1 Penetration Testing Tools
3-2 Exploitation Tools
3-3 Post-Exploitation Tools
3-4 Reporting Tools
3-5 Scripting and Automation
3-6 Programming Languages
3-7 Code Analysis
3-8 Open Source Intelligence (OSINT) Tools
4 Planning and Scoping
4-1 Penetration Testing Methodologies
4-2 Legal and Compliance Considerations
4-3 Scope Definition
4-4 Risk Assessment
4-5 Threat Modeling
4-6 Information Gathering
4-7 Asset Identification
4-8 Data Classification
4-9 Business Impact Analysis
4-10 Penetration Testing Objectives
5 Information Gathering and Vulnerability Identification
5-1 Passive Reconnaissance
5-2 Active Reconnaissance
5-3 Vulnerability Scanning
5-4 Network Mapping
5-5 Service Identification
5-6 Web Application Scanning
5-7 Wireless Network Scanning
5-8 Social Engineering Techniques
5-9 OSINT Techniques
5-10 Vulnerability Databases
6 Attacks and Exploits
6-1 Exploit Development
6-2 Buffer Overflows
6-3 SQL Injection
6-4 Cross-Site Scripting (XSS)
6-5 Cross-Site Request Forgery (CSRF)
6-6 Command Injection
6-7 Privilege Escalation
6-8 Lateral Movement
6-9 Evasion Techniques
6-10 Exploit Delivery Methods
7 Penetration Testing Process
7-1 Pre-Engagement Activities
7-2 Reconnaissance
7-3 Scanning and Enumeration
7-4 Exploitation
7-5 Post-Exploitation
7-6 Reporting
7-7 Remediation
7-8 Retesting
7-9 Documentation and Evidence Collection
7-10 Communication and Coordination
8 Reporting and Communication
8-1 Report Structure
8-2 Executive Summary
8-3 Technical Findings
8-4 Risk Assessment
8-5 Remediation Recommendations
8-6 Legal and Compliance Considerations
8-7 Presentation Skills
8-8 Communication with Stakeholders
8-9 Documentation Standards
8-10 Continuous Improvement
9 Security and Compliance
9-1 Regulatory Requirements
9-2 Industry Standards
9-3 Compliance Audits
9-4 Data Protection
9-5 Privacy Laws
9-6 Incident Response Planning
9-7 Disaster Recovery Planning
9-8 Business Continuity Planning
9-9 Risk Management
9-10 Security Awareness Training
4.8 Data Classification Explained

4.8 Data Classification Explained

Key Concepts

1. Data Sensitivity

Data sensitivity refers to the level of confidentiality and importance of data. It helps in determining the appropriate security measures required to protect the data. Sensitivity levels can range from public data that can be shared freely to highly sensitive data that requires strict access controls.

Example: Personal health information (PHI) is highly sensitive data that requires stringent security measures, such as encryption and access controls, to protect patient privacy.

2. Data Classification Levels

Data classification levels categorize data based on its sensitivity and importance. Common classification levels include public, internal, confidential, and restricted. Each level defines the appropriate handling, storage, and transmission protocols.

Example: A company might classify its financial reports as "confidential" and restrict access to only authorized personnel, while marketing materials might be classified as "public" and freely distributed.

3. Data Handling Procedures

Data handling procedures outline the steps and protocols for managing data based on its classification level. These procedures ensure that data is protected throughout its lifecycle, from creation to disposal.

Example: For "restricted" data, handling procedures might include using secure file transfer protocols, encrypting data at rest and in transit, and conducting regular security audits.

4. Data Retention and Disposal

Data retention and disposal policies define how long data should be retained and the methods for securely disposing of data when it is no longer needed. These policies help prevent data breaches and ensure compliance with legal and regulatory requirements.

Example: A healthcare organization might retain patient records for seven years and then securely destroy them using methods like data wiping or physical destruction of storage media.

Examples and Analogies

Consider a library as an analogy for data classification:

1. Data Sensitivity: Just as a library has sections with different levels of access (e.g., public, restricted), data sensitivity determines who can access and view the data.

2. Data Classification Levels: The library's classification system (e.g., fiction, non-fiction, reference) is similar to data classification levels, which categorize data based on its importance and sensitivity.

3. Data Handling Procedures: The library's rules for borrowing and returning books are akin to data handling procedures, ensuring that data is managed securely and appropriately.

4. Data Retention and Disposal: The library's policy for removing outdated or damaged books is similar to data retention and disposal policies, ensuring that data is kept only as long as necessary and then securely disposed of.

By understanding and implementing data classification, organizations can ensure that their data is properly managed, protected, and compliant with legal and regulatory requirements.

© 2024 Ahmed Baheeg Khorshid. All rights reserved.