Cybersecurity Data Analyst's Foundational Data Security & Privacy
Master the essential principles and practical techniques of data security and privacy crucial for a data analyst role in the cybersecurity domain.
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Understanding Core Data Security & Privacy Principles
Unit 1: Foundational Concepts of Data Security
What is Data Security?
The CIA Triad Explained
Confidentiality in Practice
Ensuring Data Integrity
Maintaining Data Availability
Unit 2: Data Privacy: Concepts and Importance
What is Data Privacy?
Security vs. Privacy
Why Privacy Matters
Unit 3: Data Governance and Compliance
What is Data Governance?
Data Governance Roles
Introduction to Compliance
Key Regulations: GDPR
Key Regulations: CCPA
Compliance in Practice
Data Classification and Handling
Unit 1: Foundations of Data Classification
What is Data Classification?
Why Classify Data?
Data Types & Sensitivity
Who Owns Data Classification?
Classification Levels
Unit 2: Implementing Data Classification
Classification Criteria
Classification Lifecycle
Manual vs. Automated
Data Labeling Best Practices
Unit 3: Data Handling Procedures
Handling Public Data
Handling Internal Data
Handling Confidential Data
Handling Restricted Data
Data Handling Policies
Secure Data Storage and Transmission
Unit 1: Data at Rest: Storage Security
Data Storage Basics
Encryption for Stored Data
Access Controls for Storage
Secure Storage Configurations
Data Backup & Recovery
Unit 2: Data in Transit: Transmission Security
Data Transmission Basics
Encryption for Transmitted Data
Secure Network Protocols
Secure File Transfer
Unit 3: Maintaining Security Posture
Patch Management
Vulnerability Management
Secure Configurations
Logging and Monitoring
Auditing & Compliance
Data Masking and Anonymization Techniques
Unit 1: Introduction to Data De-identification
Why De-identify Data?
Masking vs. Anonymization
Key De-identification Terms
Unit 2: Data Masking Techniques
Static Data Masking
Dynamic Data Masking
Data Masking Methods
Format-Preserving Masking
Unit 3: Data Anonymization Techniques
K-Anonymity Explained
L-Diversity & T-Closeness
Differential Privacy
Data Aggregation
Unit 4: Selecting & Applying Techniques
Choosing the Right Method
De-identification Workflow
Tools for De-identification
Data Loss Prevention and Integrity Monitoring
Unit 1: Introduction to Data Loss Prevention (DLP)
What is Data Loss?
DLP: Your Data's Guardian
Why DLP Matters to You
Unit 2: DLP Components and Policy Enforcement
DLP Components Explained
Crafting DLP Policies
DLP in Action
Unit 3: Introduction to File Integrity Monitoring (FIM)
What is Data Integrity?
FIM: Your Data's Watchdog
Why FIM Matters to You
Unit 4: FIM Mechanisms and Best Practices
How FIM Works
What FIM Monitors
FIM Alerts & Responses
Unit 5: DLP & FIM in Practice
DLP & FIM: A Team Effort
Analyst's Role in DLP/FIM
DLP/FIM Challenges
Recognizing and Mitigating Insider Threats
Unit 1: Understanding Insider Threats
What's an Insider Threat?
Types of Insider Threats
Impact of Insider Threats
Why Analysts are Targets
Unit 2: Recognizing Insider Threat Indicators
Behavioral Indicators
Digital Footprints
Data Access Anomalies
Data Exfiltration Signs
Unit 3: Mitigating Insider Risks
Access Controls & Least Priv
Data Handling Best Practices
Monitoring & Auditing
User Behavior Analytics
Training & Awareness
Incident Response for Insiders