CCDL1 SOC Analyst Certification

Overview

This project summarizes my completion of the CyberDefenders CCDL1 SOC Analyst certification path. CCDL1 is a practical blue team certification focused on developing the skills needed to operate as a Tier 1 SOC analyst.

The training focused on detecting, investigating, and responding to cyber attacks through realistic investigations. The course covered SOC operations, threat intelligence, network and endpoint security, SIEM operations, log analysis, phishing defense, digital forensics, incident response, cloud forensics, AWS log analysis, Microsoft Sentinel, and AI supported security workflows.

Certification Scope

The CCDL1 path focused on the core areas needed for SOC analyst work:

• SOC operations
• Threat intelligence
• Network security
• Endpoint security
• SIEM operations
• Log analysis
• Email security
• Phishing defense
• Digital forensics
• Incident response
• Cloud forensics
• AWS log analysis
• Microsoft Sentinel investigations
• AI supported security workflows
• Alert triage and escalation
• Reporting and documentation

Objective

The objective of this certification was to develop practical SOC analyst skills by learning how to:

• Detect suspicious activity across multiple log sources
• Investigate security alerts
• Correlate events across endpoint, network, identity, email, and cloud data
• Analyze AWS logs for suspicious cloud activity
• Use Microsoft Sentinel for SIEM investigation workflows
• Review phishing artifacts and email based threats
• Perform forensic analysis during security investigations
• Respond to incidents using a structured process
• Document findings clearly for escalation and reporting

Skills Demonstrated

• SOC alert triage
• SIEM investigation
• Microsoft Sentinel usage
• AWS log analysis
• Cloud security investigation
• Threat intelligence analysis
• Network traffic analysis
• Endpoint investigation
• Email and phishing analysis
• Digital forensics
• Incident response
• Case documentation
• Log correlation
• Evidence review
• Risk based prioritization
• Investigation reporting

Tools and Concepts Used

• Microsoft Sentinel
• AWS security logs
• AWS CloudTrail style audit logs
• AWS authentication and API activity review
• CloudWatch style log review
• VPC Flow Log style network review
• GuardDuty style alert analysis
• SIEM dashboards
• Alert queues
• Log correlation
• Threat intelligence
• Digital forensics tools
• Email security tools
• Network analysis tools
• Endpoint investigation workflows
• Case management concepts
• MITRE ATT&CK
• Incident response methodology

Methodology

1. SOC Operations and Threat Intelligence

The training began with SOC operations and threat intelligence fundamentals. This section focused on how SOC teams monitor environments, triage alerts, enrich indicators, and prioritize suspicious activity.

I practiced reviewing alerts from an analyst perspective and thinking through which events required escalation, deeper investigation, or additional context.

2. Network and Endpoint Security

This section focused on monitoring enterprise infrastructure for suspicious network and endpoint activity.

I practiced identifying signs of malware, intrusion attempts, suspicious connections, lateral movement, and abnormal endpoint behavior. The goal was to understand how network and endpoint evidence can support a larger security investigation.

3. SIEM Operations and Log Analysis

This section focused on using SIEM data to investigate suspicious activity.

I practiced collecting, reviewing, and correlating logs from different sources to validate alerts and understand the full scope of activity. This included analyzing authentication events, system activity, network indicators, endpoint evidence, and cloud related logs.

The main goal was to avoid treating alerts as isolated events and instead connect them into a larger investigation timeline.

4. Microsoft Sentinel Investigation

The course included Microsoft Sentinel style investigation workflows.

I practiced using SIEM concepts to review alerts, query logs, analyze security events, and understand how cloud native SIEM tools help defenders detect and investigate threats.

This strengthened my understanding of how modern SOC teams use centralized log data, dashboards, detection rules, and investigation workflows to respond to suspicious activity.

5. AWS Log Analysis

A key part of the cloud security portion involved analyzing AWS related logs and cloud activity.

I practiced reviewing AWS log evidence to identify suspicious behavior such as unusual authentication activity, abnormal API calls, access from unexpected sources, cloud resource changes, and possible signs of compromised credentials.

The AWS log analysis portion helped me understand how cloud investigations differ from traditional endpoint and network investigations. Instead of only looking at hosts and local artifacts, cloud investigations require reviewing identity activity, API actions, account behavior, access patterns, and cloud service logs.

6. Email Security and Phishing Defense

This section focused on identifying and investigating phishing attempts, spoofing, and business email compromise style activity.

I practiced reviewing suspicious emails, analyzing sender details, examining links and attachments, checking reputation data, and identifying indicators that could support defensive action.

The goal was to understand how phishing investigations move from initial user report to evidence review, risk determination, and response.

7. Digital Forensics and Incident Response

The DFIR portion focused on evidence acquisition, forensic analysis, and coordinated response workflows.

I practiced reviewing investigation evidence, identifying attacker activity, documenting findings, and thinking through containment, eradication, and recovery steps.

This helped connect technical evidence to the larger incident response process.

8. Cloud Forensics and AI Supported Security

The cloud forensics section introduced modern cloud investigation concepts and AI supported security workflows.

I practiced thinking through cloud incidents from a defender perspective, including how cloud logs, identity events, and service activity can be used to identify suspicious behavior.

The AI security portion helped show how automation and AI supported tooling can assist with visibility, triage, and response, while still requiring analyst judgment to validate findings.

AWS Log Analysis Focus

The AWS log analysis portion strengthened my ability to investigate cloud activity by reviewing evidence such as:

• Authentication activity
• API activity
• Account and identity behavior
• Suspicious source locations
• Resource creation or modification events
• Permission related activity
• Network flow style records
• Cloud security alerts
• Unusual access patterns
• Potential compromised credential activity

This helped me understand that cloud investigations rely heavily on identity, audit, and service activity logs. The analyst must determine who performed an action, from where, against what resource, and whether the behavior fits expected business activity.

Example Investigation Questions Practiced

During the certification path, I practiced asking questions such as:

• What triggered the alert?
• Which user, host, account, or cloud identity was involved?
• Was the activity expected or suspicious?
• What log source supports the finding?
• Were there related events before or after the alert?
• Did the activity involve endpoint, network, email, identity, or cloud data?
• Was there evidence of lateral movement, credential abuse, or data access?
• Does the activity require escalation?
• What containment or remediation action would reduce risk?

What I Did

Throughout this certification path, I practiced SOC analyst workflows across traditional and cloud based environments.

I reviewed alerts, analyzed log data, correlated events, investigated phishing indicators, examined endpoint and network evidence, reviewed AWS cloud logs, used Microsoft Sentinel style workflows, and documented findings in a way that supports escalation and incident response.

The training helped reinforce the mindset that SOC analysts must not only find suspicious activity, but also explain what happened, why it matters, what evidence supports the finding, and what should happen next.

Key Takeaways

CCDL1 strengthened my practical understanding of SOC analyst work by combining alert triage, SIEM analysis, cloud investigation, phishing defense, digital forensics, and incident response.

The most valuable part was learning how to connect multiple evidence sources together. A single alert rarely tells the full story. A strong analyst must correlate endpoint, network, email, identity, SIEM, and cloud logs to understand the full scope of an incident.

The AWS log analysis portion was especially useful because cloud investigations require a different mindset. In cloud environments, identity activity, API calls, permissions, and resource changes often matter as much as host based evidence.

What This Project Demonstrates

This project demonstrates my ability to:

• Operate within SOC investigation workflows
• Triage and investigate security alerts
• Use SIEM data to analyze suspicious activity
• Work with Microsoft Sentinel style investigations
• Analyze AWS logs for cloud security events
• Correlate endpoint, network, identity, email, and cloud evidence
• Investigate phishing and email based threats
• Review forensic evidence during incident response
• Understand cloud forensics concepts
• Apply structured incident response thinking
• Document findings clearly for escalation and reporting

Disclosure Notice

This writeup does not include CCDL1 exam questions, answers, private exam content, protected lab answers, or assessment material.

The purpose of this page is to document the skills, tools, and defensive security concepts practiced during the certification path while respecting exam integrity.