312-39 · Question #18
Jannet works in a multinational corporation that operates multiple data centers, cloud environments, and on-premises systems. As a SOC analyst, she notices that security incidents are taking too…
The correct answer is B. Log normalization. Log normalization is the key step that converts heterogeneous logs into a consistent, common schema so analysts and detections can reliably query and correlate events. In real SOC workflows, different sources use different field names, timestamp formats, severity labels, and…
Question
Jannet works in a multinational corporation that operates multiple data centers, cloud environments, and on-premises systems. As a SOC analyst, she notices that security incidents are taking too long to detect and investigate. After analyzing this, she discovers that logs from firewalls, endpoint security solutions, authentication servers, and cloud applications are scattered across different systems in various formats. Her team has to manually convert logs into a readable format before investigating incidents. What approach should she implement to accept logs from heterogeneous sources with different formats, convert them into a common format, and improve incident detection and response time?
Options
- ALog transformation
- BLog normalization
- CLog correlation
- DLog collection
How the community answered
(46 responses)- A7% (3)
- B87% (40)
- C4% (2)
- D2% (1)
Explanation
Log normalization is the key step that converts heterogeneous logs into a consistent, common schema so analysts and detections can reliably query and correlate events. In real SOC workflows, different sources use different field names, timestamp formats, severity labels, and value conventions (for example, “src_ip” vs “SourceIP,” “user” vs “AccountName”). Normalization standardizes these into consistent fields (time, host, user, source/destination, action, outcome), enabling rule logic and dashboards to work across vendors without constant manual translation. Log collection is simply getting logs into a central place; it does not guarantee they are usable or consistent. Log transformation is a broader term that can include parsing or enrichment, but normalization is the specific practice of mapping diverse formats into a common model. Log correlation comes after normalization; correlation links related events (e.g., failed logons + suspicious process + outbound beaconing) and depends on normalized data to work well. Since the problem is explicitly “multiple formats” and manual conversion delays investigation, the best solution is normalization to accelerate triage, reduce query complexity, and improve detection
Topics
Community Discussion
No community discussion yet for this question.