CPTIA Exam Dumps : CREST Practitioner Threat Intelligence Analyst

Employee monitoring tools are primarily used by employers to detect and prevent malicious insider threats. These tools can track activities such as data access, data exfiltration attempts, unauthorized actions, and other behaviors that could indicate malicious intent or pose a risk to the organization's security. While such tools may also incidentally uncover issues like lost registry keys, conspiracies, or stolen credentials, their main purpose is to safeguard against insiders who might misuse their access to harm the organization, steal data, sabotage systems, or engage in espionage.References:CREST CPTIA study materials cover various security measures and tools that organizations can use to protect against insider threats, emphasizing the role of monitoring in detecting and responding to malicious activities by insiders.

Logging User IDs (D) can pose privacy concerns and may conflict with regulations such as the General Data Protection Regulation (GDPR), which emphasizes the protection of personal data and privacy. Therefore, while logging details such as Timestamps, Session IDs, and Source IP addresses are essential for security auditing to track when events occur, who is initiating sessions, and from where, care must be taken with User IDs. The handling of personally identifiable information (PII) must comply with privacy laws and organizational policies to safeguard individual privacy rights.

References:Security best practices and compliance frameworks discussed in the CREST guide incident handlers on what information should and should not be logged, emphasizing the need to balance security auditing requirements with privacy and regulatory obligations.

Normalization in the context of data analysis refers to the process of organizing data to reduce redundancy and improve efficiency in storing and sharing. By filtering, tagging, and queuing, Miley is effectively normalizing the data—converting it from various unstructured formats into a structured, more accessible format. This makes the data easierto analyze, store, and share. Normalization is crucial in cybersecurity and threat intelligence to manage the vast amounts of data collected and ensure that only relevant data is retained and analyzed. This technique contrasts with sandboxing, which is used for isolating and analyzing suspicious code; data visualization, which involves representing data graphically; and convenience sampling, which is a method of sampling where samples are taken from a group that is conveniently accessible.References:

"The Application of Data Normalization to Database Security," International Journal of Computer Science Issues

SANS Institute Reading Room, "Data Normalization Considerations in Cyber Threat Intelligence"