D-SF-A-24 Exam Dumps - EMC Security Questions and Answers

The security hardening techniques should be matched with the corresponding source area as follows:

Operating System:Enables secure boot and removes unnecessary drivers.

Database:Implements Role-Based Access Control and removes unnecessary database services.

Network:Implements Intrusion Prevention System.

Server:Encrypts the host device using hardware trusted privilege.

Operating System Hardening:Involves enabling secure boot to ensure that only trusted software is loaded during the system startup and removing unnecessary drivers to minimize potential vulnerabilities1.

Database Hardening:Role-Based Access Control (RBAC) restricts system access to authorized users, and removing unnecessary services reduces the attack surface1.

Network Hardening:An Intrusion Prevention System (IPS) monitors network traffic for suspicious activity and takes action to prevent intrusions1.

Server Hardening:Encrypting the host device using hardware-based mechanisms like Trusted Platform Module (TPM) provides a secure environment for the server’s operating system1.

These matches are based on standard security practices that align with the Dell Security Foundations Achievement’s emphasis on security hardening across different areas of IT infrastructure1.

For the question regarding the type of edge security riskA .R.T.I.E.is primarily exposed to, let’s analyze the options:

Data risk: This refers to the risk associated with the storage, processing, and transmission of data. Given thatA .R.T.I.E.is a social media company with a platform for sharing content and making in-app purchases, there is a significant amount of data being handled, which could be at risk if not properly secured.

Internet of Things (IoT) risk: This involves risks associated with IoT devices, which may not be applicable in this context asA .R.T.I.E.is described as a social media company rather than one that specializes in IoT devices.

Protection risk: This could refer to the overall security measures in place to protect the company’s assets. SinceA .R.T.I.E.has moved some applications to the public cloud and operates an internal network accessible via VPN, the protection of these assets is crucial.

Hardware risk: This involves risks related to the physical components of the network. The case study does not provide specific details about hardware vulnerabilities, so this may not be the primary concern.

Considering the case study’s focus on data handling, cloud migration, and the need for secure solutions,Data riskseems to be the most relevant edge security riskA .R.T.I.E.is exposed to. The decentralization of compute and data creation, along with the inability to physically secure the edge as one would with a data center, increases the risk to the data being processed and stored at the edge.

Remember, when preparing for assessments like the Dell Security Foundations Achievement, it’s important to thoroughly review the study materials provided, understand the key concepts, and apply them to the scenarios presented in the case studies. Good luck with your preparation!

Quantitative risk analysis in cybersecurity is a method that uses objective and mathematical models to assess and understand the potential impact of risks. It involves assigning numerical values to the likelihood of a threat occurring, the potential impact of the threat, and the cost of mitigating the risk. This approach allows for a more precise measurement of risk, which can then be used to make informed decisions about where to allocate resources and how to prioritize security measures.

The focus of a quantitative risk analysis is to provide risk acumens, which are insights into the level of risk associated with different threats. This is achieved by calculating the potential loss in terms of monetary value and the probability of occurrence. The result is a risk score that can be compared across different threats, enabling an organization to prioritize its responses and resource allocation.

For example, if a particular vulnerability in the IT system has a high likelihood of being exploited and the potential impact is significant, the quantitative risk analysis would assign a high-riskscore to this vulnerability. This would signal to the organization that they need to address this issue promptly.

Quantitative risk analysis is particularly useful in scenarios where organizations need to justify security investments or when making decisions about risk management strategies. It provides a clear and objective way to communicate the potential impact of risks to stakeholders.

In the context of the Dell Security Foundations Achievement, understanding the principles of quantitative risk analysis is crucial for IT staff and application administrators.It aligns with the topics covered in the assessment, such as security hardening, identity and access management, and security in the cloud, which are all areas where risk analysis plays a key role123.

Double Extortion Ransomware:This type of ransomware not only encrypts files but also attempts to encrypt backups and shared, networked, and cloud drives1.

Attack Method:Attackers first exfiltrate sensitive data before encrypting it, then threaten to release the data if the ransom is not paid, hence the term 'double extortion’1.

Impact on Organizations:This method increases the pressure on the victim to pay the ransom, as they face the risk of their sensitive data being published or sold1.

Prevention and Response:Organizations should implement robust backup strategies, including offsite and offline backups, and have an incident response plan that includes dealing with ransomware and data breaches1.

Double extortion ransomware attacks are particularly dangerous because they combine the threat of data encryption with the threat of data exposure, significantly increasing the potential damage to the victim organization1.

User Analytics:UEBA systems analyze user behavior to establish a baseline of normal activities and detect anomalies12.

Threat Detection:By monitoring for deviations from the baseline, UEBA can detect potential security threats, such as compromised accounts or insider threats12.

Data Analysis:UEBA solutions ingest and analyze large volumes of data from various sources within the organization to identify suspicious activities12.

Behavioral Analytics:UEBA uses behavioral analytics to understand how users typically interact with the organization’s systems and data12.

Machine Learning and Automation:Advanced machine learning algorithms and automation are employed to refine the analysis and improve the accuracy of anomaly detection over time12.

UEBA is essential forA .R.T.I.E.as it provides a comprehensive approach to security monitoring, which is critical given the diverse and dynamic nature of their user base and the complexity of their IT environment12.