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Question 81
Which technique is most effective for detecting lateral movement within a complex enterprise network environment?
A) Network traffic analysis
B) Endpoint antivirus scanning
C) Firewall rule enforcement
D) Patch management
Answer: A
Explanation:
Network traffic analysis is a fundamental technique for detecting lateral movement within enterprise networks, as it provides visibility into how devices communicate internally and externally. Lateral movement occurs when attackers exploit compromised accounts or systems to traverse the network, seeking higher-value targets. Unlike option B, endpoint antivirus scanning, which primarily detects known malware signatures, or option C, firewall rule enforcement, which restricts traffic but does not analyze communication patterns, network traffic analysis examines flow data, protocol anomalies, and unusual connection patterns that may indicate an attacker moving stealthily. Similarly, option D, patch management, reduces vulnerabilities but does not actively monitor movement within the network.
Effective network traffic analysis uses tools such as intrusion detection systems (IDS), flow monitoring (NetFlow/sFlow), and deep packet inspection (DPI). These tools can identify abnormal connections, unexpected communication between devices, and anomalies in data transfer sizes or destinations. Network traffic analysis also enables correlation with user behavior analytics (UBA) and threat intelligence feeds to distinguish between legitimate administrative activity and malicious movement. Sophisticated threat actors often employ techniques like pass-the-hash, token impersonation, or scheduled task manipulation, which leave subtle network traces but may bypass endpoint defenses.
By implementing network segmentation, monitoring lateral movement, and analyzing traffic patterns for deviations from normal baselines, organizations can detect these tactics early, reducing dwell time and potential impact. Additionally, network traffic analysis supports forensic investigations, helping security teams reconstruct attack paths and improve future detection strategies. By proactively analyzing internal communications, enterprises can identify stealthy adversaries, contain threats more effectively, and enhance overall cybersecurity posture. For these reasons, the correct answer is A.
Question 82
Which method best mitigates the risk of data exfiltration via unauthorized removable media?
A) Endpoint device control policies
B) Firewall logging
C) Patch management automation
D) Multi-factor authentication
Answer: A
Explanation:
Endpoint device control policies are the most effective method to mitigate the risk of data exfiltration via unauthorized removable media. Attackers and insiders often use USB drives, external hard disks, or other portable devices to extract sensitive data. While option B, firewall logging, captures network activity, it cannot detect or prevent local media-based exfiltration. Similarly, option C, patch management automation, strengthens system security but does not control access to removable devices, and option D, multi-factor authentication, enhances authentication security but is irrelevant for local media use. Endpoint device control policies involve creating strict rules to restrict the use of removable devices based on user roles, encrypting approved devices, and auditing their usage. Many organizations leverage endpoint management solutions or mobile device management (MDM) platforms to enforce these policies. Additionally, integration with data loss prevention (DLP) systems ensures that attempts to copy sensitive files to unauthorized devices are logged or blocked in real-time. Administrators can also implement device whitelisting, ensuring only pre-approved media types are accessible, while leveraging behavioral analytics to detect abnormal transfer attempts. By monitoring both local and network endpoints, organizations gain a comprehensive defense against insider threats and external actors attempting to exfiltrate data. Continuous auditing and periodic testing of device control policies further ensure compliance and effectiveness. Furthermore, these policies support regulatory adherence for standards such as HIPAA, GDPR, and PCI DSS, which mandate the protection of sensitive data from unauthorized disclosure. In essence, endpoint device control is a proactive, layered security measure that prevents data leakage at the source, complementing network-based defenses and minimizing the risk of data compromise. Therefore, A is the correct answer.
Question 83
Which analytical approach most accurately detects anomalies in cloud workloads across multiple environments?
A) Behavioral baselining with machine learning
B) Manual log review
C) Static vulnerability scanning
D) Network firewall segmentation
Answer: A
Explanation:
Behavioral baselining with machine learning is the most accurate analytical approach for detecting anomalies in cloud workloads across diverse environments. Cloud workloads are dynamic, ephemeral, and highly distributed, making traditional detection methods, such as option B, manual log review, inefficient and error-prone. Option C, static vulnerability scanning, identifies known weaknesses but cannot detect runtime anomalies or sophisticated attacks. Option D, network firewall segmentation, is preventive but does not provide insight into workload behavior. Behavioral baselining uses historical activity data to establish expected patterns for cloud instances, user activity, and application behavior. Machine learning algorithms can then detect deviations, such as abnormal CPU usage, unusual network traffic, or unexpected API calls. This approach is particularly effective in hybrid and multi-cloud environments, where workloads shift frequently, and static rules fail to capture emerging threats. Additionally, it allows the integration of cloud-native telemetry, SIEM correlation, and threat intelligence feeds, providing actionable alerts and insights. For instance, an attacker attempting privilege escalation or data exfiltration within a cloud instance may trigger anomalous read/write activity, unrecognized API access, or irregular authentication attempts—all detectable via behavioral analysis. Combining this with automated incident response and orchestration enhances security posture by reducing dwell time and enabling swift containment. Organizations adopting behavioral baselining with machine learning benefit from predictive detection, improved compliance auditing, and continuous monitoring across cloud-native and hybrid architectures. The proactive nature of this method makes it a cornerstone for modern cloud security operations, ensuring organizations can detect and respond to advanced threats efficiently. Therefore, A is the correct answer.
Question 84
Which practice most effectively strengthens endpoint resilience against ransomware attacks?
A) Application whitelisting and strict execution controls
B) Multi-factor authentication
C) Network firewall rule modification
D) Manual log auditing
Answer: A
Explanation:
Application whitelisting and strict execution controls significantly enhance endpoint resilience against ransomware attacks by ensuring only approved software can execute. Ransomware often attempts to run unauthorized executables to encrypt files and propagate within a network. Option B, multi-factor authentication, secures account access but does not prevent malware execution. Option C, network firewall rule modification, limits network access but cannot stop local encryption activity, and option D, manual log auditing, is reactive rather than preventive. Application whitelisting enforces execution policies at the OS level, blocking unknown or suspicious programs from running. Combining this with behavioral monitoring, endpoint detection and response (EDR) solutions, and automated alerts further strengthens security. Advanced ransomware may try to exploit living-off-the-land binaries (LOLBins) or abuse system utilities, making strict execution controls essential to prevent lateral movement and file encryption. Organizations should implement whitelisting policies based on risk profiles, testing new applications in isolated environments to avoid operational disruption. Periodic review and continuous updates ensure whitelists remain effective as software environments evolve. Moreover, combining these measures with regular offline backups and immutable storage ensures rapid recovery if ransomware bypasses controls. Application whitelisting represents a proactive, high-assurance approach, reducing attack surface and minimizing potential damage from malware, including ransomware. Security teams using this method gain a deterministic control over endpoint behavior, allowing rapid containment and robust resilience against increasingly sophisticated threats. Therefore, A is the correct answer.
Question 85
Which mechanism is most effective for correlating multi-source security telemetry to detect advanced threats?
A) Security information and event management (SIEM)
B) Antivirus signature updates
C) Manual vulnerability scanning
D) Multi-factor authentication
Answer: A
Explanation:
Security information and event management (SIEM) systems are crucial for correlating multi-source security telemetry to detect advanced threats. Modern attacks often span endpoints, cloud workloads, network devices, and applications. Option B, antivirus signature updates, only address known malware and lack correlation capabilities. Option C, manual vulnerability scanning, identifies weaknesses but does not integrate diverse telemetry streams. Option D, multi-factor authentication, enhances access security but cannot detect complex attack patterns. SIEM platforms aggregate logs and events from multiple sources, normalize them, and correlate activities to identify indicators of compromise (IoCs). Using behavioral analytics, threat intelligence feeds, and anomaly detection, SIEM identifies sophisticated attacks, such as APTs or insider threats, which often involve low-and-slow tactics. Automated alerting and integration with orchestration and response (SOAR) solutions enable rapid investigation and containment, minimizing impact. By centralizing data, security teams can uncover hidden patterns, link seemingly unrelated events, and prioritize responses based on risk. SIEM also supports regulatory compliance and forensic analysis, providing a comprehensive view of organizational security. Its ability to correlate telemetry across multiple domains is indispensable for modern cybersecurity operations, making it the most effective solution for detecting advanced, multi-vector threats. Therefore, A is the correct answer.
Question 86
Which technique is most effective for detecting insider threats using abnormal user behavior across multiple systems?
A) User and entity behavior analytics (UEBA)
B) Manual access log review
C) Standard antivirus scanning
D) Periodic password resets
Answer: A
Explanation:
User and entity behavior analytics (UEBA) is highly effective for detecting insider threats because it focuses on patterns and anomalies in user behavior across multiple systems. Unlike traditional methods that rely on known signatures or manual log reviews, UEBA establishes baseline behavior for users and entities and flags deviations that may indicate malicious or careless activity. For example, a user accessing sensitive data at unusual hours, copying large amounts of files, or logging in from multiple geographic locations simultaneously would trigger alerts in a UEBA system. Option B, manual access log review, is time-consuming, prone to human error, and cannot scale across large enterprise environments. Option C, standard antivirus scanning, only detects known malware and does not detect unauthorized user behavior. Option D, periodic password resets, helps reduce credential misuse but is insufficient to detect malicious insider actions actively. UEBA integrates with SIEM platforms and leverages machine learning to detect subtle anomalies that human analysts might miss. This approach enhances security by correlating login patterns, file access trends, application usage, and network activity to identify potentially malicious or compromised users. UEBA also supports regulatory compliance, such as HIPAA, GDPR, and SOX, which require monitoring and auditing for unusual activity. Implementing UEBA strengthens incident response capabilities by providing actionable alerts, enabling rapid containment and investigation of suspicious user behavior. Organizations can reduce insider threat risks, protect sensitive data, and ensure overall enterprise security by leveraging UEBA in conjunction with existing monitoring and endpoint detection solutions. Therefore, A is the correct answer.
Question 87
Which method best identifies zero-day malware targeting critical applications before it executes?
A) Sandbox analysis with dynamic behavior monitoring
B) Signature-based antivirus scanning
C) Firewall traffic filtering
D) Manual patch verification
Answer: A
Explanation:
Sandbox analysis with dynamic behavior monitoring is the most effective approach for detecting zero-day malware targeting critical applications before execution. Zero-day malware exploits unknown vulnerabilities and is not detectable using traditional signature-based methods. Option B, signature-based antivirus scanning, relies on known malware signatures, which makes it ineffective against new, previously unseen threats. Option C, firewall traffic filtering, only controls network communication and cannot analyze malware behavior locally. Option D, manual patch verification, ensures applications are updated but cannot prevent execution of zero-day malware that bypasses patching. Sandboxing involves running unknown or suspicious files in a controlled virtual environment, allowing the security system to observe runtime behavior without affecting production systems. Behavioral analysis identifies actions such as file encryption attempts, registry modifications, or unexpected network connections. Integration with EDR and SIEM solutions allows alerts to be correlated with other indicators of compromise, providing early detection and rapid response. Sandboxing enhances an organization’s proactive security posture, enabling security teams to identify and block threats before they compromise critical applications. Regulatory frameworks such as NIST CSF, ISO 27001, and PCI DSS emphasize proactive threat detection and containment, which sandboxing supports effectively. The ability to detect zero-day malware in real-time reduces the risk of data breaches, service disruptions, and financial losses, making sandbox analysis a crucial component of modern cybersecurity defense. Therefore, A is the correct answer.
Question 88
Which approach most effectively reduces the risk of lateral movement after a credential compromise?
A) Network segmentation and access control policies
B) Antivirus endpoint scanning
C) Manual password auditing
D) Periodic firewall rule updates
Answer: A
Explanation:
Network segmentation and access control policies are the most effective methods to reduce lateral movement after a credential compromise because they isolate systems, limit access privileges, and prevent attackers from freely navigating the network. Option B, antivirus endpoint scanning, identifies malware but does not restrict attacker movement once credentials are compromised. Option C, manual password auditing, is reactive and cannot prevent ongoing lateral movement. Option D, periodic firewall rule updates, may control traffic but cannot dynamically enforce user-level restrictions across segmented networks. Implementing least privilege access, micro-segmentation, VLANs, and role-based access controls (RBAC) ensures that compromised credentials cannot access critical systems or sensitive data unnecessarily. Network segmentation also enables monitoring of traffic flows between segments, allowing faster detection of unauthorized access attempts. Combining segmentation with EDR and UEBA allows correlation of anomalous behavior, such as lateral login attempts or unauthorized file transfers, triggering automated alerts and containment actions. Security frameworks such as NIST SP 800-53, ISO 27001, and CIS Controls emphasize the importance of segmentation and access controls in limiting attack surfaces and reducing dwell time. By enforcing strict network boundaries and continuous monitoring, organizations mitigate the impact of credential compromise, contain attacks, and protect critical business functions. Network segmentation complements incident response strategies and strengthens overall cybersecurity resilience, making it a crucial defense mechanism in enterprise environments. Therefore, A is the correct answer.
Question 89
Which tool is most suitable for detecting phishing attempts in real-time across an organization’s email infrastructure?
A) Advanced email threat protection with AI-based filtering
B) Manual user reporting
C) Periodic phishing awareness training
D) Firewall email port blocking
Answer: A
Explanation:
Advanced email threat protection with AI-based filtering is the most suitable tool for detecting phishing attempts in real-time across an organization’s email infrastructure. Phishing attacks are increasingly sophisticated, often bypassing traditional keyword-based filters or user awareness measures. Option B, manual user reporting, relies on human vigilance and is not scalable across large organizations. Option C, periodic phishing awareness training, improves user knowledge but cannot provide real-time prevention. Option D, firewall email port blocking, may reduce spam but cannot identify malicious links or attachments within legitimate email traffic. AI-based email protection tools leverage machine learning, natural language processing, and threat intelligence feeds to identify suspicious emails by analyzing sender reputation, embedded URLs, attachment behavior, and email content patterns. These tools can automatically quarantine or block emails, alert security teams, and provide detailed reports for incident response. Integration with SIEM platforms enhances detection and correlation with other suspicious activities, enabling faster response and remediation. Real-time protection ensures that potential compromises, credential theft, or malware delivery are prevented before users interact with phishing emails. Regulatory frameworks such as HIPAA, GDPR, and SOX require organizations to implement proactive controls to protect sensitive data from email-based attacks. By deploying AI-driven threat detection, enterprises reduce the risk of phishing-related breaches, enhance user safety, and maintain operational continuity. Therefore, A is the correct answer.
Question 90
Which method is most effective in identifying lateral movement using unusual remote access patterns?
A) Network traffic analysis combined with anomaly detection
B) Manual remote access log review
C) Standard antivirus endpoint scanning
D) Firewall IP blocklists
Answer: A
Explanation:
Network traffic analysis combined with anomaly detection is the most effective method for identifying lateral movement through unusual remote access patterns. Lateral movement often involves exploiting valid credentials or remote services, making it difficult to detect through traditional signature-based approaches. Option B, manual log review, is slow and cannot scale in modern enterprise networks. Option C, standard antivirus endpoint scanning, only identifies malware but not suspicious use of legitimate credentials. Option D, firewall IP blocklists, may block known malicious sources but cannot detect sophisticated attacks using legitimate access points. Network traffic analysis examines connection frequency, destination patterns, protocol usage, and session anomalies, allowing detection of unusual behavior, such as repeated connections to sensitive servers, off-hours access, or atypical application use. Integration with UEBA and SIEM platforms enables correlation of alerts across endpoints, applications, and network layers, increasing detection accuracy. Early identification of lateral movement reduces dwell time, limits attack impact, and supports incident response and containment efforts. Regulatory compliance frameworks, such as NIST CSF, ISO 27001, and PCI DSS, emphasize monitoring and detecting unauthorized access or anomalous activity to protect sensitive data. Combining traffic analysis with automated anomaly detection allows organizations to proactively identify suspicious behavior, investigate incidents efficiently, and strengthen overall network security. Therefore, A is the correct answer.
Question 91
Which strategy best ensures threat intelligence feeds improve proactive threat detection across multiple security platforms?
A) Integrate with SIEM and correlation engines
B) Rely on periodic manual threat feed updates
C) Only monitor social media threat feeds
D) Use isolated standalone threat intelligence tools
Answer: A
Explanation:
Integrating threat intelligence feeds with SIEM and correlation engines is the most effective strategy for proactive threat detection because it allows organizations to centralize alerts, analyze patterns, and respond to potential threats in real-time. Threat intelligence feeds provide information on emerging malware, phishing campaigns, IP reputation, malicious domains, and indicators of compromise (IOCs). However, if these feeds are not integrated with operational security tools like SIEM, EDR, and network monitoring platforms, the intelligence remains passive and cannot be leveraged to prevent attacks. Option B, relying on periodic manual updates, is reactive and introduces latency between threat emergence and detection. Option C, monitoring only social media feeds, limits visibility and misses a wide spectrum of actionable intelligence. Option D, using standalone tools, fails to correlate events across endpoints, networks, and applications, reducing detection accuracy. When integrated with a SIEM, threat intelligence can trigger automated alerts, correlate suspicious activity across multiple data sources, and prioritize response efforts. Security teams can leverage machine learning and UEBA to identify anomalies, quickly detect credential misuse, malware propagation, and lateral movement. Integration supports compliance with frameworks such as NIST CSF, ISO 27001, PCI DSS, and HIPAA, which emphasize proactive monitoring, real-time threat detection, and evidence-based response procedures. Additionally, automated enrichment of logs with threat intelligence enables rapid containment of attacks and improved incident response times. A holistic threat intelligence integration strategy ensures continuous visibility, actionable insights, and measurable improvement in an organization’s security posture. Therefore, A is the correct answer.
Question 92
Which method is most effective for reducing false positives in anomaly-based intrusion detection systems?
A) Continuous tuning of detection rules using baseline behavior
B) Disabling alerts for all minor anomalies
C) Relying exclusively on signature-based detection
D) Periodically restarting intrusion detection software
Answer: A
Explanation:
Continuous tuning of detection rules using baseline behavior is the most effective approach to reduce false positives in anomaly-based intrusion detection systems (IDS). Anomaly-based IDS detect deviations from normal activity, such as unusual login times, abnormal data transfers, or uncommon network protocols. Without tuning, the IDS may flag legitimate activity as suspicious, overwhelming analysts with alerts and reducing operational efficiency. Option B, disabling alerts for minor anomalies, risks missing true threats and reduces overall security effectiveness. Option C, relying only on signature-based detection, fails to detect unknown attacks or zero-day exploits. Option D, periodically restarting the software, does not impact detection accuracy and may disrupt monitoring. Effective tuning involves analyzing historical network traffic, identifying normal patterns for users, devices, and applications, and continuously adjusting thresholds and rules. Integrating IDS with UEBA, SIEM, and automated response systems enhances detection and ensures that alerts are prioritized based on risk severity. Reducing false positives also allows security analysts to focus on genuine incidents, improving response times and incident management efficiency. Additionally, accurate anomaly detection supports compliance with regulatory standards such as PCI DSS, HIPAA, and NIST CSF, which mandate monitoring for unusual activity and incident response preparedness. Implementing machine learning models that learn from normal behavior and adapt over time further minimizes false positives, enabling organizations to maintain strong security posture without overwhelming analysts. Therefore, A is the correct answer.
Question 93
Which technique is most effective in correlating logs from multiple endpoints to detect coordinated attacks?
A) Centralized SIEM log aggregation with correlation rules
B) Manual log comparison by analysts
C) Periodic firewall log review
D) Standalone endpoint antivirus reporting
Answer: A
Explanation:
Centralized SIEM log aggregation with correlation rules is the most effective method for detecting coordinated attacks across multiple endpoints. Coordinated attacks often involve subtle, distributed actions, such as simultaneous login attempts, lateral movement, and malware propagation. Without centralized correlation, these indicators may appear unrelated when examined individually. Option B, manual log comparison, is time-consuming, prone to human error, and infeasible for large-scale networks. Option C, periodic firewall log review, focuses on network traffic and does not correlate endpoint-specific behaviors. Option D, standalone antivirus reporting, only identifies malware infections on individual systems, missing broader attack patterns. A SIEM aggregates logs from endpoints, network devices, servers, and applications, applying correlation rules to detect suspicious sequences of events. This enables analysts to identify indicators such as brute-force attacks, privilege escalation, and unusual access attempts that span multiple systems. Integration with UEBA enhances detection by identifying anomalous user behavior that may indicate a coordinated attack. Regulatory frameworks such as NIST SP 800-53, ISO 27001, and PCI DSS emphasize log aggregation, analysis, and incident correlation as key components of a mature security program. Effective SIEM implementation reduces mean time to detection (MTTD) and mean time to response (MTTR), enabling proactive containment of threats and minimizing organizational risk. Continuous refinement of correlation rules, along with real-time dashboards and automated alerting, strengthens overall situational awareness, ensuring that coordinated attacks are promptly detected and mitigated. Therefore, A is the correct answer.
Question 94
Which control is most effective in preventing data exfiltration via removable media?
A) Endpoint DLP solutions with device control policies
B) Antivirus scanning of removable drives
C) Annual user security awareness training
D) Disabling USB ports physically without monitoring
Answer: A
Explanation:
Endpoint DLP (Data Loss Prevention) solutions with device control policies are the most effective control to prevent data exfiltration through removable media. DLP solutions monitor and control the transfer of sensitive data to USB drives, external hard drives, and other portable storage devices. They enforce policies that restrict which users or groups can copy data, log attempts, and generate alerts for suspicious activity. Option B, antivirus scanning, only detects known malware and does not prevent intentional exfiltration of legitimate data. Option C, annual training, improves awareness but cannot enforce real-time prevention. Option D, physically disabling USB ports, may stop unauthorized copying but lacks monitoring, policy enforcement, and auditing capabilities. Endpoint DLP solutions also integrate with SIEM platforms to provide centralized reporting and real-time alerts, supporting incident response and forensic investigations. Organizations can apply content inspection, file fingerprinting, encryption enforcement, and policy-based restrictions to ensure compliance with regulatory standards such as HIPAA, GDPR, PCI DSS, and SOX, which mandate protection of sensitive data. By implementing endpoint DLP, organizations significantly reduce the risk of data breaches, maintain visibility over sensitive information, and enforce governance policies across distributed environments. Combining endpoint DLP with continuous monitoring, anomaly detection, and user activity analytics provides a robust defense against insider threats and accidental data leaks. Therefore, A is the correct answer.
Question 95
Which analysis method is most effective for detecting ransomware before it encrypts critical files?
A) Behavior-based endpoint detection with real-time monitoring
B) Signature-based antivirus scanning
C) Scheduled vulnerability assessments
D) Manual file integrity checks
Answer: A
Explanation:
Behavior-based endpoint detection with real-time monitoring is the most effective method for detecting ransomware before it encrypts critical files. Ransomware often bypasses signature-based antivirus tools because new variants or zero-day ransomware may not have known signatures. Option B, signature-based antivirus scanning, is reactive and cannot detect unknown threats effectively. Option C, scheduled vulnerability assessments, identify system weaknesses but do not detect active ransomware execution. Option D, manual file integrity checks, are too slow to prevent rapid file encryption. Behavior-based detection monitors process creation, file system changes, registry modifications, unusual encryption patterns, and abnormal network connections in real-time. When a process exhibits suspicious behavior indicative of ransomware, such as rapid file encryption or deletion of backups, endpoint detection and response (EDR) systems can immediately isolate the affected endpoint, block execution, and alert security teams. Integration with SIEM and threat intelligence platforms enhances detection by correlating ransomware activity with other indicators of compromise across the environment. Behavior-based monitoring also supports incident response playbooks, enabling organizations to contain ransomware before it spreads laterally, mitigating operational disruption and potential financial loss. Compliance frameworks like NIST CSF, ISO 27001, HIPAA, and PCI DSS require proactive detection and prevention of malware, making behavior-based detection an essential component of modern cybersecurity defense. This approach ensures continuous protection against evolving ransomware threats while maintaining business continuity. Therefore, A is the correct answer.
Question 96
Which approach is most effective for identifying privilege escalation attempts on critical servers in real time?
A) Implementing EDR with behavior analytics and continuous monitoring
B) Periodic manual review of admin logs
C) Relying solely on firewall alerts
D) Conducting quarterly vulnerability scans
Answer: A
Explanation:
Implementing EDR (Endpoint Detection and Response) with behavior analytics and continuous monitoring is the most effective strategy for identifying privilege escalation attempts on critical servers in real time. Privilege escalation often occurs when attackers exploit misconfigurations, software vulnerabilities, or weak credentials to gain administrative access and move laterally. Option B, periodic manual review of admin logs, is insufficient because it is reactive, labor-intensive, and often too slow to detect ongoing attacks. Option C, relying solely on firewall alerts, provides visibility only into network traffic and cannot detect attacks executed directly on endpoints. Option D, quarterly vulnerability scans, may identify potential weaknesses but cannot detect real-time exploitation or malicious activity.
EDR solutions combine real-time monitoring, advanced analytics, and automated alerts to detect suspicious activities such as creation of new administrative accounts, modification of privileged user groups, or execution of commands that bypass standard privilege boundaries. Behavior analytics identify deviations from baseline patterns, such as unusual command sequences or abnormal access times. Integration with SIEM platforms enhances correlation of alerts across endpoints and servers, enabling rapid identification of coordinated attacks. EDR solutions also provide automated containment options, such as isolating compromised endpoints or blocking malicious processes, reducing the risk of further compromise.
Privilege escalation detection is particularly important for compliance with standards like NIST SP 800-53, ISO 27001, and PCI DSS, which mandate monitoring and protecting critical systems from unauthorized access. Continuous monitoring ensures attackers cannot exploit escalation attempts undetected, protecting sensitive data, preventing operational disruption, and mitigating the risk of breaches. By implementing EDR with behavior analytics, organizations gain granular visibility into user and system activity, enabling proactive threat detection, improved incident response, and reduced dwell time. Therefore, A is the correct answer.
Question 97
Which method is most effective for detecting abnormal network traffic indicative of a distributed denial-of-service (DDoS) attack?
A) Real-time network traffic analysis with anomaly detection
B) Manual firewall log reviews
C) Endpoint antivirus monitoring
D) Periodic bandwidth usage reports
Answer: A
Explanation:
Real-time network traffic analysis with anomaly detection is the most effective method for detecting abnormal traffic patterns indicative of a distributed denial-of-service (DDoS) attack. DDoS attacks flood targeted systems with excessive traffic, potentially causing service outages and affecting operational continuity. Traditional methods, such as manual log review or periodic bandwidth reports, are reactive, slow, and cannot scale to detect rapidly evolving attacks. Option B, manual firewall log reviews, may provide some insight but are labor-intensive and cannot detect volumetric attacks in real time. Option C, endpoint antivirus monitoring, is designed to detect malware and does not provide visibility into network traffic anomalies. Option D, periodic bandwidth usage reports, is retrospective and insufficient for timely detection.
Anomaly detection systems continuously monitor traffic patterns, volume, packet types, and connection frequencies to identify deviations from baseline behavior. Sophisticated solutions leverage machine learning algorithms to distinguish between legitimate traffic spikes and malicious activity. When abnormal patterns are detected, alerts can trigger automated mitigation actions, such as rate-limiting, traffic filtering, or redirecting traffic to scrubbing centers. Integration with SIEM or network monitoring platforms allows correlation with other indicators, such as source IP reputation, DNS anomalies, or unusual protocol usage, providing context for response. Detecting DDoS attacks early minimizes operational disruption, protects critical services, and reduces financial and reputational damage.
Real-time detection also aligns with industry frameworks like NIST CSF, ISO 27001, and CIS Controls, which emphasize continuous monitoring and incident response for cyber threats. Organizations can implement proactive strategies, such as threat intelligence feeds, anomaly detection thresholds, and automated mitigation workflows, to reduce the risk of DDoS impact. By leveraging anomaly-based network monitoring, security teams gain visibility into potential attack vectors, can respond rapidly to mitigate threats, and ensure business continuity. Therefore, A is the correct answer.
Question 98
Which control is most effective in identifying and preventing data manipulation by malicious insiders in critical databases?
A) Database activity monitoring (DAM) with anomaly detection
B) Periodic manual database audits
C) Antivirus scanning of database servers
D) Annual user access review
Answer: A
Explanation:
Database activity monitoring (DAM) with anomaly detection is the most effective control for identifying and preventing data manipulation by malicious insiders. Malicious insiders can exploit legitimate access to modify, delete, or exfiltrate sensitive information in critical databases. Option B, periodic manual database audits, are too infrequent and often fail to detect ongoing manipulations in real time. Option C, antivirus scanning of database servers, only identifies known malware and cannot detect insider-driven unauthorized changes. Option D, annual user access reviews, verify permissions but do not provide continuous monitoring of activity.
DAM solutions capture detailed audit trails of database transactions, including read, write, update, and delete operations. These solutions employ anomaly detection and machine learning to flag suspicious activity, such as unusually large data exports, access at odd hours, or unauthorized privilege escalation. Integration with SIEM platforms allows correlation with other logs, such as authentication events and network activity, enhancing detection accuracy. Alerts generated by DAM systems can trigger automated preventive measures, such as session termination, encryption, or workflow escalation to security teams.
Monitoring critical databases aligns with regulatory compliance requirements, including HIPAA, PCI DSS, GDPR, and SOX, which mandate safeguarding sensitive information and ensuring data integrity. By employing DAM with anomaly detection, organizations can detect insider threats before they cause substantial damage, maintain data integrity, and prevent potential breaches. Additionally, proactive monitoring supports forensic investigations, providing detailed evidence of malicious activity for legal and internal accountability. Continuous database activity monitoring strengthens overall cybersecurity posture, mitigates insider risks, and ensures that critical enterprise data remains secure, trustworthy, and auditable. Therefore, A is the correct answer.
Question 99
Which method is most effective for detecting lateral movement within an enterprise network before critical assets are compromised?
A) Network traffic analysis with anomaly-based detection and UEBA integration
B) Periodic vulnerability scanning
C) Manual firewall log review
D) Standalone antivirus on endpoints
Answer: A
Explanation:
Network traffic analysis with anomaly-based detection and UEBA integration is the most effective approach for detecting lateral movement within an enterprise network before critical assets are compromised. Lateral movement occurs when attackers, having gained initial access, traverse internal networks to escalate privileges or exfiltrate data. Detection is challenging because attackers often use legitimate credentials, standard protocols, and legitimate administrative tools, making traditional security measures less effective. Option B, periodic vulnerability scanning, identifies weaknesses but cannot detect active movement or malicious behavior. Option C, manual firewall log review, is reactive, labor-intensive, and unlikely to correlate complex patterns of lateral movement. Option D, standalone antivirus on endpoints, focuses on malware detection and does not monitor network traffic or user behaviors across systems.
Network traffic analysis involves capturing and inspecting internal communications, identifying anomalies such as unusual authentication attempts, abnormal SMB activity, atypical RDP sessions, or unexpected data transfers between systems. UEBA (User and Entity Behavior Analytics) enhances this by establishing a baseline of normal user and system behavior, then flagging deviations that could indicate compromised accounts or insider threats. By combining anomaly-based network monitoring with UEBA, security teams gain visibility into the subtle, stepwise actions of attackers attempting to move laterally, allowing proactive mitigation before high-value systems are impacted.
Integration with SIEM platforms improves detection through correlation of multiple data sources, enabling analysts to see patterns across endpoints, servers, applications, and network devices. This unified approach reduces dwell time and helps organizations respond more quickly to attacks, minimizing the risk of data breaches, intellectual property theft, and operational disruption. Continuous monitoring also aligns with regulatory and compliance requirements such as NIST CSF, ISO 27001, PCI DSS, and HIPAA, which emphasize real-time monitoring, threat detection, and incident response.
Behavioral detection algorithms can identify suspicious patterns, including unusual login times, excessive credential use, or unexpected application access, enabling early intervention. Alerting and automated response measures, such as session isolation, account lockdown, or network segmentation, can be triggered immediately, reducing the potential impact of attacks. By employing anomaly-based network traffic analysis with UEBA integration, organizations can proactively detect lateral movement, strengthen overall cybersecurity posture, and ensure critical assets remain secure. Therefore, A is the correct answer.
Question 100
Which practice is most effective for ensuring continuous compliance and reducing risks in cloud security environments?
A) Continuous monitoring with automated compliance checks and cloud security posture management
B) Annual cloud configuration reviews
C) Manual inspection of access logs
D) Endpoint antivirus on cloud-connected devices
Answer: A
Explanation:
Continuous monitoring with automated compliance checks and cloud security posture management (CSPM) is the most effective practice for ensuring ongoing compliance and reducing risks in cloud security environments. Modern organizations increasingly rely on cloud services, making visibility and control across distributed and dynamic environments critical. Traditional approaches, such as annual configuration reviews or manual log inspections, are insufficient due to the speed at which cloud environments change and the sophistication of potential attacks. Option B, annual cloud configuration reviews, are periodic and do not provide real-time insights into configuration drift or emerging vulnerabilities. Option C, manual inspection of access logs, is time-consuming, prone to human error, and lacks scalability in complex cloud environments. Option D, endpoint antivirus on cloud-connected devices, protects endpoints but does not monitor cloud configurations, user permissions, or misconfigured workloads.
CSPM solutions continuously assess cloud resources for misconfigurations, enforce security policies, and provide automated alerts for non-compliance. They monitor configurations across IaaS, PaaS, and SaaS platforms, detect excessive privileges, exposed storage buckets, unencrypted data, and insecure network configurations. By integrating automated compliance checks, organizations can ensure adherence to regulatory standards such as PCI DSS, GDPR, HIPAA, SOC 2, and ISO 27017, which mandate secure cloud operations, proper data handling, and continuous risk management. Continuous monitoring also supports incident response by providing real-time visibility into changes, anomalies, and potential threats, enabling security teams to quickly remediate issues before they escalate.
Additionally, CSPM facilitates proactive risk management by identifying trends in misconfigurations, enabling automated remediation, and ensuring that security policies are consistently applied across all cloud accounts. Security teams gain visibility into shadow IT, unauthorized access, and policy violations, which reduces the likelihood of breaches caused by human error or misconfigured cloud resources. Integration with SIEM platforms allows correlation with other threat intelligence and endpoint data, enhancing detection and response capabilities. Organizations adopting continuous monitoring with automated compliance checks can enforce best practices such as least privilege access, encryption enforcement, and multi-factor authentication across cloud services.
By employing CSPM and automated monitoring, organizations strengthen cloud security posture, reduce the risk of misconfigurations and insider threats, maintain continuous compliance with regulatory frameworks, and gain actionable insights for rapid remediation. This approach ensures that security teams can proactively detect and address vulnerabilities, minimize operational disruption, and maintain trust with stakeholders and customers. Therefore, A is the correct answer.
