CAS-005 Exam Dumps - CompTIA SecurityX Certification Exam

The issue is tracing the original source of requests in a tiered architecture with a load balancer. The web server logs show internal IPs (192.168.1.10), not the external client IPs, because the load balancer forwards requests without preserving the source. Enabling theX-Forwarded-Forheader on the load balancer adds the client’s original IP to the HTTP request headers, allowing downstream servers to log it. This ensures traceability without altering the architecture significantly.

Option A:Correct—X-Forwarded-For is the standard solution for preserving client IPs through load balancers.

Option B:A Host-based Intrusion Detection System (HIDS) detects anomalies but doesn’t address IP traceability.

Option C:A trusted CA certificate fixes the self-signed warning but is unrelated to source tracking.

Option D:Stored procedures improve database security but don’t help with IP logging.

Option E:Storing $_SERVER['REMOTE_ADDR'] captures the loadbalancer’s IP, not the client’s, unless X-Forwarded-For is enabled.

Attack surface reductionfocuses on minimizingunnecessary services, open ports, and vulnerabilities, reducing the exposure to potential adversaries. This aligns withzero trust and least privilege principles.

Secure Boot (A)helps ensure system integrity but does not minimize exposed services.

Disabling third-party integrations (C)may help, but broader attack surface reduction is the best first step.

Limiting access (D)is important but does not directly reduce exposed services.

After applying mitigations that reduce the likelihood of a risk’s impact, the next step is toassess the residual risk—the risk that remains after controls are implemented. This ensures the organization understands if the mitigation is sufficient or if further action is needed, aligning with risk management best practices.

Option A:Correct—residual risk assessment is the logical next step to evaluate the effectiveness of mitigations.

Option B:Updating the threat model might follow but isn’t immediate; residual risk comes first.

Option C:Moving to the next risk skips evaluating the current mitigation’s success.

Option D:Recalculating impact magnitude is part of residual risk assessment but isn’t the full process.

Homomorphic encryption allows computations to be performed on encrypted data without decrypting it, providing strong privacy guarantees. However, the adoption of homomorphic encryption is challenging due to several factors:

A. Incomplete mathematical primitives: This is not the primary barrier as the theoretical foundations of homomorphic encryption are well-developed.

B. No use cases to drive adoption: There are several compelling use cases for homomorphic encryption, especially in privacy-sensitive fields like healthcare and finance.

C. Quantum computers not yet capable: Quantum computing is not directly related to the challenges of adopting homomorphic encryption.

D. Insufficient coprocessor support: The computational overhead of homomorphic encryption is significant, requiring substantial processing power. Current general-purpose processors are not optimized for the intensive computations required by homomorphic encryption, limiting its practical deployment. Specialized hardware or coprocessors designed to handle these computations more efficiently are not yet widely available.

Malware that does not execute in a sandbox environment often contains anti-analysis techniques, such as anti-virtualization code. This code detects when the malware is running in a virtualized environment and alters its behavior to avoid detection. Checking for anti-virtualization code is a logical next step because:

It helps determine if the malware is designed to evade analysis tools.

Identifying such code can provide insights into themalware's behavior and intent.

This step can also inform further analysis methods, such as running the malware on physical hardware.

The best way to automate reporting from disparate security appliances that do not currently communicate is to configure an API Integration to aggregate the different data sets. Here's why:

Interoperability: APIs allow different systems to communicate and share data, even if they were not originally designed to work together. This enables the integration of various security appliances into a unified reporting system.

Automation: API integrations can automate the process of data collection, aggregation, and reporting, reducing manual effort and increasing efficiency.

Scalability: APIs provide a scalable solution that can easily be extended to include additional security appliances or data sources as needed.

Encrypting patient data at rest ensures that sensitive information is protected from unauthorized access, thereby maintaining patient privacy. Additionally, encryption supports data portability byallowing secure transfer and storage of data across different systems and devices without compromising confidentiality. This practice is crucial for healthcare providers to comply with regulations such as the Health Insurance Portability and Accountability Act (HIPAA), which mandates the protection of patient information.​

To ensure that logs from a legacy platform are properly retained beyond the default retention period, configuring the SIEM to aggregate the logs is the best approach. SIEM solutions are designed to collect, aggregate, and store logs from various sources, providing centralized log management and retention. This setup ensures that logs are retained according to policy and can be easily accessed for analysis and compliance purposes.