CAS-005 Exam Dumps - CompTIA SecurityX Certification Exam

The logs indicate multiple failed login attempts for user10, who may have been part of the staff reduction 60 days prior. If user10 ' s account was removed, and their home directory deleted, any applications or services relying on files or configurations within that directory would fail. This scenario is common when service accounts are not properly identified and preserved during staff reductions.

Ensuring that service accounts are documented and maintained separately from user accounts is essential to prevent unintended disruptions to applications and services.

Incident response follows a standard process (e.g., NIST 800-61): Preparation, Identification, Containment, Eradication, Recovery, Lessons Learned. After identifying the attack (file and origin), the next step isContainment—limiting the spread or impact (e.g., isolating systems) before remediation or recovery.

Option A:Remediation (fixing the root cause) follows containment.

Option B:Correct—containment prevents further damage post-identification.

Option C:“Response” is too vague; it encompasses all steps.

Option D:Recovery (restoring systems) comes after containment and eradication.

To meet the requirements of having allendpoints establish telemetry with a SIEM, integrate into an XDR platform, and allow SOC services to monitor the XDR platform, the best approach is to implement Host Intrusion Prevention Systems (HIPS) and a host-based firewall. HIPS can provide detailed telemetry data to the SIEM and can be integrated into the XDR platform for comprehensive monitoring and response. The host-based firewall ensures that only authorized traffic is allowed, providing an additional layer of security.

The code snippet exposes a hardcoded access token in a public repository. According to SecurityX CAS-005 secure coding best practices, the immediate action must be to revoke the exposed secret to prevent unauthorized access.

Removing the code from public view without revoking the token leaves the secret still usable by any attacker who has already seen or copied it.

SAST scanning would detect the issue but not mitigate it immediately.

Security awareness training is a long-term prevention measure but does not fix the immediate exposure.Revoking the secret first stops ongoing exploitation, after which the code can be removed, and preventative measures can be implemented.

The incident highlights gaps in visibility, monitoring, and log management that allowed unauthorized access to persist undetected. The most critical corrective actions are to extend log retention for all devices (B) and to ensure all devices are forwarding relevant logs to the SIEM (E). Together, these steps strengthen monitoring and incident detection capabilities by ensuring that sufficient telemetry is collected, stored, and available for correlation and investigation.

Disabling bulk user creation (A) may reduce misuse but does not directly address monitoring gaps. Daily review of the application allow list (C) is operationally impractical and does not provide the breadth of monitoring needed. Routine patching (D) is essential for security hygiene but is separate from monitoring improvements. Configuring firewall rules (F) may reduce traffic flows but does not ensure detection or visibility of unauthorized activity.

By prioritizing comprehensive log collection and ensuring adequate retention, the SOC can correlate anomalies across systems, detect malicious behavior earlier, and conduct forensic investigations effectively. This aligns with CAS-005 best practices for security operations and continuous monitoring in hybrid environments.

When using AI to fully automate the process of deciding client loan rates, the primary concern from a privacy perspective is model explainability.

Why Model Explainability is Critical:

Transparency: It ensures that the decision-making process of the AI model can be understood and explained to stakeholders, including clients.

Accountability: Helps in identifying biases and errors in the model, ensuring that the AI is making fair and unbiased decisions.

Regulatory Compliance: Various regulations require that decisions, especially those affecting individuals ' financial status, can be explained and justified.

Trust: Builds trust among users and stakeholders by demonstrating that the AI decisions are transparent and justifiable.

Other options, such ascredential theft, prompt injections, and social engineering, are significant concerns but do not directly address the privacy and fairness implications of automated decision-making.

Encrypting patient data at rest ensures that sensitive information is protected from unauthorized access, thereby maintaining patient privacy. Additionally, encryption supports data portability by allowing 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.​

Per SecurityX CAS-005 GRC principles, outsourcing a function does not transfer accountability for protecting personally identifiable information (PII). While subprocessors handle data, the originating organization remains responsible under most data protection laws and frameworks (e.g., GDPR, CCPA).

Due care in procurement (option B) is important, but it is a supporting concept, not the primary driver in this context.

Jurisdictional compliance (option D) is a requirement, but the underlying reason for risk assessment is that accountability for PII protection remains with the organization.