CAS-005 Exam Dumps : CompTIA SecurityX Certification Exam

The “cipher unavailable” message indicates that the client and server could not agree on a common cipher suite. After the OpenSSL update, the server likely dropped support for older, insecure ciphers (such as RC4 or 3DES) that legacy clients still use. The safest remediation is to update or configure the client applications to support modern, secure ciphers such as AES in Galois/Counter Mode (AES-GCM) or an equivalent strong cipher suite that is supported by the updated OpenSSL server.

Option A (SSL 3.0) is unsafe because SSL 3.0 is deprecated and vulnerable to multiple attacks (e.g., POODLE).

Option C (ECB mode) is insecure due to pattern leakage and should never be enforced.

Option D (ECC signatures) relates to key exchange and signatures, not to the “cipher unavailable” issue directly.

This approach aligns with SecurityX CAS-005 cryptographic interoperability guidance—modernize clients rather than reintroduce insecure protocols.

Privileged Access Management (PAM)restricts elevated permissions, reducing the risk of widespread ransomware attacks.Multi-Factor Authentication (MFA)protects against credential theft and ensures that even if passwords are compromised, accounts are not easily accessible.Network segmentationbreaks the flat network into secure zones, limiting lateral movement by attackers. SD-WAN and BGP relate to network routing and efficiency, not security architecture specifically. Remote access VPN secures external access but does not solve internal flat network issues. Network Access Control (NAC) is helpful but secondary compared to PAM, MFA, and segmentation in this context.

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.