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Dynamic Application Security Testing (DAST) is crucial for identifying and addressing security vulnerabilities during the software development life cycle (SDLC). Ensuring that DAST scans are routinely scheduled helps in maintaining a secure development process.

Why Routine DAST Scans?

Continuous Security Assessment: Regular DAST scans help in identifying vulnerabilities in real-time, ensuring they are addressed promptly.

Compliance: Routine scans ensure that the development process complies with security standards and regulations.

Proactive Threat Mitigation: Regular scans help in early detection and mitigation of potential security threats, reducing the risk of breaches.

Integration into SDLC: Ensures security is embedded within the development process, promoting a security-first approach.

Other options, while relevant, do not directly address the continuous assessment and proactive identification of threats:

A. If developers are unable to promote to production: This is more of an operational issue than a security assessment.

B. If DAST code is being stored to a single code repository: This concerns code management rather than security testing frequency.

D. If role-based training is deployed: While important, training alone does not ensure continuous security assessment.

Understanding the Ransomware Issue:

The key issue here is thatbackups were not recoverable within the required RPO timeframe.

This means the organizationdid not properly testitsbackup and disaster recovery (DR) processes.

To prevent this from happening again, regular disaster recovery testing is essential.

Why Option C is Correct:

Disaster recovery testing ensures that backups are functionaland can meetbusiness continuity needs.

Frequent DR testingallows organizations to identify and fixgaps in recovery strategies.

Regular testing ensuresthat recoverymeets the RPO & RTO (Recovery Time Objective) requirements.

Why Other Options Are Incorrect:

A (Encrypt & label backup tapes):While encryption is important, it does not address thefailure to meet RPO requirements.

B (Reverting to manual business processes):While amanual continuity planis good for resilience, it doesnot resolve the backup and recovery failure.

D (Tabletop exercise & RACI matrix):Atabletop exerciseis a planning activity, butit does not involve actual recovery testing.

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.

Code-signing within theCI/CD pipelineensures that only verified and signed code is deployed, mitigating the risk of supply chain attacks. Updating Python withaptitudeand updating modules withpipensures vulnerabilities are patched. Deploying the solution to production after testing maintains application availability while securing the development lifecycle.

Branch protection (B)applies only to version-controlled environments, which is not the case here.

NFS network share (C)does not address the deprecated Python vulnerability.

Version designation (D)does not eliminate security risks from outdated dependencies.

The vulnerability lies in line [10], where the function strcpy(transmit, input) is used. The strcpy function does not perform boundary checking when copying strings. Since input is defined with a size of 256 characters and transmit only has 20 characters allocated, the strcpy operation will cause a buffer overflow when the contents of input exceed the allocated size of transmit. This creates a significant security vulnerability, as attackers can overwrite adjacent memory, potentially injecting malicious code or altering program execution.

Lines [02], [04], [07], and [08] are not inherently vulnerable by themselves. Line [04] defines the oversized input, but the vulnerability only materializes when combined with the unsafe copy in line [10]. Secure coding practices recommend using safer alternatives like strncpy, which includes a length parameter, or implementing runtime checks to ensure the destination buffer size is not exceeded.

Thus, the vulnerable line that must be changed is line [10], where strcpy is used.

The best solution to harden a three-tier environment (web, database, and application servers) is to implement microsegmentation on the server VLANs. Here’s why:

Enhanced Security: Microsegmentation creates granular security zones within the data center, allowing for more precise control over east-west traffic between servers. This helps prevent lateral movement by attackers who may gain access to one part of the network.

Isolation of Tiers: By segmenting the web, database, and application servers, the organization can apply specific security policies and controls to each segment, reducing the risk of cross-tier attacks.

Compliance and Best Practices: Microsegmentation aligns with best practices for network security and helps meet compliance requirements by ensuring that sensitive data and systems are properly isolated and protected.

The best approach is to use Terraform scripts while creating golden images (A). Terraform is an Infrastructure as Code (IaC) tool that allows organizations to automate infrastructure deployment consistently across environments. A golden image is a pre-configured, patched, and hardened system image used as a standard baseline. By creating golden images via Terraform scripts, the company ensures that every microservice instance is deployed on an already-updated and secure OS. This eliminates the need for repeatedly patching the base OS before code deployment.

Option B (cron job with apt-update) applies patches but introduces delays (every 30 days) and lacks consistency across new deployments. Option C (snapshots) saves deployment states but risks replicating outdated or unpatched images. Option D (centralized update server) is useful but still requires updates post-deployment, which slows the rollout of microservices.

By automating golden image creation through Terraform, the company gains efficiency, repeatability, and security assurance, aligning with DevSecOps principles and CAS-005 cloud-native best practices.