Applying baseline configurations immediately to all identified systems without further analysis can lead to unintended consequences, such as system outages or disruptions in service, especially if those systems are currently in production. A full audit of all systems, while thorough, may delay necessary actions and could overwhelm the team with data, making it difficult to prioritize urgent issues. Informing management about the non-compliance issues is important, but waiting for their directive can lead to delays in addressing critical vulnerabilities. Instead, the analyst should take proactive steps to create a structured remediation plan that aligns with organizational policies and compliance requirements. This plan should include timelines, resource allocation, and a communication strategy to keep stakeholders informed throughout the remediation process. By prioritizing actions based on risk and compliance needs, the analyst can effectively manage the remediation efforts while minimizing operational disruptions. In summary, a well-structured remediation plan is essential for addressing configuration discrepancies in a way that aligns with best practices in security management and compliance frameworks. This approach not only ensures that critical systems are addressed promptly but also maintains the integrity and availability of services within the organization.

Updating the firewall rules involves reviewing the current configurations, assessing the existing threat landscape, and aligning the rules with the organization’s security policies. This process should include identifying which services need to be exposed to the internet and ensuring that only necessary ports are open, thereby minimizing the attack surface. Additionally, the analyst should consider implementing best practices such as the principle of least privilege, which restricts access to only those who need it for their job functions. On the other hand, implementing a new intrusion detection system (IDS) without addressing the existing firewall vulnerabilities would be ineffective, as the IDS would still be monitoring a compromised environment. Increasing security personnel without changing the firewall configurations does not address the root cause of the vulnerabilities. Lastly, while employee training on phishing attacks is important, it does not mitigate the immediate risks posed by the outdated firewall rules. Therefore, prioritizing the review and update of the firewall rules is essential for enhancing the overall security posture of the organization and ensuring that it can effectively defend against current and emerging threats.

Implementing access control lists (ACLs) is crucial in this configuration as it enables the network engineer to enforce security policies specific to each department. For instance, the Sales department may need to access certain resources in Engineering but should not have access to HR data. ACLs can be applied to the VLAN interfaces to restrict or allow traffic based on the defined rules, ensuring that sensitive information is protected while still allowing necessary communication. In contrast, using a single VLAN for all departments (option b) would negate the benefits of VLAN segmentation, leading to potential security risks and broadcast storms. Assigning all devices to the same subnet (option c) would also eliminate the advantages of VLANs and complicate security management. Lastly, configuring a router with static routes (option d) without restrictions would allow unrestricted access between departments, which contradicts the goal of maintaining security policies. Therefore, the configuration of a Layer 3 switch with sub-interfaces and ACLs is the most effective solution for this scenario.

Implementing multiple security controls at different layers involves deploying various security measures that work together to create a robust defense. For instance, firewalls serve as the first line of defense by controlling incoming and outgoing network traffic based on predetermined security rules. Intrusion detection systems (IDS) monitor network traffic for suspicious activity and can alert administrators to potential threats. Encryption protocols protect sensitive data both at rest and in transit, ensuring that even if data is intercepted, it remains unreadable without the appropriate decryption keys. In contrast, relying solely on a single advanced firewall (as suggested in option b) creates a significant vulnerability, as it does not account for the possibility of the firewall being bypassed or compromised. Similarly, a comprehensive security policy that neglects network segmentation (option c) fails to provide adequate protection, as it does not limit the lateral movement of attackers within the network. Lastly, establishing a single point of failure (option d) undermines the entire security architecture, as it creates a critical vulnerability that can be exploited by attackers, leading to catastrophic consequences. Thus, the layered security approach, characterized by the integration of various security controls, exemplifies the defense in depth principle, effectively reducing the risk of unauthorized access and enhancing the overall security posture of the organization.

The implications of using asymmetric encryption are significant. Firstly, it provides confidentiality, as only the intended recipient (Bob) can decrypt the message. Secondly, it offers non-repudiation; since only Bob possesses the private key, he cannot deny having received the message. This is crucial in scenarios where the authenticity of the message must be guaranteed. In contrast, if Bob were to use Alice’s public key to decrypt the message, anyone with access to Alice’s public key could read the message, which compromises confidentiality. Similarly, using his public key for decryption is incorrect, as public keys are meant for encryption, not decryption. Lastly, while symmetric encryption is faster, it requires a secure method for key exchange, which asymmetric encryption inherently solves by allowing the public key to be shared openly without compromising security. Thus, the correct sequence of steps for Bob to decrypt the message involves using his private key, which aligns with the principles of asymmetric encryption, ensuring both confidentiality and non-repudiation.

In a Zero Trust model, trust is never assumed, and every access request is treated as if it originates from an untrusted network. This means that micro-segmentation plays a vital role in continuously validating the identity and context of users and devices before granting access to resources. By implementing micro-segmentation, organizations can create granular security policies that limit access to only those users and devices that require it, thereby significantly reducing the risk of data breaches. The other options present misconceptions about micro-segmentation. For instance, simplifying network management by allowing free communication within the same segment contradicts the fundamental principle of Zero Trust, which advocates for strict access controls. Similarly, enhancing application performance by reducing security checks undermines the necessity of continuous authentication and authorization, which are essential in a Zero Trust framework. Lastly, while integrating legacy systems is important, it should not come at the expense of security; thus, the notion that micro-segmentation allows for easier integration without compromising security is misleading. In summary, the essence of micro-segmentation in a Zero Trust Architecture is to enforce strict access controls and minimize the attack surface, thereby enhancing overall security posture.

While the number of nodes in the network can influence decentralization and security, it does not directly address the efficiency of transaction processing. A higher number of nodes can lead to increased redundancy and security but may also slow down the consensus process if not managed properly. Similarly, the type of cryptographic algorithms used is essential for security but does not inherently affect scalability. For example, while advanced algorithms can enhance security, they may also introduce complexity that could slow down transaction verification. Lastly, the user interface design, while important for user experience, does not impact the underlying performance of the blockchain system. It is crucial for ensuring that users can interact with the system effectively, but it does not influence how well the system can scale to accommodate increased transaction loads. In summary, the council should prioritize the consensus mechanism as it directly affects the scalability and efficiency of the blockchain system in handling future demands, ensuring that the smart city can operate smoothly as it grows.

On the other hand, increasing the sensitivity of the SIEM (option b) may lead to an overwhelming number of alerts, many of which could be irrelevant, thereby exacerbating the false positive issue. Reducing the logging level on application servers (option c) would limit the data available for analysis, potentially omitting critical events that could indicate a security incident. Lastly, configuring the SIEM to ignore alerts from the firewall (option d) undermines the purpose of having a comprehensive security monitoring system, as firewalls often provide essential information about unauthorized access attempts and other critical security events. Thus, the most effective approach to reduce false positives while maintaining the detection of genuine threats is to implement a correlation rule that combines events from multiple sources, thereby enhancing the reliability of the alerts generated by the SIEM. This method aligns with best practices in security monitoring, emphasizing the importance of context and corroboration in threat detection.

In this scenario, the organization has decided to flag anomalies that exceed 2 standard deviations above the mean. The formula to calculate the threshold for anomalies can be expressed as: \[ \text{Threshold} = \text{Mean} + (k \times \text{Standard Deviation}) \] where \( k \) is the number of standard deviations above the mean that we want to consider for anomaly detection. Here, \( k = 2 \). Substituting the values into the formula: \[ \text{Threshold} = 200 + (2 \times 30) = 200 + 60 = 260 \text{ requests per minute} \] This means that any traffic exceeding 260 requests per minute would be flagged as anomalous by the AI system. Understanding this concept is crucial in cybersecurity operations, as it allows organizations to effectively utilize AI for real-time threat detection. By setting appropriate thresholds based on statistical analysis, organizations can minimize false positives while ensuring that genuine threats are identified promptly. This approach not only enhances the efficiency of the security operations center but also aids in the proactive management of potential security incidents. In contrast, the other options represent different interpretations of the statistical data. For instance, 230 requests per minute would only account for one standard deviation above the mean, while 290 and 300 requests per minute would be excessively high thresholds that could lead to missed detections of actual anomalies. Thus, the correct threshold for flagging anomalies in this context is 260 requests per minute.

Adjusting the model’s classification threshold can indeed reduce the false negative rate; however, this often comes at the cost of increasing false positives. While this might seem like a quick fix, it can lead to user fatigue and decreased trust in the system if legitimate emails are frequently misclassified as phishing. Implementing a rule-based system alongside the machine learning model can be beneficial as a supplementary measure, but it does not directly address the underlying issue of the model’s learning capability. This approach may help catch some phishing emails that the model misses, but it does not improve the model itself. Focusing solely on improving accuracy without considering the balance between precision and recall is a flawed strategy. In cybersecurity, especially in phishing detection, it is crucial to maintain a balance between identifying as many threats as possible (recall) while minimizing the number of legitimate emails incorrectly flagged as threats (precision). Therefore, the most effective strategy is to enhance the training dataset with more labeled phishing emails, which will lead to a more robust and effective model in the long run.

In contrast, increasing the number of employees with access to sensitive data (option b) would exacerbate the confidentiality issue, as it broadens the attack surface and increases the risk of data breaches. Allowing all employees to access sensitive data during training sessions (option c) undermines confidentiality by exposing sensitive information to individuals who do not require it for their roles. Lastly, using a single password for all employees (option d) creates a significant security risk, as it makes it easier for unauthorized individuals to gain access if the password is compromised. In summary, the implementation of RBAC not only enhances confidentiality but also supports compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), which mandate strict access controls to protect sensitive information. By ensuring that access is granted based on clearly defined roles, the institution can significantly mitigate the risk of unauthorized access and maintain the integrity of its data security framework.

For a financial institution, the consequences of a data breach can be severe, including financial losses, reputational damage, and regulatory penalties. Therefore, a proactive stance through risk mitigation is essential. This approach aligns with the principles outlined in frameworks such as NIST SP 800-53, which emphasizes the importance of safeguarding information systems through a combination of technical, administrative, and physical controls. On the other hand, risk avoidance, while appealing, may not always be feasible. Completely avoiding certain risks could lead to operational inefficiencies or the inability to provide essential services. Risk transfer, such as purchasing insurance, can help manage financial repercussions but does not eliminate the risk itself. Lastly, risk acceptance may be appropriate in scenarios where the cost of mitigation exceeds the potential impact of the risk; however, this should be approached with caution, particularly in a regulated environment. In summary, prioritizing risk mitigation allows the financial institution to implement a balanced approach that not only protects sensitive customer data but also ensures compliance with regulations like GDPR and PCI DSS, ultimately fostering trust and reliability in their services.

Additionally, the RCA process should include a detailed examination of the incident’s context, including the specific conditions that allowed the breach to occur. This involves analyzing the logs and understanding the sequence of events that led to the unauthorized access. It is also essential to engage in discussions with relevant stakeholders to gather insights and perspectives that may not be captured in automated reports. Focusing solely on immediate fixes, as suggested in option b, neglects the importance of understanding the broader implications of the incident and can lead to recurring issues. Similarly, implementing new tools without addressing existing vulnerabilities or processes, as indicated in option c, may provide a false sense of security. Lastly, relying solely on automated tools, as mentioned in option d, can result in overlooking critical contextual information that is necessary for a comprehensive understanding of the incident. In summary, prioritizing a thorough review of the firewall configuration and establishing a change management process is essential for effectively addressing the root cause of the breach and enhancing the overall security posture of the organization. This approach not only mitigates the immediate risk but also fosters a culture of continuous improvement in security practices.

On the other hand, the customer retains responsibility for securing their applications and data that reside within the cloud environment. This includes configuring security settings, managing access controls, and ensuring that data is encrypted both in transit and at rest. The customer must also implement security measures such as identity and access management (IAM) policies, which dictate who can access what resources and under what conditions. The other options presented do not fall under the customer’s responsibilities. Maintaining physical security of the data center is solely the provider’s responsibility, as they control the physical premises where the servers are located. Similarly, ensuring the cloud provider’s network is secure and managing the hardware lifecycle of the cloud infrastructure are also responsibilities that lie with the provider, as they own and operate the infrastructure. Understanding the shared responsibility model is crucial for organizations to effectively manage their security posture in the cloud. It helps them identify which security measures they need to implement and where they can rely on the cloud provider’s security capabilities. This knowledge is particularly important for compliance with regulations such as GDPR, HIPAA, or PCI-DSS, which may impose specific security requirements on the customer’s applications and data management practices.

This number is astronomically large, approximately $1.1579209 \times 10^{77}$, which is often expressed in scientific notation as approximately $10^{51}$ years when considering the time it would take to test all keys at a rate of 1 billion keys per second. To calculate the time required to exhaust all possible keys, we can use the following formula: \[ \text{Time (in seconds)} = \frac{\text{Total Keys}}{\text{Keys per second}} = \frac{2^{256}}{10^9} \] This results in a time frame that is impractically long, far exceeding the age of the universe, which is about $13.8$ billion years. In contrast, the other options present significantly lower key lengths, which would not provide the same level of security. For instance, a 128-bit key ($2^{128}$) is considered secure but is vulnerable to advancements in computational power and cryptanalysis techniques. A 512-bit key ($2^{512}$) and a 64-bit key ($2^{64}$) are also not practical for modern encryption standards, as they can be broken with current technology in a feasible time frame. Thus, the correct understanding of the implications of key length in symmetric encryption is crucial for maintaining the confidentiality and integrity of secure communications.

In contrast, relying solely on firewall logs (option b) may provide limited insight, as these logs typically capture only the traffic flow without context regarding user behavior or the intent behind the traffic. Additionally, conducting a one-time review of the IP address against known threat intelligence feeds (option c) lacks depth, as it does not consider the broader context of user activity or the possibility of new, previously unknown threats. Finally, ignoring the spike in traffic (option d) is a dangerous approach, as it dismisses potential threats based solely on the absence of known signatures, which can lead to undetected compromises. In summary, a robust threat hunting strategy should incorporate behavioral analysis to provide a nuanced understanding of user activity and network behavior, enabling the team to make informed decisions about potential threats. This approach aligns with best practices in cybersecurity, emphasizing the importance of context and continuous monitoring in threat detection and response.

Furthermore, using a separate firewall to control traffic between the DMZ and the internal network enhances security by enforcing strict access controls. This configuration allows for granular policies that can limit which services and protocols are allowed to communicate with the internal network, thereby reducing the attack surface. For instance, only specific ports and protocols necessary for the email and DNS servers to function can be allowed, while all other traffic can be blocked. In contrast, directly connecting the web server to the internal network (option b) exposes the internal network to potential threats from the web server, which is inherently less secure due to its public-facing nature. Using a single firewall without segmentation (option c) can lead to a single point of failure and does not provide the necessary isolation between the DMZ and the internal network. Lastly, placing all servers in the DMZ without additional security measures (option d) undermines the purpose of the DMZ itself, as it would not provide any real protection against external threats. Thus, the best approach is to implement a reverse proxy and a separate firewall, ensuring that the DMZ serves its intended purpose of protecting the internal network while allowing necessary services to function securely.

When a company migrates its applications to a public cloud, it must understand that while the cloud provider secures the underlying infrastructure, the customer must implement security measures for their applications and data. This includes configuring security settings, managing identity and access management (IAM), and ensuring that data is encrypted both in transit and at rest. Additionally, customers must regularly audit their security practices to ensure compliance with industry standards and regulations. The incorrect options highlight common misconceptions about the shared responsibility model. For instance, stating that the customer is only responsible for physical security overlooks the broader scope of responsibilities that include application security and compliance. Similarly, the notion that the customer has no responsibilities misrepresents the model entirely, as it is essential for customers to actively manage their security posture. Lastly, suggesting that the customer is responsible for network infrastructure and hardware maintenance is misleading, as these aspects are typically managed by the cloud provider in a public cloud environment. Understanding the shared responsibility model is vital for organizations to effectively secure their cloud environments and mitigate risks associated with data breaches and compliance violations. This nuanced understanding enables organizations to allocate resources appropriately and implement robust security measures tailored to their specific needs in the cloud.

When a company migrates its applications to a public cloud, it must understand that while the cloud provider secures the underlying infrastructure, the customer must implement security measures for their applications and data. This includes configuring security settings, managing identity and access management (IAM), and ensuring that data is encrypted both in transit and at rest. Additionally, customers must regularly audit their security practices to ensure compliance with industry standards and regulations. The incorrect options highlight common misconceptions about the shared responsibility model. For instance, stating that the customer is only responsible for physical security overlooks the broader scope of responsibilities that include application security and compliance. Similarly, the notion that the customer has no responsibilities misrepresents the model entirely, as it is essential for customers to actively manage their security posture. Lastly, suggesting that the customer is responsible for network infrastructure and hardware maintenance is misleading, as these aspects are typically managed by the cloud provider in a public cloud environment. Understanding the shared responsibility model is vital for organizations to effectively secure their cloud environments and mitigate risks associated with data breaches and compliance violations. This nuanced understanding enables organizations to allocate resources appropriately and implement robust security measures tailored to their specific needs in the cloud.

First, we calculate the number of critical vulnerabilities: – Critical vulnerabilities = 40% of 150 – This can be calculated as: $$ \text{Critical vulnerabilities} = 0.40 \times 150 = 60 $$ Next, we calculate the number of high vulnerabilities: – High vulnerabilities = 30% of 150 – This can be calculated as: $$ \text{High vulnerabilities} = 0.30 \times 150 = 45 $$ Now, we add the critical and high vulnerabilities together to find the total number of vulnerabilities that require immediate attention: $$ \text{Total critical and high vulnerabilities} = 60 + 45 = 105 $$ Thus, the analyst should focus on 105 vulnerabilities for immediate remediation. This prioritization is crucial in cybersecurity as it allows the organization to address the most severe threats first, thereby reducing the risk of exploitation and potential damage to the network. Understanding the classification of vulnerabilities is essential, as it helps in aligning remediation efforts with the organization’s risk management strategy and compliance requirements, such as those outlined in frameworks like NIST SP 800-53 or ISO 27001.

In contrast, a Cloud Access Security Broker (CASB) serves as a security intermediary between cloud service users and cloud applications, providing visibility and control over data security policies but does not inherently define the shared responsibilities between the CSP and the customer. The Zero Trust Security Model emphasizes strict access controls and assumes that threats could be internal or external, but it does not specifically address the shared responsibilities in a cloud context. Defense in Depth is a security strategy that employs multiple layers of security controls but does not provide a framework for understanding the division of responsibilities in cloud environments. Understanding the nuances of these models is crucial for organizations to effectively manage their security posture in the cloud. The Shared Responsibility Model not only facilitates compliance with regulations such as GDPR or HIPAA by ensuring that both parties are aware of their obligations but also enhances operational efficiency by allowing organizations to focus on their specific security needs while relying on the CSP for infrastructure security. This model is essential for organizations looking to leverage cloud technologies while maintaining a robust security framework.

A robust training program should include regular updates and simulations to keep employees engaged and informed about the latest threats. This proactive approach not only empowers staff but also fosters a culture of security awareness within the organization. On the other hand, simply increasing the frequency of system updates and patch management without addressing user awareness does not tackle the root cause of many incidents, which often stem from human factors. A complex incident reporting structure may lead to confusion and delays in response, undermining the effectiveness of the incident response team. Additionally, reducing the budget for cybersecurity tools can leave the organization vulnerable, as it may lack the necessary resources to detect and respond to threats effectively. Therefore, prioritizing employee training and awareness is essential for creating a resilient cybersecurity posture, ensuring that all employees understand their role in safeguarding the organization against potential threats. This comprehensive approach to incident response not only addresses immediate vulnerabilities but also builds a foundation for continuous improvement in security practices.

1. **Critical Vulnerabilities**: Given that 40% of the vulnerabilities are classified as critical, we can calculate the number of critical vulnerabilities as follows: \[ \text{Critical} = 150 \times 0.40 = 60 \] 2. **High Vulnerabilities**: Next, we calculate the number of high vulnerabilities, which are reported as 35% of the total: \[ \text{High} = 150 \times 0.35 = 52.5 \] Since the number of vulnerabilities must be a whole number, we round this to 52. 3. **Medium Vulnerabilities**: The remaining vulnerabilities are classified as medium. To find this, we first determine the total number of critical and high vulnerabilities: \[ \text{Total Critical and High} = 60 + 52 = 112 \] Now, we subtract this from the total number of vulnerabilities to find the medium vulnerabilities: \[ \text{Medium} = 150 – 112 = 38 \] Thus, the final breakdown of vulnerabilities is 60 critical, 52 high, and 38 medium. This categorization is crucial for prioritizing remediation efforts, as critical vulnerabilities typically pose the highest risk and should be addressed immediately. Understanding the distribution of vulnerabilities helps organizations allocate resources effectively and mitigate potential threats in a timely manner. The use of a vulnerability scanning tool that references a comprehensive CVE database is essential in identifying these vulnerabilities accurately, ensuring that the organization remains compliant with security standards and best practices.

Additionally, WPA3 employs stronger encryption protocols, such as 192-bit security for enterprise networks, which enhances data protection during transmission. This is a significant improvement over WPA2, which typically uses 128-bit encryption. The enhanced encryption not only secures data but also helps in maintaining the integrity of the communication, making it more difficult for attackers to intercept and manipulate data. Moreover, WPA3 includes features like Forward Secrecy, which ensures that session keys are not compromised even if a long-term key is exposed. This means that past communications remain secure even if an attacker gains access to the network at a later time. In contrast, the incorrect options highlight misconceptions about WPA3. For instance, WPA3 does not use the same encryption methods as WPA2; it enhances them. Furthermore, WPA3 supports enterprise-level authentication, making it suitable for corporate environments. Lastly, WPA3 is designed to handle multiple connections efficiently, improving performance in high-density environments rather than causing increased latency. Thus, understanding these nuances is crucial for network administrators when considering upgrades to wireless security protocols.

\[ 1 \text{ Gbps} = \frac{10^9 \text{ bits}}{8} = 125 \times 10^6 \text{ bytes per second} \] Next, we calculate the number of packets that can be sent per second by dividing the total bytes per second by the average packet size: \[ \text{Packets per second} = \frac{125 \times 10^6 \text{ bytes per second}}{1500 \text{ bytes per packet}} \approx 83333.33 \text{ packets per second} \] However, we must account for the encryption overhead. With an estimated overhead of 10%, the effective throughput is reduced to 90% of the original throughput: \[ \text{Effective throughput} = 125 \times 10^6 \text{ bytes per second} \times 0.90 = 112.5 \times 10^6 \text{ bytes per second} \] Now, we recalculate the maximum number of packets that can be transmitted per second with the adjusted throughput: \[ \text{Adjusted packets per second} = \frac{112.5 \times 10^6 \text{ bytes per second}}{1500 \text{ bytes per packet}} = 75000 \text{ packets per second} \] This calculation shows that the adjusted maximum number of packets that can be transmitted per second, considering the encryption overhead, is 75000 packets. This scenario highlights the importance of understanding both throughput and the impact of encryption on effective data transmission rates in a VPN context. It also emphasizes the need for network administrators to consider these factors when designing secure remote access solutions to ensure they meet performance requirements while maintaining security.

Incorporating a whitelist of known safe IP addresses further enhances this rule by allowing legitimate users to bypass unnecessary alerts, thus reducing the noise generated by the SIEM. This is particularly important in environments where users may frequently change locations or use dynamic IP addresses, which could otherwise trigger alerts unnecessarily. On the other hand, setting a threshold of 5 failed attempts within a 10-minute window without considering the source IP address (as in option b) could lead to a high number of false positives, especially in environments with multiple users sharing the same IP address. Creating a rule that triggers on any failed login attempt (option c) would overwhelm the security team with alerts, making it difficult to discern genuine threats from benign activity. Lastly, while using a machine learning model (option d) may seem innovative, it introduces complexity and potential delays in response, as the model may require significant historical data to train effectively and may not adapt quickly to emerging threats. Thus, the most effective approach combines a well-defined threshold with contextual awareness of the source IP, ensuring that the SIEM tool remains responsive to genuine threats while minimizing unnecessary alerts.

In contrast, WPA2’s PSK method can be vulnerable to these types of attacks, especially if users choose weak passwords. Furthermore, WPA3 enhances the user experience by allowing for easier connections through features like Easy Connect, which simplifies the process of connecting IoT devices to the network. However, it is important to note that WPA3 does not eliminate the need for passwords; rather, it strengthens the authentication process. The other options present misconceptions about WPA3. For instance, while WPA3 does improve security, it does not eliminate the need for a password or allow unlimited device connections without performance issues. Additionally, while WPA3 is designed to be compatible with existing hardware, some older devices may require firmware updates or may not support the new protocol at all. Therefore, understanding these nuanced differences is crucial for network administrators when upgrading wireless security protocols to ensure robust protection against evolving threats.

\[ \text{Alert Threshold} = 1 \text{ Gbps} \times 0.80 = 800 \text{ Mbps} \] Since the traffic from the suspicious IP address has reached 850 Mbps, it exceeds the alert threshold of 800 Mbps. This indicates a potential security incident that requires immediate attention. Given that the IDPS is in inline mode, it has the capability to block traffic in real-time. Blocking the traffic from the suspicious IP address is the most appropriate immediate action because it prevents any potential exploitation of the vulnerability while further analysis is conducted. This proactive measure helps to mitigate risks associated with the identified suspicious activity. Increasing the alert threshold to 90% would not be advisable, as it could lead to overlooking significant threats, especially when the current traffic is already suspicious. Conducting a deeper packet inspection is a valuable step but should follow the immediate action of blocking the traffic to prevent any possible compromise. Notifying the application development team to patch the vulnerability is also important, but it does not address the immediate risk posed by the current traffic. In summary, the operational mode of the IDPS and the nature of the traffic necessitate an immediate blocking action to safeguard the network from potential exploitation. This approach aligns with best practices in incident response, emphasizing the importance of timely intervention in the face of detected anomalies.

\[ \text{Percentage of High-Severity Alerts} = \left( \frac{\text{Number of High-Severity Alerts}}{\text{Total Alerts}} \right) \times 100 \] Substituting the values from the scenario: \[ \text{Percentage of High-Severity Alerts} = \left( \frac{30}{150} \right) \times 100 = 20\% \] This indicates that 20% of the alerts were classified as high severity, which is a critical metric for understanding the potential impact of the malware incident. Next, to calculate the average response time for all alerts, we need to consider the weighted average based on the number of alerts and their respective response times. The total number of alerts is 150, with 30 high-severity alerts and 120 low-severity alerts (150 total alerts – 30 high-severity alerts). The response times are 15 minutes for high-severity alerts and 45 minutes for low-severity alerts. The formula for the weighted average response time is: \[ \text{Average Response Time} = \frac{(N_{high} \times T_{high}) + (N_{low} \times T_{low})}{N_{total}} \] Where: – \(N_{high} = 30\) (number of high-severity alerts) – \(T_{high} = 15\) minutes (response time for high-severity alerts) – \(N_{low} = 120\) (number of low-severity alerts) – \(T_{low} = 45\) minutes (response time for low-severity alerts) – \(N_{total} = 150\) (total alerts) Substituting the values: \[ \text{Average Response Time} = \frac{(30 \times 15) + (120 \times 45)}{150} \] Calculating the numerator: \[ (30 \times 15) = 450 \] \[ (120 \times 45) = 5400 \] \[ \text{Total} = 450 + 5400 = 5850 \] Now, dividing by the total number of alerts: \[ \text{Average Response Time} = \frac{5850}{150} = 39 \text{ minutes} \] Thus, the average response time for all alerts generated during this period is approximately 39 minutes. This analysis not only helps in understanding the effectiveness of the EDR system but also aids in identifying areas for improvement in incident response strategies. The ability to calculate and interpret these metrics is crucial for security analysts in evaluating the performance of EDR solutions and ensuring that they are adequately prepared to handle future incidents.

In contrast, increasing the number of employees with access to sensitive data (option b) directly undermines confidentiality, as it broadens the attack surface and increases the likelihood of data breaches. Allowing all employees temporary access during peak periods (option c) is also problematic, as it creates a scenario where sensitive data could be exposed to individuals who do not typically require access, further jeopardizing confidentiality. Lastly, using a single password for all employees (option d) is a significant security risk, as it eliminates individual accountability and makes it easier for unauthorized individuals to gain access to sensitive information. By implementing RBAC, the financial institution can effectively manage access to sensitive data, ensuring that confidentiality is upheld while still allowing employees to perform their necessary functions efficiently. This approach not only adheres to best practices in information security but also aligns with regulatory requirements that mandate the protection of sensitive customer information.