University Of The Cumberlands School Of Computer Information ✓ Solved

University Of The Cumberlandsschool Of Computer Information Science

University Of The Cumberlandsschool Of Computer Information Science

University of the Cumberlands School of Computer & Information Sciences ISOL-536 - Security Architecture & Design Chapter 8: Business Analytics Spring 2020 Dr. Yehia Mohamed Chapter 8: Business Analytics • 8.1 Architecture • 8.2 Threats • 8.3 Attack Surfaces • 8.3.1 Attack Surface Enumeration • 8.4 Mitigations • 8.5 Administrative Controls • 8.5.1 Enterprise Identity Systems (Authentication and Authorization) • 8.6 Requirements 8.1 Architecture Data science is a set of fundamental principles that guide the extraction of knowledge from data. Data mining is the extraction of knowledge from data via technologies that incorporate these principles. • Like many enterprises, Digital Diskus has many applications for the various processes that must be executed to run its business, from finance and accounting to sales, marketing, procurement, inventory, supply chain, and so forth.

A great deal of data is generated across these systems. But, unfortunately, as a business grows into an enterprise, most of its business systems will be discreet. Getting a holistic view of the health of the business can be stymied by the organic growth of applications and data stores. • The system shown in Figure 8.1 (next slide) comprises not only the business analytics and intelligence but also the many enterprise systems with which analytics must interact. In order to consider the entire system, we must understand not only the architecture of the business analysis system itself, but also its communications with other systems. 8.1 Architecture – Cont.

Figure 8.1 Business analytics logical data flow diagram (DFD). 8.1 Architecture – Cont. Figure 8.2 Business analytics data interactions. • Figure 8.2 is a drill down view of the data gathering interactions of the business analytics system within the enterprise architecture. Is the visualization in Figure 8.2 perhaps a bit easier to understand? To reiterate, we are looking at the business analysis and intelligence system, which must touch almost every data gathering and transaction- processing system that exists in the internal network. And, as was noted, business analytics listens to the message bus, which includes messages that are sent from less trusted zones. 8.2 Treats Figure 8.3 Business analytics system architecture. • As we move to system specificity, if we have predefined the relevant threats, we can apply the threats’ goals to the system under analysis. This application of goals leads directly on to the “AS†of ATASM: attack surfaces. Understanding your adversaries’ targets and objectives provides insight into possible attack surfaces and perhaps which attack surfaces are most important and should be prioritized. • It’s useful to understand a highly connected system like business analytics in situ, that is, as the system fits into its larger enterprise architectural context. However, we don’t yet have the architecture of the system itself. Figure 8.3 presents the logical components of this business analytics system. There are five major components of the system: 1. Data Analysis processing 2. Reporting module 3. Data gathering module 4. Agents which are co-located with target data repositories 5. A management console 8.3 Attack Surfaces • In this context, where several components share the same host, how would you treat the communications between them? Should these communications be considered to traverse a trusted or an untrusted network? If Digital Diskus applies the rigor we indicated above to the management of the servers on which business analytics runs, what additional attack surfaces should be added from among those three components and their intercommunications when all of these share a single host? • If an attacker can retrieve the API and libraries, then use these to write an agent, and then get the attacker’s agent installed, how should Digital Diskus protect itself from such an attack? Should the business analytics system provide a method of authentication of valid agents in order to protect against a malicious one? Is the agent a worthy attack surface? • Why should the output of Management Console be considered an attack surface? Previously, the point was made that all inputs should be considered attack surfaces. However, when the outputs of the system need protection, such as the credentials going into the business analytics configuration files and metadata, then the outputs should be considered an attack surface. If the wily attacker has access to the outputs of Management Console, then the attacker may gain the credentials to many systems. 8.3 Attack Surfaces – Cont. Figure 8.4 Business analytics user interactions. • Figure 8.4 returns to a higher level of abstraction, obscuring the details of the business analytics modules running on the host. Since we can treat the collection of modules as an atomic unit for our purposes, we move up a level of granularity once again to view the system in its logical context. Management Console has been broken out as a separate component requiring its own defenses. The identity system has been returned to the diagram, as has the security monitoring systems. These present possible attack surfaces that will need examination. In addition, these will become part of the defenses of the system, as we shall see. • Access controls to Management Console itself, authentication and authorization to perform certain actions, will be key because Management Console is, by its nature, a configurator and controller of the other functions, a target. Which brings us to Figure 8.4. 8.3 Attack Surfaces – Cont. • How might an attacker deliver such a payload? The obvious answer to this question will be to take over a data source in some manner. This, of course, would require an attack of the data source to be successful and becomes a “one-two punch.†However, it’s not that difficult. If the attacker can deliver a payload through one of the many exposed applications that Digital Diskus maintains, the attack can rest in a data store and wait until the lucky time when it gets delivered to the business analytics system. In other words, the attacker doesn’t have to deliver the payload directly to Data Gathering. She or he must somehow deliver the attack into a data store where it can wait patiently to be brought into the data gathering function. • The results most certainly present an attack opportunity if the permissions on the results store are not set defensively, which, in this case means: • Processing store is only mounted on the host that runs Processing and Reporter • Write permission is only granted to Processing • Read permission is only granted to Reporter • Only a select few administers may perform maintenance functions on the processing data store • Every administrative action on processing store is logged and audited for abnormal activity 8.3.1 Attack Surface Enumeration 8.4 Mitigations • As you consider the attack surfaces in the list on the previous slide, what security controls have already been listed? • The questions that then will be asked for this type of critical system that maintains highly sensitive data will be something like, “Who should have these privileges and how many people need them?†• Competing against simplicity and economies of scale are the differences in data sensitivity and system criticality. In the case of business analytics, there appears to be a clear need to protect the configuration files and the results files as carefully as possible leaving as small an attack surface as can be managed. That is, these two sensitive locations that store critical organizational data should be restricted to a need-to- access basis, which essentially means as few administrators as possible within the organization who can manage the systems effectively and continuously. • If we were actually implementing the system, we might have to engage with the operational server management teams to construct a workable solution for everyone. For our purposes in this example, we can simply specify the requirement and leave the implementation details unknown. 8.5 Administrative Controls • Access will be restricted to a need-to-know basis. As we have noted, changes to the systems are monitored and audited. At the application level, files and directories will be given permissions such that only the applications that need to read particular files or data are given permission to read those files. This is all in accordance with the way that proper administrative and operating system permissions should be set up. The business analytics systems and tools don’t require superuser rights for reading and executing everything on the system. Therefore, the processing unit has rights to its configuration files and data gathering module files. The reporting module reads its own configuration files. None of these can write into the configuration data. Only Management Console is given permission to write data into the configuration files. In this way, even if any of the three processing modules is compromised, the compromised component cannot make use of configuration files to compromise any of the other modules in the system. This is how self-defensive software should operate. Business analytics adheres to these basic security principles, thus allowing the system to be deployed in less trusted environments, even less protected than what Digital Diskus provides. 8.5.1 Enterprise Identity Systems (Authentication and Authorization) • Authentication via the corporate directory and authorization via group membership still remain two of the important mitigations that have been implemented. • Having reviewed the available mitigations, which attack surfaces seem to you to be adequately protected? And, concomitantly, which attack surfaces still require an adequate defense? 8.6 Requirements • In order to prevent an attacker from obscuring an attack or otherwise spoofing or fooling the security monitoring system, the business analytics activity and event log files should only be readable by the security monitoring systems. And the log files permissions should be set such that only event-producing modules of the business analytics system may write to its log file. Although it is true that a superuser on most operating systems can read and write any file, in this way, attackers would have to gain these high privileges before they could alter the log files that will feed into the security monitoring system. • Table 8.1 (next slide) summarizes the additional security requirements that Digital Diskus will need to implement in order to achieve the security posture required for this sensitive system, the business intelligence and analytics system. 8.6 Requirements – Cont. • Table 8.1 is not intended as a complete listing of requirements from which the security architecture would be designed and implemented. As I explained above, when I perform a security architecture analysis, I try to document every requirement, whether the requirement has been met or not. In this way, I document the defense-in-depth of the system. If something changes during implementation, or a security feature does not fulfill its promise or cannot be built for some reason, the requirements document provides all the stakeholders with a record of what the security posture of the system should be. I find that risk is easier to assess in the face of change when I’ve documented the full defense, irrespective of whether it exists or must be built. Chapter 8: Summary • The architect (or peer reviewing architect team) must decide the scope of the risk’s possible impact (consequences). The scope of the impact dictates at what level of the organization risk decisions must be made. The decision maker(s) must have sufficient organizational decision-making authority for the impacts. For instance, if the impact is confined to a particular system, then perhaps the managers involved in building and using that system would have sufficient decision making scope for the risk. If the impact is to an entire collection of teams underneath a particular director, then she or he must make that risk decision. If the risk impacts an enterprise’s brand, then the decision might need to be escalated all the way to the Chief Operating Officer or even the Chief Executive, perhaps even to the Board of Directors, if serious enough. The scope of the impact is used as the escalation guide in the organizations for which I’ve worked. Of course, your organization may use another approach.

Sample Paper For Above instruction

Understanding the Security Architecture and Threat Management in Business Analytics Systems

In today's rapidly evolving digital landscape, the integration of data science, data mining, and business analytics is vital for organizations seeking a competitive edge. The architectural complexity of business analytics systems introduces unique threats and attack surfaces that require a robust security framework. This paper explores the essential principles of security architecture within business analytics environments, focusing on threat identification, attack surface enumeration, and mitigation strategies, supported by credible sources to inform secure design practices.

Business Analytics Architecture: Foundations and Challenges

Business analytics systems are integral to extracting actionable insights from vast and disparate datasets generated across enterprise applications such as finance, sales, and supply chain management (Sharma & Sinha, 2018). The architectural design of these systems involves complex data flows between multiple components, including data analysis modules, reporting interfaces, data gathering modules, and management consoles (Chen et al., 2019). The visual representations, such as logical data flow diagrams and data interaction models, aid in understanding the intricate communication paths critical for security assessment (Davis, 2020).

Effective architecture management necessitates an understanding of how components interact both locally and over networks, especially considering the potential vulnerabilities introduced by shared hosts and untrusted network zones (Kim & Solomon, 2020). Identifying attack surfaces within this context involves analyzing each component's exposure and the pathways through which an attacker might compromise data integrity, confidentiality, or availability (Anderson & Moore, 2018).

Identifying and Protecting Attack Surfaces

Key attack surfaces include communication channels between components, access control mechanisms, and output interfaces like management consoles (Lemos & Cordeiro, 2021). For example, inter-component communication within a shared host environment could potentially be compromised if not secured with encryption and strict access controls (Stallings & Brown, 2021). Similarly, interfaces such as APIs, libraries, and configuration files represent critical attack vectors if left inadequately protected (Williams & Huxham, 2019).

Particularly, attack vectors like malicious agents impersonating legitimate components pose significant risks. Protecting these involves implementing rigorous authentication and authorization protocols, such as enterprise identity management using corporate directories and group memberships (Li & Liu, 2022). Preventative controls like digital signatures and cryptographic validation help verify agent legitimacy, reducing the risk of malicious infiltration (Lo et al., 2020).

Mitigation Strategies and Defensive Measures

Once attack surfaces are identified, mitigation strategies focus on minimizing exposed vectors and implementing defense-in-depth mechanisms. Restricting permissions on configuration and result files ensures that only authorized personnel and processes can access sensitive data, reducing the risk of insider or outsider threats (Kumar & Srinivasan, 2019). Employing role-based access control (RBAC), encrypted communications, and audit logs contribute to establishing a resilient security posture (Ferguson & Schneier, 2021).

Furthermore, implementing comprehensive monitoring systems that restrict activity logs' accessibility to security modules prevents attackers from tampering with forensic data (O’Connor et al., 2020). In the event of an attack, such controls facilitate timely detection and containment, limiting potential damage (Chen et al., 2019).

Administrative Controls and Policy Enforcement

Administrative controls are critical for enforcing security policies such as need-to-know access, regular auditing, and change management. Proper permission configurations at both the application and operating system levels prevent unauthorized modifications, even if one component is compromised (Russell & Gangemi, 2018). These processes should be continuously reviewed and updated to accommodate emerging threats and organizational changes (Khan & Kumar, 2022).

Implementing Identity and Access Management (IAM)

Enterprise identity systems leveraging corporate directories, coupled with group-based authorization, provide essential identity management for business analytics systems (Sinha & Sharma, 2021). Authentication factors such as passwords, digital certificates, or multi-factor authentication enhance security, ensuring only trusted entities operate within the system (Das & Poovendran, 2020). Proper IAM implementation prevents malicious actors from gaining unauthorized access, which is crucial given the sensitive nature of analytical outputs and configurations.

Security Requirements and Continuous Improvement

Security requirements must be diligently documented and regularly reviewed, capturing specific needs such as restricting access to logs, audit trails, and vital configuration data. Setting appropriate permissions ensures that only designated modules can write logs, which are essential for incident analysis (Ferguson & Schneier, 2021). Continual assessment of security postures, through risk analysis and threat modeling, underpins a resilient security architecture capable of adapting to new threats (Williams & Huxham, 2019).

Conclusion

The integration of secure architecture principles, attack surface analysis, mitigation techniques, and administrative controls creates a comprehensive defense strategy for business analytics systems. As enterprise data environments grow in complexity, maintaining a vigilant and adaptable security posture remains paramount, supported by credible research and best practices in security engineering. Effective implementation of these strategies will significantly reduce the risk of breaches, protect sensitive data, and uphold organizational integrity (Kim & Solomon, 2020; Stallings & Brown, 2021; Sinha & Sharma, 2021).

References

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• Chen, L., et al. (2019). Security architecture for enterprise analytics. IEEE Transactions on Security & Privacy, 17(2), 88-97.
• Das, S., & Poovendran, R. (2020). Enhancing identity management with multi-factor authentication. ACM Computing Surveys, 52(3), 1-36.
• Khan, A., & Kumar, S. (2022). Organizational policies for security management in enterprise systems. International Journal of Information Security, 21(4), 512-526.
• Kim, D., & Solomon, M. G. (2020). Fundamentals of Information Systems Security. Jones & Bartlett Learning.
• Kumar, V., & Srinivasan, R. (2019). Defense-in-depth strategies for enterprise applications. Cybersecurity Journal, 5(1), 23-31.
• Li, J., & Liu, Y. (2022). Role-based access control in enterprise systems. IEEE Software, 39(2), 45-52.
• Lo, D., et al. (2020). Digital signatures and their role in agent authentication. Journal of Cryptographic Engineering, 10(4), 345-358.
• O’Connor, T., et al. (2020). Forensic analysis and integrity in security auditing. Forensic Science International, 308, 110119.
• Russell, N., & Gangemi, A. (2018). Operating system permissions and security management. Systems Security Journal, 14(3), 124-139.
• Sinha, A., & Sharma, N. (2021). Enterprise identity management systems. Security & Communication Networks, 2021, Article