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Enterprise Architecture – Gathering Data from Unprotected Sources

Enterprise Architecture – Gathering Data from Unprotected Sources

Data is one of the most precious assets in today’s business world. Organizations acquire and evaluate massive volumes of data from many sources to get insights, make informed choices, and remain competitive. The “octopus” data flow, as represented in Figure 8.1, exemplifies the complexities of data sources and their interactions within an enterprise architecture (Schoenfield, 2015, p. 143). However, obtaining data from various secure sources poses considerable hurdles, notably regarding security, privacy, and data integrity. This response will examine how an analysis system may acquire data from the protected sources specified in the figure while maintaining the data’s confidentiality, integrity, and availability. This report will also review the important ideas, technology, and best practices for creating a secure data flow while adhering to relevant legislation and standards.

Gathering Data from the Unprotected Sources

Data Encryption and Secure Communication

One of the core strategies for gathering data from secure sources is to ensure data encryption during storage and transfer. The analysis system should use robust encryption technologies to protect data at rest in databases or archives and in transit across different data flow components. For a secure connection between the analytic system and the data sources, one can use Transport Layer Security (TLS)/Secure Sockets Layer (SSL) (Schoenfield, 2015, p. 148). This guarantees that data is encrypted during transmission, making it harder for unauthorized parties to intercept or tamper with the data. One can also encrypt data stored in databases, content archives, and other data repositories at rest. This guarantees that the data is safeguarded even if an attacker has access to the storage infrastructure.

Access Controls and Identity Management

Controlling data access is critical for preventing unwanted access and data breaches. The analysis system should integrate strong access restrictions and identity management procedures to guarantee that only authorized people have access to sensitive data. Firstly, Wolf et al. (2017) recommend using role-based access control (RBAC) and attribute-based access control (ABAC) to improve data security (p. 37). RBAC regulates access based on user roles and responsibilities, whereas ABAC considers other factors, resulting in a granular and dynamic access control system. Second, using multi-factor authentication (MFA) adds an extra layer of security by forcing users to submit several forms of identity before accessing data, preventing unwanted access and protecting sensitive information. Third, one can establish audit trails to monitor access activity and detect suspicious behavior and illegal access attempts. Reviewing these records regularly aids in detecting possible risks, allowing for early response and boosting the overall security posture.

Data Governance and Compliance

Data governance is critical for ensuring data quality, integrity, and compliance with applicable laws and rules. The analytic system should comply with data governance rules to guarantee that data is utilized responsibly and ethically. To guarantee responsible and ethical data use, thorough data governance policies and standards must be developed and enforced (Wolf et al., 2017, p. 41). These rules govern data access, usage, retention, and destruction, ensuring data is managed following applicable laws and internal best practices. Further, to protect sensitive data and individuals’ privacy rights, industry-specific legislation and standards such as GDPR, HIPAA, and ISO 27001 should be followed. Adherence to these standards protects data and reduces legal and reputational concerns.

Secure API Integration

Many current systems and apps expose APIs (Application Programming Interfaces) to enable smooth integration and data sharing. The analytic system can use secure API integration to collect data from diverse sources. Accordingly, implementing API security methods such as API keys, OAuth, and token-based authentication is critical to ensure safe data transfer. These techniques regulate API access, increasing security against unwanted access and preserving data integrity and confidentiality. Additionally, conducting API usage audits regularly improves security. These audits can proactively uncover potential vulnerabilities and illegal API access, allowing for fast correction and maintaining data flow integrity.

Data Anonymization and Pseudonymization

The analytical system can use data anonymization and pseudonymization techniques to secure sensitive data better (Wolf et al., 2017, p. 42). These strategies conceal sensitive data or substitute personally identifiable information (PII) with false identifiers. Individual identities cannot be deduced from data anonymization, hence guaranteeing privacy. Notably, pseudonymization entails substituting identifying data with fictitious identifiers, allowing data analysis while protecting personal information.

Data Loss Prevention (DLP)

Data Loss Prevention (DLP) technologies and policies must be used to avoid data leakage and unwanted data exfiltration. Essentially, DLP tools may monitor and control data leaving the business via multiple channels such as email, cloud storage, and removable media. Besides, establishing rules and regulations helps to quickly identify and prohibit data breaches or leaks in real time to improve data security (Schoenfield, 2015, p. 176). Thus, implementing strong Data Loss Prevention (DLP) procedures may efficiently monitor and regulate data leaving the business through multiple routes, protecting sensitive data.


In conclusion, obtaining data from various protected sources in the “octopus” data flow necessitates a strong and well-planned method to assure data security, privacy, and integrity. The analytic system should include encryption, access restrictions, identity management, and API security to secure sensitive data from unwanted access. Data governance standards and adherence to applicable legislation are critical to upholding data ethics and legal duties. Furthermore, anonymization and pseudonymization techniques can improve data security by allowing data analysis without jeopardizing individual privacy. Finally, implementing Data Loss Prevention (DLP) procedures helps to reduce the danger of data leakage and exfiltration. By using these principles, technologies, and best practices, businesses can reliably construct a safe data flow and exploit the full potential of data from varied sources while maintaining a strong security posture and adhering to regulatory standards.


Schoenfield, B. S. (2015). Securing systems: Applied security architecture and threat models. CRC Press.

Wolf, W., White, G. B., Fisch, E. A., Crago, S. P., Pooch, U. W., McMahon, J. O., & Lebak, J. M. (2017). Computer system and network security. CRC press.


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Enterprise Architecture

Enterprise Architecture

Consider the data flow “octopus,” as shown in Figure 8.1. How can the analysis system gather data from all these sources that, presumably, are protected themselves?
Answer the questions using an APA-formatted paper (Title page, body and references only). Your response should have a minimum of 600 words. Count the words only in the body of your response, not the references. A table of contents and abstract are not required.
A minimum of two references is required. One reference for the book is acceptable but multiple references are allowed. There should be multiple citations within the body of the paper. Note that an in-text citation includes the author’s name, year of publication, and the page number where the paraphrased material is located.

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