UMGC Challenges and Ethical Considerations in EDA and AI TRiSM Discussion

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This is a two-part response question. Please provide a full paragraph for each. 

1. Enterprise architecture aims at designing approaches around managing IT infrastructure for the use of business purposes. To this end, such architectures define the direction and processes that will be involved to ensure the effective use of data and resources. The enterprise architecture is made up of a set of messaging among its components. As such, the business processes can be a set of functions that are used by entities at providing a Service-Oriented Architecture (SOA) (Clark & Barn, 2011). On the other hand, such processes can be designed to be triggered by a significant event and allow complex analysis which is known as Event-Driven Architecture (EDA) (Clark & Barn, 2011).

EDA focuses on the analysis and functionality of events within the business system. Components of the architecture produce or create events to trigger actions/processes within the system allowing other components to respond through the messaging system (Clark & Barn, 2011). Thus, EDA fits well with sophisticated business operations that require continuous event analysis, and it can have versatile applications. For example, EDA can be used to manage the Internet of Things (IoT) domain, where events from connected devices are collected and redirected to trigger actions from other devices in real time. 

EDA promotes loose coupling and distributes actions among components (Theorin et al., 2017). This is contrary to a system requiring a centralized trigger system to begin an action. Components can make their own actions creating and responding to events ensuring high flexibility ad efficiency. This allows for complex event analysis in real-time providing insights to make necessary and time-sensitive decisions. In summary, EDA provides a highly sophisticated and versatile enterprise architecture that allows a distributed and highly efficient system. Since the focus of this discussion was the importance and benefits of EDA:

What do you think could be the disadvantages that could come with such an infrastructure?

Furthermore, do you think there are challenges to adapting such a system to different businesses?

What about the ethical considerations of data sharing in a decentralized system architecture? 

  • 2. AI Trust, Risk and Security Management a.k.a. AI TRiSM is a framework that helps organizations stay compliant with regulations, abide by data privacy laws, and responsibly govern and prevent abuse when using AI (artificial intelligence) by helping identify, monitor, and reduce potential risks (Siddiqui, 2023). This framework was originally developed and published by Gartner Inc. (Gartner) in their Market guide for AI Trust, risk and security management. Gartner states that this framework ensures AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection.The Gartner guide breaks down solutions and techniques into the following four (4) categories.
  • Model interpretability and explainability
  • AI data protection
  • ModelOps

Adversarial attack resistance

Model interpretability and explainability falls under trust management. It includes providing transparent explanations of the AI’s decision making to ensure that it is free from bias and discrimination, and adhering to ethical principles and standards (Aggarwal, 2023). AI data protection includes privacy, legal, and ethical responsibilities to protect individual’s personal information and to use the data responsibility. The AI TRiSM guidelines help organizations develop policies and procedures to protect AI data (Siddiqui, 2023). ModelOps or model operations covers the AI system, including the system development lifecycle (SDLC). The ModelOps guidelines ensure system performance and reliability through proper development, deployment, maintenance, and monitoring procedures (Aggarwal, 2023).Adversarial attack resistance includes data privacy and cybersecurity. AI models need to be built and maintained with the highest levels of cybersecurity. The AI TRiSM framework provides guidelines for developing the security protocols, policies and procedures and governance necessary for safeguarding the AI application(s) and data. The guidelines cover identifying risks, risk avoidance, implementing and maintaining cybersecurity measures, and contingency planning (Aggarwal, 2023).AI TRiSM is important because “AI brings new trust, risk and security management challenges that conventional controls do not address” (Gartner Inc., 2023). The AI TRiSM framework was written specifically for AI to improve model reliability, trustworthiness, fairness, privacy and security.Thought-provoking questions for my classmates:

Provide and discuss an organization that would benefit from implementing AI TRiSM.

  1. Why do you think AI TRiSM is important?
  2. Is AI TRiSM really all that different from other IT security frameworks?
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