Decoding TRiSM: Challenge of Achieving Trust, Risk, and Security Management for Data Engineereing Leadership

TRiSM stands for Trust, Risk and Security Management in AI models and applications. It is a set of solutions and techniques to ensure that data driven decision making (AI, ML, Algorithmic Applications, etc.) systems are ethical, transparent, accountable, reliable, robust, fair and compliant.

Data engineering is the process of designing, building and maintaining data pipelines, platforms and architectures that support data and analytics use cases. TRiSM with data engineering involves applying TRiSM principles and practices to the data sources, transformations, quality, governance and security aspects of data engineering.

I have worked with our own experts and other leaders in the field, together with ErgoSum / X Labs, to develop a new framework for TRiSM for Data Archiecture & Engineering. This framework consists of six interrelated components that cover all the aspects of building and managing trustworthy, secure and effective AI systems.

Model Interpretability and Explainability:

How to make data engineering processes and AI models more transparent, understandable and accountable to the users and stakeholders.

Data and Content Anomaly Detection:

How to identify and correct data errors, outliers, inconsistencies and biases that may affect the data quality and the performance and accuracy of AI models.

Model Data Protection:

How to safeguard the privacy and confidentiality of data used for AI training and inference, using methods such as encryption, anonymization, pseudonymization and differential privacy.

Model Operations:

How to monitor and manage the data lifecycle, quality, drift, lineage and provenance of AI models, and to ensure the alignment of data engineering and AI development workflows.

Adversarial Attack Resistance:

How to prevent and mitigate the risks of malicious manipulation of data and AI models, such as data poisoning, backdoor attacks and model evasion.

Sustainable Technology Platform Engineering:

How to design and operate data and analytics platforms that minimize the environmental impact and carbon footprint of data-intensive workloads, while also optimizing the performance and cost efficiency of such platforms.

ApprochX TRiSM Framework 2024 - by ErgoSum / X Labs

Data and analytics platforms are essential for enabling data-driven decision making, innovation, and value creation in the digital era. However, these platforms also pose significant challenges and risks in terms of environmental impact, carbon footprint, performance, cost efficiency, and trustworthiness. To address these challenges and risks, we propose a novel framework for sustainable technology platform engineering (STPE), which aims to design and operate data and analytics platforms that minimize the environmental impact and carbon footprint of data-intensive workloads, while also optimizing the performance and cost efficiency of such platforms.

The rapid development and deployment of AI systems across various domains and regions has raised many legal and ethical challenges, such as data privacy, human rights, accountability, and liability. Different countries and regions have adopted different approaches to regulate AI, reflecting their values, interests, and priorities.

The EU’s AI Act is the most comprehensive and restrictive AI law in the world, aiming to protect fundamental rights and ensure safety and trust in AI. The US President’s Executive Order on AI is more focused on promoting innovation and competitiveness, while also addressing some aspects of AI governance and ethics.

ApprochX TRiSM Framework aims to bring a transparent and cohesive approach to building simple yet effective, compliant and productive data-driven solutions for Quantitative Analytics, AI and Machine Learning applications.

Please, follow The Author, Newsletter, and ErgoSum / X Labs to stay updated about the framework. We will post more updates and detailed assessment systems to improve and measure the readiness.

About Author

Technology thought-leader and LinkedIn top voice in Cloud Compute, ML and Data Engineering, the author mentors R&D breakthroughs in advanced analytics platforms at ErgoSum/X Labs. With deep expertise across the data stack and passion for accelerating innovation, he champions bespoke benchmarking, design, delivery and optimization of analytics platforms.

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