AI resources update: 1. GenAI Concepts 2. MITRE AI Maturity Model 3. The AI Tech Stack
This article is part of an ongoing series looking at AI in KM, and KM in AI.
This AI resources update presents three key resources that can assist organisations to deploy AI in a well-planned and well-organised way:
- GenAI Concepts.
- MITRE AI Maturity Model.
- The AI Tech Stack.
1. GenAI Concepts
To help entities interested in GenAI deployments, the GenAI Concepts publication outlines 42 concepts fundamental to AI software systems. Each concept is illustrated through descriptions, examples and real-world use cases, with accessible language and visual elements to accommodate a diverse range of stakeholders and readerships.
GenAI Concepts was developed as a collaboration between two Australian organisations, the ARC Centre of Excellence for Automated Decision-Making and Society(ADM+S) and the Office of the Victorian Information Commissioner(OVIC). It was produced through expert consultation and analysis of academic literature, industry reports, and policy guidance.
With thanks to Peter Slattery, PhD on LinkedIn.
2. MITRE AI Maturity Model
The MITRE Artificial Intelligence (AI) Maturity Model (MM) can be viewed as a methodology to provide guidance and recommendations for building a foundation for successful AI implementation across an organization. It could potentially be integrated with KM maturity models.
It was developed based on a systematic review of commercial AI MMs extant throughout the private sector as well as an assessment of both the Capability Maturity Model Integration (CMMI) appraisal processes developed by Carnegie Mellon University and the National Institute of Standards and Technology’s AI Standards.
The AI MM is organized according to six pillars that industry considers major aspects of maturity that are key to successful AI adoption:
- Ethical, equitable, and responsible use.
- Strategy and resources.
- Organization.
- Technology enablers.
- Data.
- Performance and application.
Each pillar has either three or four dimensions (20 total) describing specific actions and activities that demonstrate advancing mastery of AI maturity for that dimension. These pillars and dimensions are assessed across five readiness levels that qualitatively describe different approaches to AI adoption. They are juxtaposed with five assessment levels intended to describe hierarchical and scalable progress throughout AI adoption: Initial, Adopted, Defined, Managed, and Optimized.
With thanks to Peter Slattery, PhD on LinkedIn.
3. The AI Tech Stack
The AI Tech Stack report serves as a comprehensive primer, offering public policy and cybersecurity practitioners insights into this dynamic landscape where their domains increasingly intersect.
The AI technology stack comprises five distinct yet interdependent layers:
- Governance layer – The framework that effectively wraps around the whole AI Technology Stack—a layer that aims to ensure responsible deployment through security protocols, legal constraints, ethical principles, and policies.
- Application layer – The user interface that transforms complex AI capabilities into accessible tools through browsers, APIs, dashboards, and other user interfaces.
- Infrastructure layer – The essential computational foundation that powers AI systems, enabling the intensive demands of training and inference through specialized hardware, cloud platforms, and energy resources.
- Model layer – The core computational component that processes data according to sophisticated algorithms to recognize patterns and generate predictions or decisions. This includes the machine learning approaches that enable systems to learn without explicit programming.
- Data layer – The foundation of AI systems, providing the raw material that fuels models. The quality, diversity, and quantity of this data largely determine the intelligence and capabilities of the final model.
Robust security across this stack is a technical necessity and a strategic imperative. AI security extends traditional cybersecurity concepts to confront unique vulnerabilities within machine learning systems, including adversarial attacks, model poisoning, and data exploitation. Organizations that prioritize comprehensive AI security not only mitigate risks but also position themselves as leaders in tomorrow’s innovation networks, capable of rapidly integrating advancements while sustaining trust. By embedding security measures early in the development process, organizations gain downstream competitive advantages, including faster deployment cycles, greater stakeholder confidence, and better products. The first step to this process is understanding the AI Tech Stack.
With thanks to Peter Slattery, PhD on LinkedIn.
Header image source: Created by Bruce Boyes with Microsoft Designer Image Creator.
