SMART: a Technology Readiness Methodology in the Frame of the NIS Directive

• URL: http://arxiv.org/abs/2201.00546v1
• Date: Mon, 3 Jan 2022 09:31:59 GMT
• Title: SMART: a Technology Readiness Methodology in the Frame of the NIS Directive
• Authors: Archana Kumari, Stefan Schiffner, Sandra Schmitz
• Abstract summary: Knowing Technology readiness level (TRL) of a given target technology proved to be useful to mitigate risks such as cost overrun, product roll out delays, or early launch failures. Originally developed for space programmes by NASA, TRL became a de facto standard among technology and manufacturing companies. We aim to address the gaps identified with existing Technology Readiness Assessment (TRA)s and aim to overcome these by developing standardised method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An ever shorter technology lifecycle engendered the need for assessing new technologies w.r.t. their market readiness. Knowing the Technology readiness level (TRL) of a given target technology proved to be useful to mitigate risks such as cost overrun, product roll out delays, or early launch failures. Originally developed for space programmes by NASA, TRL became a de facto standard among technology and manufacturing companies and even among research funding agencies. However, while TRL assessments provide a systematic evaluation process resulting in meaningful metric, they are one dimensional: they only answer the question if a technology can go into production. Hence they leave an inherent gap, i.e., if a technology fulfils requirements with a certain quality. This gap becomes intolerable when this metric is applied software such as technological cybersecurity measures. With legislation such as the General Data Protection Regulation4 (GDPR) and the Network and Information Systems Directive5 (NIS-D) making reference to state of the art when requiring appropriate protection measures, software designers are faced with the question how to measure if a technology is suitable to use. We argue that there is a potential mismatch of legal aim and technological reality which not only leads to a risk of non-compliance, but also might lead to weaker protected systems than possible. In that regard, we aim to address the gaps identified with existing Technology Readiness Assessment (TRA)s and aim to overcome these by developing standardised method which is suitable for assessing software w.r.t. its market readiness and quality (in sum maturity).

Related papers

• Assessing the State of AI Policy [0.5156484100374057]
  This work provides an overview of AI legislation and directives at the international, U.S. state, city and federal levels. It also reviews relevant business standards, and technical society initiatives.
  arXiv  Detail & Related papers  (2024-07-31T16:09:25Z)
• Open Problems in Technical AI Governance [93.89102632003996]
  Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
  arXiv  Detail & Related papers  (2024-07-20T21:13:56Z)
• DETECTA 2.0: Research into non-intrusive methodologies supported by Industry 4.0 enabling technologies for predictive and cyber-secure maintenance in SMEs [0.19972837513980318]
  The DETECTA 2.0 project harmonizes real-time anomaly detection, sophisticated analytics, and predictive forecasting capabilities. At the core lies a Digital Twin interface, providing intuitive real-time visualizations of machine states and detected anomalies. The predictive engine uses advanced time series algorithms like N-HiTS to forecast future machine utilization trends.
  arXiv  Detail & Related papers  (2024-05-24T08:38:38Z)
• No Trust without regulation! [0.0]
  The explosion in performance of Machine Learning (ML) and the potential of its applications are encouraging us to consider its use in industrial systems. It is still leaving too much to one side the issue of safety and its corollary, regulation and standards. The European Commission has laid the foundations for moving forward and building solid approaches to the integration of AI-based applications that are safe, trustworthy and respect European ethical values.
  arXiv  Detail & Related papers  (2023-09-27T09:08:41Z)
• When to Trust AI: Advances and Challenges for Certification of Neural Networks [26.890905486708117]
  Early adoption of AI technology for real-world applications has not been without problems. This paper provides an overview of techniques that have been developed to ensure safety of AI decisions.
  arXiv  Detail & Related papers  (2023-09-20T10:31:09Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Qlib: An AI-oriented Quantitative Investment Platform [86.8580406876954]
  AI technologies have raised new challenges to the quantitative investment system. Qlib aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.
  arXiv  Detail & Related papers  (2020-09-22T12:57:10Z)
• Constraint Programming Algorithms for Route Planning Exploiting Geometrical Information [91.3755431537592]
  We present an overview of our current research activities concerning the development of new algorithms for route planning problems. The research so far has focused in particular on the Euclidean Traveling Salesperson Problem (Euclidean TSP) The aim is to exploit the results obtained also to other problems of the same category, such as the Euclidean Vehicle Problem (Euclidean VRP), in the future.
  arXiv  Detail & Related papers  (2020-09-22T00:51:45Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)
• Quality Management of Machine Learning Systems [0.0]
  Artificial Intelligence (AI) has become a part of our daily lives due to major advances in Machine Learning (ML) techniques. For business/mission-critical systems, serious concerns about reliability and maintainability of AI applications remain. This paper presents a view of a holistic quality management framework for ML applications based on the current advances.
  arXiv  Detail & Related papers  (2020-06-16T21:34:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.