Automated Compliance Blueprint Optimization with Artificial Intelligence

• URL: http://arxiv.org/abs/2206.11187v1
• Date: Wed, 22 Jun 2022 15:59:16 GMT
• Title: Automated Compliance Blueprint Optimization with Artificial Intelligence
• Authors: Abdulhamid Adebayo, Daby Sow, Muhammed Fatih Bulut
• Abstract summary: In banking and healthcare, one of the major hindrances to the adoption of cloud computing is compliance with regulatory standards. This is a complex problem due to many regulatory and technical specification (techspec) documents that the companies need to comply with. We present early results to identify the mapping between techspecs and regulation controls, and discuss challenges that must be overcome for this solution to be fully practical.
• Score: 1.90073733366566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For highly regulated industries such as banking and healthcare, one of the major hindrances to the adoption of cloud computing is compliance with regulatory standards. This is a complex problem due to many regulatory and technical specification (techspec) documents that the companies need to comply with. The critical problem is to establish the mapping between techspecs and regulation controls so that from day one, companies can comply with regulations with minimal effort. We demonstrate the practicality of an approach to automatically analyze regulatory standards using Artificial Intelligence (AI) techniques. We present early results to identify the mapping between techspecs and regulation controls, and discuss challenges that must be overcome for this solution to be fully practical.

Related papers

• RIRAG: Regulatory Information Retrieval and Answer Generation [51.998738311700095]
  We introduce a task of generating question-passages pairs, where questions are automatically created and paired with relevant regulatory passages. We create the ObliQA dataset, containing 27,869 questions derived from the collection of Abu Dhabi Global Markets (ADGM) financial regulation documents. We design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it with RePASs, a novel evaluation metric.
  arXiv  Detail & Related papers  (2024-09-09T14:44:19Z)
• Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis [0.7373617024876725]
  This study fills a crucial gap in aligning XAI applications in bioelectronics with stringent provisions of EU regulations. It provides a practical framework for developers and researchers, ensuring their AI innovations adhere to legal and ethical standards.
  arXiv  Detail & Related papers  (2024-08-27T14:59:27Z)
• An FDA for AI? Pitfalls and Plausibility of Approval Regulation for Frontier Artificial Intelligence [0.0]
  We explore the applicability of approval regulation -- that is, regulation of a product that combines experimental minima with government licensure conditioned partially or fully upon that experimentation -- to the regulation of frontier AI. There are a number of reasons to believe that approval regulation, simplistically applied, would be inapposite for frontier AI risks. We conclude by highlighting the role of policy learning and experimentation in regulatory development.
  arXiv  Detail & Related papers  (2024-08-01T17:54:57Z)
• 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)
• AuditNet: A Conversational AI-based Security Assistant [DEMO] [10.941722434218262]
  We propose a versatile conversational AI assistant framework designed to facilitate compliance checking on the go. Our framework automates the review, indexing, and retrieval of relevant, context-aware information. This AI assistant not only reduces the manual effort involved in compliance checks but also enhances accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-07-19T08:33:07Z)
• Securing the Future of GenAI: Policy and Technology [50.586585729683776]
  Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety. A workshop co-organized by Google, University of Wisconsin, Madison, and Stanford University aimed to bridge this gap between GenAI policy and technology. This paper summarizes the discussions during the workshop which addressed questions, such as: How regulation can be designed without hindering technological progress?
  arXiv  Detail & Related papers  (2024-05-21T20:30:01Z)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• A General Framework for Verification and Control of Dynamical Models via Certificate Synthesis [54.959571890098786]
  We provide a framework to encode system specifications and define corresponding certificates. We present an automated approach to formally synthesise controllers and certificates. Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks.
  arXiv  Detail & Related papers  (2023-09-12T09:37:26Z)
• Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities [26.50898051963262]
  We consider the technical half of the question: To what extent can AI experts vet an AI system for adherence to regulatory requirements? We investigate this question through the lens of two public sector procurement checklists.
  arXiv  Detail & Related papers  (2023-06-22T00:12:30Z)
• Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels [47.187609203210705]
  We propose a Neuro-Symbolic approach for automating the compliance verification of the electrical control panels. Our approach is based on the combination of Deep Learning techniques with Answer Set Programming (ASP) and allows for identifying possible anomalies and errors in the final product. The experiments conducted on a real test case provided by an Italian Company demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2023-05-17T10:29:02Z)
• Compliance Costs of AI Technology Commercialization: A Field Deployment Perspective [1.637145148171519]
  Many AI startups are not financially prepared to cope with a broad spectrum of regulatory requirements. Complex and varying regulatory processes across the globe subtly give advantages to well-established and resourceful technology firms. The continuation of this trend may phase out the majority of AI startups and lead to giant technology firms' monopolies of AI technologies.
  arXiv  Detail & Related papers  (2023-01-31T07:22:12Z)

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.