Cloud 101CircleEventsBlog
Join us for Cybersecurity Awareness Month! Strengthen your cyber resilience with essential security tips and resources for everyone.

Download Publication

AI Resilience: A Revolutionary Benchmarking Model for AI Safety - Japanese Translation
AI Resilience: A Revolutionary Benchmarking Model for AI Safety - Japanese Translation

AI Resilience: A Revolutionary Benchmarking Model for AI Safety - Japanese Translation

Release Date: 09/23/2024

This localized version of this publication was produced from the original source material through the efforts of chapters and volunteers but the translated content falls outside of the CSA Research Lifecycle. For any questions and feedback, contact [email protected].


The rapid evolution of Artificial Intelligence (AI) promises unprecedented advances. However, as AI systems become increasingly sophisticated, they also pose escalating risks. Past incidents, from biased algorithms in healthcare to malfunctioning autonomous vehicles, starkly highlight the consequences of AI failures. Current regulatory frameworks often struggle to keep pace with the speed of technological innovation, leaving businesses vulnerable to both reputational and operational damage. 


This publication from the CSA AI Governance & Compliance Working Group addresses the urgent need for a more holistic perspective on AI governance and compliance, empowering decision makers to establish AI governance frameworks that ensure ethical AI development, deployment, and use. The publication explores the foundations of AI, examines issues and case studies across critical industries, and provides practical guidance for responsible implementation. It concludes with a novel benchmarking approach that compares the (r)evolution of AI with biology and introduces a thought-provoking concept of diversity to enhance the safety of AI technology.


Key Takeaways: 

  • The difference between governance and compliance 
  • The history and current landscape of AI technologies 
  • The landscape of AI training methods 
  • Major challenges with real-life AI applications
  • AI regulations and challenges in different industries
  • How to rate AI quality by using a benchmarking model inspired by evolution

The other two publications in this series discuss core AI security responsibilities and the AI regulatory environment. By outlining recommendations across these key areas of security and compliance in 3 targeted publications, this series guides enterprises to fulfill their obligations for responsible and secure AI development and deployment.
Download this Resource

Prefer to access this resource without an account? Download it now.

Bookmark
Share
Related resources
AI in Medical Research: Applications & Considerations
AI in Medical Research: Applications & Consider...
AI Organizational Responsibilities - Core Security Responsibilities - Korean Translation
AI Organizational Responsibilities - Core Secur...
Don’t Panic! Getting Real about AI Governance
Don’t Panic! Getting Real about AI Governance
Reflections on NIST Symposium in September 2024, Part 1
Reflections on NIST Symposium in September 2024, Part 1
Published: 10/04/2024
Embracing AI in Cybersecurity: 6 Key Insights from CSA’s 2024 State of AI and Security Survey Report
Embracing AI in Cybersecurity: 6 Key Insights from CSA’s 2024 State...
Published: 10/04/2024
Secure by Design: Implementing Zero Trust Principles in Cloud-Native Architectures
Secure by Design: Implementing Zero Trust Principles in Cloud-Nativ...
Published: 10/03/2024
AI Legal Risks Could Increase Due to Loper Decision
AI Legal Risks Could Increase Due to Loper Decision
Published: 10/03/2024
CSA Global AI Symposium
CSA Global AI Symposium
October 22 | Virtual
Are you a research volunteer? Request to have your profile displayed on the website here.

Interested in helping develop research with CSA?

Related Certificates & Training