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AI in Cybersecurity: How to Protect Against Evolving Threats, Stay Updated on Regulations, and Evaluate AI Tools
AI Cybersecurity, Regulations and Privacy Week in Review
Anne Borsohalmi
November 06, 2023
Dear readers, here’s your weekly recap on the latest news on AI security, regulations, and privacy.
In this issue, we’ve included some of the latest and most interesting articles, including:
The main point of AI in cybersecurity is protecting against evolving threats
The latest AI regulation updates in AI international governing boards
Key considerations when evaluating AI-powered tools
ML system improves the safety and efficiency of sodium-cooled fast reactors, a type of carbon-free nuclear power
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RISK AND SECURITY MANAGEMENT
Predictive AI in Cybersecurity: Outcomes Demonstrate All AI is Not Created Equally - The Hacker News
The main point of AI in cybersecurity is to achieve outcomes that protect against evolving threats. AI-based security solutions should be evaluated by their effectiveness and value, not hype.
Google is rewarding people who find bugs in its generative AI products, such as Google Bard and Google Cloud’s Contact Center AI. The company wants to make AI safer and more secure for everyone. Google has a list of what counts as a vulnerability and what doesn't in its security blog.
AI, cloud computing, and security are three high-growth technologies that are closely connected. Cloud enables AI to deliver limitless benefits but also raises new security challenges. This article explores how to address these risks with innovative solutions.
REGULATIONS
Governments race to regulate AI tools - Reuters
The latest AI regulation updates from the international governing bodies.
The U.S. government wants to ensure AI systems are safe and secure for the public. President Biden signed an order that requires AI developers to share their safety tests and data with the authorities. This is part of a global effort to create ethical and responsible AI guidelines.
G7 Countries Establish Voluntary AI Code of Conduct - TechRepublic
The G7’s AI code of conduct is a set of voluntary guidelines for developing advanced AI systems in a safe, secure, and trustworthy way. It covers the whole AI lifecycle, from design to deployment, and aims to prevent and mitigate risks, vulnerabilities, and misuse. It also requires transparency and accountability from the organizations that create and use advanced AI systems.
The letter is a response to the AI Safety Summit, where some companies argued for more regulation and less openness in AI development. The signatories disagree and say that openness, transparency, and broad access are essential for mitigating the harms of AI. They also accuse some companies of fear-mongering and trying to control the AI industry.
Platform Engineering
Cloud development has become more productive thanks to innovations like debuggers, IDEs, and cloud services. However, developers still face challenges such as learning new tools, understanding code, and testing. Generative AI can help by using large language models (LLMs) to complete, transform, explain, and test code. Generative AI tools can speed up development and reduce repetitive tasks.
Minimize the high costs of AI in cloud with FinOps - TechTarget
AI can increase cloud spending by consuming more resources and time, requiring more computing and storage, and performing frequent data transfers. To optimize costs, enterprises must collaborate across IT, finance, and AI teams and adjust their provisioning, sizing, and instance type choices according to AI workloads.
Sustainability
Advances in machine learning for nuclear power operations spell a brighter future for carbon-free energy - TechXplore
Scientists at Argonne National Laboratory have developed a machine learning system to improve the safety and efficiency of sodium-cooled fast reactors, a type of carbon-free nuclear power. The system monitors and detects anomalies in the liquid sodium coolant, which is vital to prevent corrosion and blockages. The system uses data from 31 sensors at a test facility that simulates the reactor conditions.
Resources
Generative AI for Everyone - DeepLearning
Generative AI for Everyone is a course by AI pioneer Andrew Ng that teaches you how to use generative AI in your work and life. You will learn the basics of generative AI, its applications and limitations, and how to engineer effective prompts. Some real-world applications you will explore are content creation, data augmentation, and style transfer. You will also practice with generative AI tools and explore the impact of AI on society. Duration: 3 hours. Introductory
This microlearning course introduces you to the key concepts of artificial intelligence (AI) and machine learning (ML) in health care. You will learn how big data affects AI and ML, how they differ from traditional programming, and what are their strengths and limitations in health care. You will also learn the steps to develop AI for health care settings.