Guidelines, Hacks, and Generative Tools

Welcome to The AI Collective Word, your go-to source for the latest developments in AI security. In this edition, we've curated key insights to keep you informed and empowered in the ever-evolving digital security landscape.

Explore guidelines from global cybersecurity authorities on secure AI system development and learn how hackers leverage AI to bypass biometric security in banking.

Dive into the emerging trend of Generative AI tools for backup and disaster recovery, and discover how AWS Clean Rooms ML facilitates secure AI collaboration.

Stay ahead of the curve with our handpicked articles, designed to enhance your understanding of AI's role in safeguarding the digital realm. Share the knowledge – empower your network!

Guidelines for secure AI system development [published by UK National Cyber Security Centre (NCSC), the US Cybersecurity and Infrastructure Security Agency (CISA), and their international partners] This document provides guidelines for secure and responsible AI systems, covering four key areas: design, development, deployment, and operation. It is intended for AI system providers who use hosted models or external APIs. It helps them address novel and standard security vulnerabilities throughout the AI system life cycle.

Hackers use AI to bypass biometrics security [ITWeb] Hackers are using AI and advanced technologies to overcome biometric defenses in the financial sector, according to law firm Baker McKenzie. While biometrics, such as fingerprint and facial recognition, have become mainstream for identity verification, the continuous evolution of technology, particularly in AI, poses new threats. Experts recommend exploring advanced solutions like 4D liveness facial biometrics and complementary approaches such as behavioral biometrics to stay ahead of cyber threats and enhance security in the face of evolving hacking techniques.

Generative AI tools for backup and DR still in early days [TechTarget] Generative AI assistants are the new trend in data backup and disaster recovery, but they have not proven their value yet. They use chatbots and other features to automate tasks, but they may not change operations significantly, and they also face challenges in security and business justification.

7 Uses for Generative AI to Enhance Security Operations [The Hacker News] Generative AI is revolutionizing cybersecurity, utilizing artificial intelligence to generate realistic and diverse data outputs. In security operations, it plays a crucial role in detecting and preventing threats like malware, phishing, and data breaches by analyzing patterns and behaviors in large datasets. Seven practical use cases for Generative AI in cybersecurity include information management, malware analysis, tool development, risk evaluation, tabletop exercises, incident response, and threat intelligence, showcasing its potential to transform how cyber threats are detected and addressed.

AWS Clean Rooms ML lets companies securely collaborate on AI [TechCrunch] Clean Rooms ML is a new service from AWS that lets customers create AI models with partners without sharing data. It uses a small sample of records to generate more records that are similar but not identical. Customers can control the model outputs and delete them when needed. Clean Rooms ML is useful for applications like marketing, finance, and healthcare.

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