AI Security: Keeping Up So the Machines Don’t Outrun Us
....must-read picks to help you stay ahead of emerging threats and the latest defense strategies in AI.
[article] Google Cloud highlights five critical GenAI security mistakes to avoid: prompt injections, data leakage, over-reliance on model outputs, inadequate access controls, and insufficient monitoring.
[article] Anthropic and Stairwell have partnered to enhance cybersecurity threat detection by leveraging Claude’s AI capabilities to analyze, summarize, and correlate vast amounts of security data for faster and more efficient incident response.
[research] This paper introduces LLM in the Loop, a framework that enhances AI-assisted security analysis by integrating large language models (LLMs) into security workflows, improving efficiency in tasks like vulnerability detection and threat analysis.
[research] Microsoft’s red teaming of 100 generative AI products revealed key takeaways on threat modeling, real-world attack simulations, and continuous adversarial testing, emphasizing the need for proactive security measures to mitigate emerging AI risks. Read the full white paper here.
[research] AI-supported spear-phishing study from Harvard (and Bruce Schneier) reveals that AI-assisted spear phishing attacks successfully deceive over 50% of targets, highlighting the growing sophistication of AI-driven cyber threats and the need for enhanced phishing defenses. Check out the complementary article here.
[resource] Publication from CSA on Securing LLM Backend Systems. This publication provides security guidance for designing and deploying LLMs safely, particularly when they make autonomous decisions or interact with external data sources
[resource] HITRUST Launches AI Security Assessment with Certification Setting Benchmark for AI Security Assurance.
[book] Machine Learning for High Risk Applications. This book is a practical guide for improving real-world AI/ML system outcomes while mitigating risks and addressing ethical considerations.