
How Hackers Attack AI: The New Battle to Secure Intelligent Machines | Harriet Farlow
Security by DefaultShow Notes
This podcast episode delves into the intricate nexus of artificial intelligence and security, featuring an enlightening conversation with Harriet, the author of a newly released book Practical AI Security. We explore her compelling journey from a background in physics and anthropology to becoming a pivotal figure in the realm of cybersecurity, particularly focusing on the challenges posed by adversarial machine learning. Harriet elucidates the pressing necessity for organizations to comprehend and mitigate the security vulnerabilities inherent in AI systems, as well as the broader implications for national security. Our discourse also addresses the critical need for collaboration between cybersecurity professionals and AI developers to ensure that security considerations are embedded within AI design from the outset. Ultimately, we aim to provide our audience with a profound understanding of the evolving landscape of AI security and the imperative of safeguarding these transformative technologies. 🎙️ Security by Default Podcast Practical AI Security: Attacking, Defending, and Securing the Future of AI With Harriet Farlow — Founder of Mileva Security Labs & Author of Practical AI Security
Artificial Intelligence is transforming the way we build technology, automate decisions, analyze data, and solve some of the world’s biggest challenges. But as AI becomes more powerful and more deeply embedded into our lives, one critical question becomes increasingly important:
How do we secure AI itself? In this episode of Security by Default, host Joseph Carson is joined by Harriet Farlow, AI security researcher, founder of Mileva Security Labs, and author of “Practical AI Security: A Hands-On Guide to Attacking, Defending, and Securing Modern AI Systems.” Together they explore the rapidly evolving world of AI security, adversarial machine learning, and why understanding how AI works is essential before we can protect it.
About This Episode AI is often described as the next technological revolution, but securing AI requires us to rethink many traditional cybersecurity approaches. Unlike conventional software, AI systems are built on data, probability, optimization, and learning models. They do not always fail in predictable ways, and vulnerabilities are not always solved with a simple patch. Harriet shares her fascinating journey from studying physics and anthropology to working in data science, national security, and artificial intelligence, eventually discovering the world of adversarial machine learning — where attackers attempt to manipulate and disrupt AI systems themselves. This conversation goes beyond the hype and explores what defenders, developers, and organizations need to understand as AI becomes a critical part of modern technology.
What You Will Learn 🤖 Why AI Security Matters More Than Ever AI is becoming part of software development, business operations, healthcare, finance, critical infrastructure, and cybersecurity itself. As adoption accelerates, organizations must move beyond simply asking: “How can we use AI?” and start asking: “How do we secure AI?”
🧠 Understanding How AI Really Works Harriet explains why machine learning systems are fundamentally different from traditional software. AI systems are: Probabilistic rather than deterministic Dependent on training data quality Designed around optimization Continuously influenced by changing environments
Understanding these foundations is essential for anyone responsible for protecting AI.
🔓 The World of Adversarial Machine Learning What happens when attackers stop targeting only applications and infrastructure… …and start targeting the AI model itself? The episode explores: Model manipulation Data poisoning AI weaknesses Training challenges Unexpected behaviors The difficulty of understanding model decisions
🛠️ How Do You Patch AI? One of the biggest questions facing cybersecurity professionals today: If AI learns something wrong, how do we fix it? Traditional secu
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