Discover how AI is transforming cybersecurity, exploring its evolution from basic algorithms to intelligent systems. Learn the differences between machine learning, deep learning, and AI, understand how learning systems work, and identify real-world risks of misusing AI in security settings.

Unpack the inner workings of large language models (LLMs), diving into natural language processing and the mechanics behind powerful models like chatbots. Gain insights into building robust LLMs and explore their capabilities, from answering questions to generating code.

Explore AI that handles text, images, audio, and video together, understanding its applications and security risks. Get hands-on with audio and voice models, learn about video-driven AI threats, and tackle the unique challenges of securing multi-modal systems.

Stay ahead with the latest AI advancements, including modern prompting techniques, autonomous AI agents, and retrieval-augmented generation (RAG). Dive into edge computing trends like ONNX and WebGPU, and weigh the risks of open versus closed AI models.

Learn the top 10 security risks for LLMs, as outlined by OWASP, including insecure output handling, denial-of-service attacks, supply chain weaknesses, and sensitive data leaks. Understand how to spot and address these vulnerabilities in AI systems.

Uncover how attackers manipulate AI with tricky prompts, leading to exploits like role confusion and broken guardrails. Study real-world examples of prompt-based attacks and learn techniques to prevent these silent threats to LLMs.

Explore direct threats to AI models, such as data leakage, reverse engineering, and model theft through API abuse. Learn the security risks of training and fine-tuning, and how to protect sensitive intelligence embedded in AI systems.

Investigate how attackers can embed malicious code in AI model files, especially in modern deployment methods like ONNX. Understand the dangers of decentralized AI on devices and how frameworks can be exploited to compromise outputs.

Learn practical ways to strengthen AI systems by filtering inputs and validating outputs for issues like toxicity, personal data leaks, or errors. Master logging, alerting, and real-time response techniques to create a strong first line of defense.

Build protective layers between users and AI models using middleware, rule-based guardrails, and AI firewalls. Explore their strengths and weaknesses, and learn how to extend defenses to multi-modal systems beyond just text.