Скачать или смотреть AI vs Cyber Threats: Defenses, Attacks & Quantum Future

Descript Link: https://get.descript.com/968yizg2t4r3
🚀 AI is revolutionizing cybersecurity, but it's a double-edged sword! This deep dive explores how AI enhances defenses (threat detection, GNNs, Federated Learning) while also becoming a target and tool for attackers (adversarial attacks, model theft). Plus, a look at the quantum computing threat.
In this episode, you'll learn:
🛡️ AI-Powered Defenses: How ML (supervised/unsupervised), Deep Learning, and GNNs spot subtle threats faster and more accurately than traditional methods.
🔐 Privacy & Collaboration: Discover techniques like Federated Learning (FL) and Homomorphic Encryption enabling secure data sharing for better security models.
⚔️ AI Under Attack: Understand adversarial attacks (poisoning, evasion), model theft, prompt injection, and the unique vulnerabilities of AI systems.
🧠 Key ML Techniques: Explore the roles of Deep Learning (detection power vs. black box), Reinforcement Learning (adaptive defense), and NLP (threat intelligence).
☁️ Securing New Frontiers: See how AI helps protect complex IoT ecosystems, critical infrastructure (SCADA), and multi-cloud environments.
We break down the step-by-step process of:
AI analyzing vast security datasets to identify anomalies.
Training models collaboratively while preserving privacy (Federated Learning).
How attackers craft adversarial inputs to fool AI detectors.
Applying ML techniques like RL and NLP to specific cybersecurity challenges.
Preparing for the quantum threat with AI-assisted Post-Quantum Cryptography (PQC).
Compare traditional rule-based security with adaptive, AI-driven defenses. Understand the trade-offs between AI model power and explainability (black box problem).

Gain special insights into:
🔥 The shift towards adaptive security systems powered by continuous AI learning.
💡 Techniques like Self-Supervised Learning for Intrusion Detection (SSID) requiring no pre-labeled data.
🤝 Federated Learning as a "paradigm shift" for secure collaboration.
⚠️ The significant risks posed by data poisoning and model extraction attacks.
⚛️ AI's crucial role in developing and deploying Post-Quantum Cryptography (PQC).
Subscribe for more deep dives into AI and security! 👍 Like this video if you're navigating the AI cyber landscape, and comment below: What's the biggest AI security threat we face?

TIMESTAMPS:
00:00 Introduction to AI and Cybersecurity
00:57 AI in Threat Detection and Analysis
02:52 Advanced AI Techniques in Cybersecurity
03:29 AI in Vulnerability Management
04:30 Federated Learning and Privacy
05:38 Adversarial Attacks on AI
08:22 Machine Learning Techniques in Cybersecurity
10:30 AI in IoT and Cloud Security
12:28 Quantum Computing and Future Trends
13:28 Ethical Considerations in AI
15:06 Real-World Applications and Future Trends
17:42 Conclusion and Final Thoughts

TOOLS MENTIONED:
(Techniques/Concepts mentioned, rather than specific software tools in this transcript)
Machine Learning (Supervised, Unsupervised)
Deep Learning
Graph Neural Networks (GNNs)
Self-Supervised Learning for Intrusion Detection (SSID)
Natural Language Processing (NLP)
Federated Learning (FL)
Homomorphic Encryption
Reinforcement Learning (RL / MARL)
Explainable AI (XAI)
Generative AI
Post-Quantum Cryptography (PQC)
Digital Twins (Used in context of RL training)
(Companies like Darktrace, IBM, CrowdStrike, Nvidia, Veracode, AWS, Tenable, Palo Alto Networks mentioned as examples using AI)


CONTACT INFORMATION:
🌐 Website: ianochiengai.substack.com
📺 YouTube: Ian Ochieng AI
🐦 Twitter: @IanOchiengAI
📸 Instagram: @IanOchiengAI