エピソード

  • The Future of AI: Ethics, Resilience, and Progress [10]:

    The Future of AI: Ethics, Resilience, and Progress [10]:
    2025/12/31
    In our final episode, we look to the future of AI one that is resilient, ethical, and trustworthy by design. We’ll discuss how to build AI systems that withstand attacks, integrate ethical considerations from the start, and adapt to evolving challenges. We’ll also explore how AI is being used for social good, from climate modeling to disaster response, and why continuous auditing and policy enforcement are essential for long-term success. Through examples like AI-driven environmental monitoring and optimized aid delivery, you’ll leave with a vision of how trustworthy AI can help solve some of humanity’s biggest challenges if we commit to building it responsibly. The road ahead isn’t just about managing risk it’s about shaping a future where AI works for everyone.#aiethics #aigovernance #TRISM #AISafety #aitrust #aisecurity #airisks #responsibleai #Cybersecurity #DigitalTransformation #AIEthics #airegulations #aiinclassroom #TechSecurity
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    11 分
  • The Threat of AI: Deepfakes and Disinformation [9]:

    The Threat of AI: Deepfakes and Disinformation [9]:
    2025/12/29
    AI isn’t just a tool for progress it can also be a weapon. In this episode, we confront the dark side of AI, from hyper-realistic deepfakes to automated disinformation campaigns that threaten democracy and trust. We’ll explore how synthetic media is being used in scams, how AI-driven bot farms manipulate public opinion, and what can be done to detect and defend against these threats. Through real-world examples like deepfake financial scams and election interference, you’ll see why proactive threat intelligence and public awareness are critical in the fight against AI misuse.#aiethics #aigovernance #TRISM #AISafety #aitrust #aisecurity #airisks #responsibleai #Cybersecurity #DigitalTransformation #AIEthics #airegulations #aiinclassroom #TechSecurity
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    14 分
  • Human vs. Machine: Striking the Balance [8]:

    Human vs. Machine: Striking the Balance [8]:
    2025/12/27
    As AI becomes more autonomous, what role should humans play? In this episode, we explore the concept of "Human in the Loop," where AI systems are designed to collaborate with rather than replace human experts. We’ll discuss meaningful human control, the risks of automation bias, and how to design AI tools that augment human intelligence. Through examples like aviation’s autopilot systems and AI-assisted cancer detection, you’ll learn why the future of AI isn’t about full autonomy it’s about effective teamwork between humans and machines.#aiethics #aigovernance #TRISM #AISafety #aitrust #aisecurity #airisks #responsibleai #Cybersecurity #DigitalTransformation #AIEthics #airegulations #aiinclassroom #TechSecurity
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    13 分
  • Balancing AI Growth and Privacy Protection [7]:

    Balancing AI Growth and Privacy Protection [7]:
    2025/12/25
    AI thrives on data, but privacy is a fundamental right. In this episode, we explore the tension between these two forces and the innovative solutions bridging the gap. We’ll dive into differential privacy, which protects individual identities while enabling data analysis, and federated learning, which trains models on decentralized data without compromising privacy. We’ll also discuss synthetic data, which mimics real datasets without exposing sensitive information. Through examples like Apple’s privacy-preserving features and Google’s federated learning for G-board, you’ll see how privacy and AI innovation can coexist if we design systems with both in mind.#aiethics #aigovernance #TRISM #AISafety #aitrust #aisecurity #airisks #responsibleai #Cybersecurity #DigitalTransformation #AIEthics #airegulations #aiinclassroom #TechSecurity
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    12 分
  • AI in Action: Managing Risks in the Enterprise [6]:

    AI in Action: Managing Risks in the Enterprise [6]:
    2025/12/23
    How do organizations actually implement AI TRiSM? In this episode, we move from theory to practice, exploring how businesses build governance structures, audit AI systems, and manage models throughout their lifecycle. We’ll discuss AI governance committees, model lifecycle management, and AI auditing, which evaluate risks and ethical implications before deployment. Through examples like banks’ fraud detection systems and third-party AI audits in healthcare, you’ll learn that effective AI governance isn’t a one-time check it’s a continuous process embedded in every stage of development and deployment.#aiethics #aigovernance #TRISM #AISafety #aitrust #aisecurity #airisks #responsibleai #Cybersecurity #DigitalTransformation #AIEthics #airegulations #aiinclassroom #TechSecurity
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    12 分
  • Who Controls AI? The Global Governance Debate [5]:

    Who Controls AI? The Global Governance Debate [5]:
    2025/12/21
    As AI’s influence grows, governments and organizations are racing to regulate it. In this episode, we navigate the global landscape of AI governance, from the EU AI Act’s risk-based approach to the U.S. NIST AI Risk Management Framework. We’ll also explore the role of international standards in creating a common language for AI trust and security. Through examples like the OECD AI Principles and GDPR’s impact on AI, you’ll understand why the era of self-regulation is ending and why compliance is becoming as critical as innovation.#aiethics #aigovernance #TRISM #AISafety #aitrust #aisecurity #airisks #responsibleai #Cybersecurity #DigitalTransformation #AIEthics #airegulations #aiinclassroom #TechSecurity
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    9 分
  • The Fight for Secure AI: Countering Adversarial Tactics [4]:

    The Fight for Secure AI: Countering Adversarial Tactics [4]:
    2025/12/19
    AI systems are vulnerable in ways traditional software isn’t. In this episode, we enter the cybersecurity battlefield of AI, where adversaries exploit weaknesses in machine learning models. We’ll explore adversarial examples subtle manipulations that trick AI into making mistakes and model poisoning, where training data is corrupted to sabotage performance. We’ll also discuss AI red teaming, the practice of ethically hacking AI to uncover vulnerabilities before malicious actors do. From tricking Tesla’s Autopilot with stickers to the downfall of Microsoft’s Tay chatbot, you’ll see why defending AI requires new strategies that go beyond firewalls and encryption.#aiethics #aigovernance #TRISM #AISafety #aitrust #aisecurity #airisks #responsibleai #Cybersecurity #DigitalTransformation #AIEthics #airegulations #aiinclassroom #TechSecurity
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    12 分
  • Explainable AI: Bridging Trust and Transparency [3]:

    Explainable AI: Bridging Trust and Transparency [3]:
    2025/12/17
    If an AI makes a life-changing decision like approving a loan or diagnosing a disease how can we trust it if we don’t understand why it made that choice? In this episode, we tackle the "black box" problem head-on, exploring the field of Explainable AI (XAI). We’ll break down key techniques like LIME and SHAP, which help demystify complex AI models, and discuss how fairness metrics can audit algorithms for bias. Through examples like Google’s "What If Tool" and DARPA’s XAI program, you’ll discover why transparency and accountability are not just technical challenges they’re the foundation of trustworthy AI.#aiethics #aigovernance #TRISM #AISafety #aitrust #aisecurity #airisks #responsibleai #Cybersecurity #DigitalTransformation #AIEthics #airegulations #aiinclassroom #TechSecurity
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    11 分