Exploring how artificial intelligence, design thinking, and cybersecurity shape the future of work. On this episode, we have Luis Maverick Gabriel, Piolo Justin Cabigao, Jon Prado, and George Louis Jose joining us to discuss AI, design, security, and the next wave of IT careers. The future of IT isn't just about code—it's about the intersection of cutting-edge fields. This episode dives into the exciting career paths emerging from the fusion of artificial intelligence, design thinking, and cybersecurity. We'll explore how these disciplines are creating new roles and opportunities, from designing secure AI systems to building user-friendly security tools. Join us to learn how to prepare for the jobs of tomorrow by bridging the gap between these powerful fields. What new roles or specializations have you seen emerge at the intersection of these three fields? (Generalization) We're seeing exciting new roles emerge that bridge these disciplines. For instance, there's the AI Security Engineer, who works to secure machine learning models and data pipelines from adversarial attacks. Another is the UX Security Researcher, who designs user-friendly security features and studies how users interact with security prompts. We've also seen the rise of AI Ethicists, who ensure AI systems are fair, transparent, and don't introduce bias, a role that sits at the intersection of AI, design, and a broader, more philosophical type of security. For a student, what's a good first step to start building skills in all three areas? (Generalization) A great first step is to find a project that combines them. Instead of studying each field in isolation, try building a simple application that requires all three. For example, create a small web app with a machine learning component (AI), a straightforward user interface (design), and a focus on basic authentication and data protection (security). This project-based learning approach forces you to understand how the concepts interact in the real world, providing a holistic understanding that is highly valuable to employers. Can you describe a project where your team successfully integrated AI, design, and security from the start? (Generalization) A good example would be the development of an intelligent fraud detection system. From the outset, the security team worked with developers to secure the data pipeline that fed the AI model, ensuring it was encrypted and tamper-proof. The AI team focused not only on building an accurate model but also on making it explainable, so that a fraud analyst could understand why a transaction was flagged. The design team then created a user interface that clearly presented this information to the analyst, ensuring they could act quickly and confidently, turning complex data into a simple, actionable workflow. How has the rise of AI changed what's considered a "secure" product? (Generalization) The rise of AI has added new layers to what's considered a secure product. Beyond protecting against traditional attacks like SQL injection, we must now defend against data poisoning, where an attacker manipulates the data to corrupt the AI model's output. A secure product must also be robust against adversarial attacks, where an attacker feeds the model specially crafted inputs to make it fail. This means that security now extends to the integrity and reliability of the data and the model itself, not just the code or infrastructure.
