AI Development Life Cycle (Part 1)

Every security control, risk mitigation, and compliance obligation in an AI system depends entirely on how well the foundation is built. Get these phases wrong, and weaknesses propagate forward, becoming progressively more expensive to fix. In this video, we visually break down the first three phases of the OECD AI Development Life Cycle: Plan and Design, Collect and Process Data, and Build and Adapt the Model, all mapped out so you can see exactly where governance controls must be applied from day one. 📌 What You'll Learn: 0:00 – Introduction 1:12 – Phase 1: Plan & Design (Business Objective, Stakeholder Alignment, Feasibility, Risk Assessment, Scope, Prototyping) 5:28 – Phase 2: Collect & Process Data (Data Requirements, Collection, Quality Assessment, Cleaning & Dataset Usage) 8:35 – Phase 3: Build & Adapt the Model (Algorithm Selection, Model Training, Explainability, Event Recording & Human-in-the-Loop) 📌 Key Takeaways: → Before writing a single line of code, justify why AI is the right approach. Choosing AI when a simpler solution would suffice introduces unnecessary complexity, cost, and risk. → Getting security and compliance at the table in Phase 1 is substantially cheaper than remediating their absence during deployment. Exclude them early, and critical threats go unidentified until they're embedded in the design. → Third-party data is a supply chain risk. Verify the integrity of external data, check for tampering or poisoning, and confirm collection methods meet your security and privacy standards before it enters your pipeline. → Low-quality or tampered data can enable data poisoning attacks, where malicious inputs manipulate the model's behavior. Catching integrity issues early is as much a security exercise as a quality control measure. → If a model's decisions cannot be explained, they cannot be audited, challenged, or defended when they cause harm. In regulated industries, deploying an unexplainable model is itself a compliance risk. → An AI system without robust, tamper-proof logging is effectively not auditable. When an incident occurs, logs are the first thing investigators and regulators will ask for. 🎯 Who Is This For? ✅ Professionals preparing for AAISM certification exam ✅ Designed for professionals aiming to grow their career in AI Security ✅ Anyone who wants to learn key concepts of AI security, governance and risk If this video helped you, LIKE 👍, COMMENT 💬, and SUBSCRIBE 🔔. I personally reply to every question. 📚 Ready to study smarter and master your certification prep? 👉 Start your FREE 7-day trial of AAISM course: https://www.sutraacademy.ai/aaism-course-page 🔗 Connect With Me: 🌐 Website: https://www.sutraacademy.ai/ 💼 LinkedIn: https://www.linkedin.com/in/himanshusutratech/