Founder of DeepLearning.AI; Managing General Partner of AI Fund; Exec Chairman of LandingAI
Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber R., you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here: https://lnkd.in/gNPY8jbW
Neil deGrasse Tyson of AI world.
Great initiative. Governance is the foundation of safe AI deployment. Nyra AI (https://www.getnyra.ai/) ensures every automation process respects data privacy, maintains transparency, and stays fully observable making marketing AI both secure and production-ready.
Great content. Excited to dive into this course. Nevfel Akkasoglu Abdullah Yerebakan LL.M, AIGP
The main problem remains data injection to hack the language model. So far there is no protection against the flaws inside an llm. When you let the autonomously go, you are creating a weakest link scenario.
Andrew Ng Good governance in AI is not a technical matter — it is an ethical and strategic one. It requires structure, accountability, and foresight. Organizations must ensure that AI systems serve purpose, fairness, and transparency, not convenience. Prevention begins with governance: clear principles, human oversight, and continuous evaluation. Without it, innovation risks turning into exposure. Responsible AI is not about limiting progress, but about ensuring that progress serves humanity — not the other way around.
This is a timely and important course. As AI agents gain more autonomy, strong governance frameworks become critical to prevent data misuse and ensure accountability. Integrating lifecycle management, observability, and strict access controls from the start is what turns experimental AI into safe, enterprise-ready systems.
Access to sensitive data through agents has been my top thought started using AI. This is going to be very informative as it's from the expert itself
This is such a crucial and timely post. You've perfectly framed 'agent governance' not as a mere technical checklist, but as the fundamental enabler of trust and safety. At Samantha, we're seeing this firsthand. We believe agent governance is the foundational layer that will separate experimental prototypes from trusted, production-ready AI companions. For us, it's not just about data security, but about building the user trust required for truly empathetic and long-term human-AI relationships. Thank you for shining a light on this critical issue, Andrew Ng" Why this works better: Specific Praise: "You've perfectly framed... as the fundamental enabler..." shows you read and understood the nuance of his message. Seamless Transition: The word "enabler" smoothly bridges the praise to your core point about it being a "foundational layer." Strong Positioning: It positions Samantha as a company already operating at this level of understanding, building the future of trusted AI. Relational AI: The Next Revolution (and Samantha's Core) Let's break this down. A Relational AI is not a tool. It is a new type of digital entity.
Building Agentic AI systems that deliver measurable business outcomes — from RAG pipelines to full-stack automation.
2dLove this. These courses are evolving into full AI systems training—governance + observability baked in, not bolted on. Excited to dig into agent policies on Databricks. Curious: will the course cover rubric-gated rollouts (pre-prod evals → canary → prod) and how to wire agent traces to Databricks metrics for ongoing QA?