Data Poisoning to Hallucinations: The Many Risks of AI Part 1

Model Drift to Bias and Discrimination: The Many Risks of AI: Part 2

In Part 2 of this Lunchtime BABLing series on AI risk, Dr. Shea Brown, CEO of BABL AI, is joined again by Jeffery Recker to continue their lightning-round exploration of the real challenges organizations face when deploying AI. This episode dives deeper into critical concepts such as model drift, bias vs. discrimination, and growing explainability gaps in modern AI systems — especially as organizations increasingly rely on large language models and automated decision-making tools. Together, they discuss: -What model drift is and how organizations can detect and manage it -Why users (not just developers) should understand performance drift in AI systems -The important distinction between statistical bias and illegal discrimination -How bias can emerge even when demographic data isn’t explicitly used -The role of diversity of thought and structured risk assessments in uncovering AI risks -Why explainability is becoming harder as AI models grow more complex -The trade-offs between performance, trust, fairness, and regulatory compliance The conversation also explores broader questions around how AI is being used today, the limitations of “black-box” systems, and why validation, testing, and governance are becoming essential capabilities for organizations adopting AI at scale. Check out the babl.ai website for more stuff on AI Governance and Responsible AI!

Data Poisoning to Hallucinations: The Many Risks of AI Part 1

Data Poisoning to Hallucinations: The Many Risks of AI | Part 1 In this episode of Lunchtime BABLing, Dr. Shea Brown, CEO of BABL AI, is joined by Jeffery Recker for a fast-paced, unscripted deep dive into the real risks behind today’s AI systems. From data poisoning and model inversion to prompt injection, membership inference, and AI hallucinations, this lightning-round conversation breaks down the security, governance, and reliability challenges organizations must understand before deploying AI at scale. But this episode doesn’t stop at definitions. Shea and Jeffery also explore: - The difference between direct vs. indirect prompt injection - Whether AI hallucinations can ever truly be “solved” - Why AI isn’t a truth machine - Whether we’re using AI the wrong way - What responsible validation should look like in enterprise AI deployment As AI systems move from experimentation into real-world decision-making, understanding these risks isn’t optional — it’s foundational. If you're working in AI governance, assurance, compliance, risk, or deploying AI inside your organization, this conversation will help you think more critically about how these systems actually behave. 🎯 Take the FREE assessment here: https://shea-1mb3pmep.scoreapp.com/ Check out the babl.ai website for more stuff on AI Governance and Responsible AI!

AI Test, Evaluation, & Red Teaming Specialist Bootcamp

In this episode of Lunchtime BABLing, BABL AI CEO Dr. Shea Brown introduces the new AI Test, Evaluation, & Red Teaming Specialist Bootcamp—a hands-on, technical program designed to train the next generation of AI assurance professionals. Drawing directly from BABL AI’s internal methodologies used to audit and evaluate high-risk AI systems across industries, this bootcamp addresses one of the most critical gaps in the AI ecosystem: the lack of practical training in how to design, execute, and interpret rigorous AI testing and red teaming in real-world contexts. Dr. Brown explains: -Why AI testing, evaluation, and red teaming are essential for high-risk AI systems -How BABL AI developed its internal, risk-driven testing and assurance frameworks -The difference between auditing AI systems and directly evaluating and validating them -What participants will learn during the five-week, hands-on bootcamp -The prerequisites, structure, and technical depth of the program -How this bootcamp will evolve into BABL’s new AI Test, Evaluation, & Red Teaming Specialist Certification -This exclusive early adopter cohort is limited to approximately 30 participants and is designed for professionals with foundational knowledge in AI auditing, governance, or assurance who want to develop practical technical capabilities in AI evaluation and red teaming. -Participants will learn how to move systematically from an AI use case to defensible test results—building real test plans, executing evaluations, and developing assurance-relevant conclusions using BABL’s proven frameworks. Take the test to see if you are a good candidate for the AI Test, Evaluation, & Red Teaming Specialist Bootcamp: https://zfrmz.eu/RBroC4VLZ9I41ihKl1XV Learn more about BABL AI Certifications: www.babl.ai About Lunchtime BABLing: Lunchtime BABLing is hosted by Dr. Shea Brown, CEO of BABL AI, an independent AI assurance firm that audits algorithms for bias, risk, and governance. The podcast explores AI auditing, governance, regulation, and technical assurance practices shaping the future of trustworthy AI. Check out the babl.ai website for more stuff on AI Governance and Responsible AI!

An Interview with Mert Çuhadaroğlu

In this episode of Lunchtime BABLing, BABL AI CEO Dr. Shea Brown sits down with Mert Çuhadaroğlu, Program Manager of BABL AI’s AI & Algorithm Auditor Certification Program, for an in-depth conversation about careers in AI governance, responsible AI, and what it really takes to become an AI auditor. Mert shares his unique professional journey — from banking and finance, to career coaching and publishing, to becoming a leading figure in AI ethics and auditing. Now based in Istanbul, Mert plays a critical role in guiding and evaluating BABL AI certification students, including reviewing capstone projects and supporting professionals from a wide range of backgrounds. Together, Shea and Mert discuss: What makes BABL AI’s AI & Algorithm Auditor Certification different from other AI governance programs Whether you need a technical background to succeed in AI auditing The real-world demand for AI auditors and AI governance professionals Common career paths for certification graduates What students actually do in the capstone project (including LLM and generative AI use cases) How BABL AI’s certifications compare to other industry credentials An overview of BABL AI’s additional certification programs, including EU AI Act Quality Management Systems, AI Governance for Business Professionals, and AI for Legal Professionals This episode is both a behind-the-scenes look at BABL AI’s training philosophy and a practical guide for anyone considering a career in AI assurance, audit, or governance. Check out the babl.ai website for more stuff on AI Governance and Responsible AI!

Diving into the AI Compliance Officer

What does a Chief AI Compliance Officer actually do—and does your organization secretly need one already? 🤔 In this episode of Lunchtime BABLing, BABL AI CEO Dr. Shea Brown is joined by co-hosts Jeffery Recker and Bryan Ilg to unpack what it really takes to own AI risk, compliance, and governance inside a modern organization. Drawing on BABL AI’s AI Compliance Officer Program and years of audit work, they break down the real pain points leaders are facing and how to move from confusion to a concrete plan. Whether you’ve just been handed “AI compliance” on top of your day job, or you’re building AI products and worried about regulations, this one’s for you. In this episode, they discuss: What a Chief AI Compliance Officer role looks like in practice – Why it often lands on general counsel, chief compliance officers, or chief AI officers – Why this work can’t be owned by one person alone The 3-part structure of BABL AI’s AI Compliance Officer Program AI foundations – Governance, AI management systems, policies, procedures, and documentation Fractional AI Compliance Officer support – Access to BABL’s research and audit team on an ongoing basis Continuous monitoring & measurement – Keeping up with self-learning, changing AI systems over time How to build an AI system inventory and triage risk – Simple rubric for identifying high, medium, and low-risk AI systems – When to treat a system as “high risk” by default – Why simplicity is the antidote to feeling overwhelmed Key AI risks every organization should know about – Data poisoning and how malicious instructions can sneak into your systems – Shadow AI (employees using unapproved tools like personal ChatGPT accounts) – Model & data drift and why “it worked when we launched it” isn’t good enough – How these risks connect to reputation, regulatory exposure, and business strategy Why governance, risk & compliance (GRC) is not a “brake” on innovation – How good governance actually lets you move faster and more confidently – The value of a “SWAT team” style AI compliance function vs. going it alone Who should watch/listen? General counsel, chief compliance officers, chief risk officers Chief AI / data / technology leaders Product owners building AI-powered tools Anyone who’s just been told: “You’re now responsible for AI compliance.” 🫠 Check out the babl.ai website for more stuff on AI Governance and Responsible AI!