AI risk is often discussed in broad, abstract terms—bias, privacy, “safety,” compliance. But when organizations actually deploy AI systems, the failures rarely arrive as philosophical problems. They show up as practical breakdowns: contaminated training data, leaked proprietary information, stolen models, unpredictable outputs, and tools that behave confidently even when they are wrong.

That’s the focus of the newest episode of Lunchtime BABLing. BABL AI CEO Dr. Shea Brown is joined by Jeffery Recker for a fast-paced, conversation on the many risks that sit underneath modern AI systems. The goal isn’t to sensationalize the threat landscape. It’s to clarify what these risks actually mean, why they matter for real organizations, and what responsible teams should be doing before they deploy AI at scale.

From Data Poisoning to Data Hygiene

The episode opens with one of the most misunderstood but increasingly relevant risks: data poisoning. In simple terms, data poisoning is the intentional contamination of training data—or the information fed into an AI system—so that the model learns patterns it shouldn’t, behaves unreliably, or even contains hidden “backdoor” behaviors that can be triggered later.

Shea explains that the most important defense is not a magic tool. It is discipline: data governance, provenance tracking, and data quality controls. As organizations build AI systems using proprietary documents, third-party sources, and retrieval-augmented generation workflows, they need to know exactly where information comes from and what is embedded inside it. Without that hygiene, teams may never notice the issue until the model behaves strangely in production.

Model Inversion and the Quiet Economics of “Stealing” AI

The conversation then moves into the risks that emerge when organizations expose models through APIs and public endpoints. Shea describes model inversion as an attacker’s attempt to infer what’s inside a model by sending inputs, observing outputs, and gradually reconstructing something that behaves like the original system.

This is not a casual prank. It is a resource-intensive process that becomes feasible when an organization fails to monitor usage patterns, enforce rate limits, and detect abnormal access behavior. In short: if someone is “blasting” your model with suspicious volumes of requests, it may not be normal usage. It may be an extraction attempt.

Membership Inference and What Training Data Really Reveals

One of the most important privacy risks discussed is membership inference. The concept is deceptively simple: can someone determine whether a specific person’s data—or a specific document—was included in the training dataset of a model?

This matters for two reasons. First, it challenges claims about what data a model was or wasn’t trained on. Second, it reveals how privacy risks can persist even when organizations believe data has been anonymized. Shea notes that membership inference techniques have been studied for years in privacy research and remain a critical part of the broader AI assurance landscape.

Prompt Injection, Jailbreaks, and the Problem of “Instructions in Disguise”

The episode also tackles prompt injection—one of the most visible risks in the generative AI era. Shea and Jeffery draw a useful distinction between direct prompt injection (where a user intentionally tries to manipulate the model) and indirect prompt injection (where malicious instructions are hidden inside external content like websites, documents, PDFs, or emails).

Indirect prompt injection is particularly dangerous because it can appear as normal business content. A model might ingest a document, interpret hidden instructions as higher-priority directives, and behave in ways that expose internal information or override the intended task. This is less about clever tricks and more about fundamental controls: input sanitization, contextual isolation, and clear boundaries for what the model is allowed to access.

Hallucinations and Why AI Isn’t a Truth Machine

No risk is more widely discussed than hallucinations—and for good reason. Shea defines hallucinations in plain terms: the system produces a confident answer that is simply wrong. But the conversation doesn’t linger on obvious examples. Instead, it digs into why the problem persists.

Shea argues that hallucinations are not a bug that will be permanently eliminated by scaling alone. The probabilistic nature of modern machine learning means there is always a nonzero chance of divergence from the “correct” answer, especially when the system is trained to be helpful and to respond rather than to admit uncertainty.

More importantly, the discussion highlights a deeper issue: truth itself is often context-dependent and socially constructed. Language evolves. Scientific understanding changes. Ethical norms shift. The expectation that AI will function as a flawless truth engine is not just unrealistic—it reflects a misunderstanding of what these systems are built to do.

Are We Using AI Wrong?

One of the most thought-provoking segments flips the question back onto the people using AI. Shea’s answer is nuanced. It isn’t that organizations are using AI for the “wrong” things. It makes sense to use AI to draft, summarize, reason, and support decisions, because those are tasks humans do. The problem is that people are using AI with too much confidence and too little validation.

Shea offers a practical analogy: you wouldn’t treat a smart non-lawyer as a legal authority just because they can speak persuasively. Yet organizations often treat AI that way—accepting outputs as expertise without the controls that would justify trust.

Validation as the Missing Layer

The episode’s core message is consistent with BABL AI’s broader mission: AI needs verification and validation. The real enterprise value is not just deploying models; it is building the testing infrastructure, monitoring, and assurance processes that confirm those models are behaving as intended in their real-world context.

For organizations deploying generative AI, these risks are not edge cases. They are baseline operational realities. And for professionals building careers in AI governance, this episode serves as a reminder that modern AI assurance requires more than policies—it requires technical understanding, practical testing, and humility about what AI can and can’t reliably do.

Where to Find Episodes

Lunchtime BABLing can be found on YouTube, Simplecast, and all major podcast streaming platforms.

Need Help?

Interested in building practical skills in AI governance and auditing? Visit BABL AI’s website for courses, certifications, and resources on AI risk management, algorithmic audits, and compliance.