Hey everyone, Ira Whiteside here, back with another episode of the IVC Test podcast. If you’ve been following along, you know we’ve been peeling away the layers of how AI and data really work together to shape business decisions. Today, I wanna get into something that's, well, maybe not the most glamorous topic, but it’s crucial—compliance. We’re talking about how AI is changing the game when it comes to compliance checks in data governance.Now, compliance might bring to mind a lot of paperwork, endless audits, folks with clipboards—at least, that’s what I remember. I mean, take GDPR or HIPAA for instance. These aren’t just a few checkboxes on a form; they’re huge frameworks packed with requirements… and the stakes are pretty high if you get it wrong. But what’s exciting is how AI can automate compliance monitoring, and actually catch stuff in real-time that used to slip through the cracks.Let’s look at an example from finance—banks are under constant pressure to flag suspicious activities that could point to money laundering. AI systems now monitor transaction patterns, using anomaly detection, and can alert compliance teams within seconds if something looks off. Healthcare’s got a similar story—AI tools crawl through access logs for patient records to spot breaches of HIPAA rules, which, honestly, would take humans forever.I actually remember, I think back in—I wanna say it was... 2011 or 2012, somewhere around there, I was working with a team that was piloting some early AI-driven anomaly detection for ETL workflows in a large insurance firm. We set up a system to catch data flows that were sneaking around permission boundaries; it found a non-compliant process running on a Friday night, something nobody would’ve noticed until it caused real trouble. That kind of proactive compliance just wasn’t possible before AI. And, you know, as we discussed a couple episodes back, having the right metadata mapped up front makes these automated checks way more accurate—so it's all connected to what we've talked about before.But let’s not stop with compliance, because data quality goes hand-in-hand with governance. If your data’s a mess, well, your decisions aren’t gonna be much better. That’s where modern AI—especially machine learning algorithms—has made a real difference for profiling, cleansing, and deduplicating large, messy datasets.Retail giants, for example, these folks process millions of transactions every day—no human could possibly check all those for duplicates or weird outliers. AI comes in, profiles the data, finds inconsistencies, and even suggests ways to clean things up automatically. But, and here’s a big but, there’s always that tension between automation and good old human judgment. I’ve seen cases where the AI gets a little too confident—maybe it flags a bunch of legitimate entries as duplicates, or it “cleans” something in a way that breaks a business rule. That’s when you really need a knowledgeable analyst to step in and say, “Wait, that doesn’t make sense.”And, look, in my—let’s see, am I up to 33 years now?—career, I’ve seen a few horror stories where bias creeps into those automated checks. For instance, an algorithm might overlook records from a minority language group just because it was underrepresented in the training data. So there’s risk—anyone saying otherwise, they’re kidding themselves. The sweet spot seems to be having AI take care of the grunt work, but keeping humans in the loop for the edge cases and sense-checks. That’s what some of the best companies do—kinda like that previous episode where we talked about AI telling your data’s story, you still need humans around to make sure it’s the right story.Now, regulations aren’t set in stone—they’re always changing, and sometimes it feels like every time you blink, there’s a new requirement. That used to be a nightmare. But, thankfully, AI is getting better at keeping pace with these changes.Take natural language processing—NLP for short. AI now can read through pages and pages of new or revised regulations, pick out the parts that matter, and even recommend how to update your privacy protocols or data handling flows. I recently saw a global tech firm put an AI-powered governance engine in place. When new international data privacy laws hit—think of all those different standards from the EU, the US, parts of Asia—they were able to configure the AI system to scan the updated rules and align internal processes basically on the fly. It wasn’t perfect, but it was a far cry from the old days where a team would spend weeks combing through legal text, hoping not to miss something critical.I might be showing my age here, but I remember doing that kind of manual mapping—it took forever, and, well, half the time you ended up chasing your own tail. Today, the shift toward automated, AI-driven compliance still needs solid business logic and oversight, sure, but it’s a huge leap forward. It really ties back to that Information Value Chain framework we kicked off in Episode 1. More automation, but it’s gotta serve business needs first.Alright, I think that’s a good place to leave it for today. AI’s role in data governance is only getting bigger—and trickier—so we’ll definitely keep digging into these topics in future episodes. Thanks for tuning in, and, no promises, but I’ll try to keep the compliance talk exciting next time!

Episodes

AI in Data Governance for Business