Trust Architecture: Translating Financial Services Security Models to AI Governance

An examination of how established security frameworks from the payments industry provide a robust foundation for developing trustworthy AI systems at enterprise scale.

There's a Yoruba adage the elders love: "Ohun ti a fi s'ile la fi n'bode"—what we use inside the house is what we use at the gate. Simply put, the tools that serve us well in familiar spaces can protect us in new territories too.

After over a decade in payments and financial services before joining big tech, this wisdom hit home. When I started tackling AI governance challenges, I realised we weren't facing entirely new problems. We'd been solving these same trust and security challenges for years—we just called them something different.

Think about it. The payment industry moves trillions daily through systems that people trust with their life savings.People tap their cards without thinking twice. We figured out how to do that securely, transparently, and at massive scale. So why are we acting like AI governance is completely new territory?

What Keeps Me Awake at 3 AM (Apart from Suya Cravings)

When I transitioned from payments to AI, the parallels were almost uncanny. Look at what both domains wrestle with:

  • Every decision matters—mess up a payment, someone's rent bounces. Mess up an AI decision, someone's visa application gets wrongly denied.
  • Regulators are watching—and they want to understand exactly how decisions get made (trust me, BaFin, the ECB, and every Data Protection Authority from Dublin to Lagos don't play).
  • Privacy requirements that make you sweat—GDPR in Europe, NDPR in Nigeria, and everyone wants to know what you're doing with their data.
  • Everyone needs to trust the system—from the Oma next door to the tech bros in Berlin.
  • Schnell versus Sicher—that eternal German struggle of moving fast but keeping things safe.

Here's the thing: we've spent decades solving these exact challenges in payments. Why reinvent the wheel?

The Three-Layer Security Recipe (That Actually Works)

1. Layer Everything Like a Proper Moin-Moin

In payments, I learnt early that single points of failure are basically invitations for disaster. You know what works? Layers. Many layers.

Here's how I've learned to translate payment security layers to AI governance:

  • What we do in payments → What works for AI?
  • Identity verification → Where did this model come from? Who trained it?
  • Access control → Who gets to use this AI and for what purpose?
  • Data protection → Keep that training data private and secure.
  • Continuous monitoring → Is the AI still behaving like it did last week?

When I built the compliance check prototype, I created checkpoints everywhere. It's like having multiple quality checks at different stages—redundant but absolutely necessary.

2. Real-Time Risk Scoring (Because Hindsight is Expensive)

Here's something that will blow your mind—banks can tell if your card's been stolen before you even realise it's missing. In my experience with payment systems, they're watching patterns in real-time, scoring every transaction for risk.

  • Old school AI governance: "Let's audit the model every quarter."
  • Payment-inspired approach: "Let's score every single AI decision as it happens."

Imagine your AI suddenly starts rejecting loan applications or declining insurance contracts from a particular Postcode (Postleitzahl in German) at 5x the normal rate. Wouldn't you want to know immediately? Not three months later during some Quartalsreview?

3. Show Your Working (Like Secondary School Maths, But More Critical)

Remember when your maths teacher insisted you show every step? Turns out, they were preparing you for enterprise AI governance. In payments,we could reconstruct any transaction from years ago—every authorisation, every fee, every timestamp. This level of audit trail is standard in financial services due to regulatory requirements.

Your AI needs the same treatment:

  • What data went in?
  • What processing occurred?
  • Who reviewed it?
  • What came out and why?

It's not paranoia—it's proper German Gründlichkeit meets Nigerian attention to detail.

The Privacy Review Secret Sauce (That Nobody Talks About)

You know what's funny? Everyone focuses on the technical security, but the real magic happens in privacy reviews. After conducting a couple of privacy reviews throughout my career—from payment systems to AI deployments—I've realised they're the Rosetta Stone of trust architecture.

  • In Payments: We ask "What data are we collecting? Why? Who sees it? How long do we keep it?"
  • In AI: Same questions, but now add "What's the model learning? Could it memorise sensitive data? Who can query it?"

My CIPT, CIPM, CIPP/E certifications taught me to spot privacy risks in payment flows. My AIGP certification? Same skills, different playground. The patterns are identical:

  • Data Minimisation (Payment: only collect what you need for the transaction → AI: only train on necessary data)
  • Purpose Limitation (Payment: don't use payment data for marketing → AI: don't repurpose medical AI for hiring)
  • Rights Management (Payment: let users dispute charges → AI: let users challenge AI decisions)

Here in Germany, with GDPR watching everything like a strict Hausmeister, privacy reviews aren't optional—they're survival. But here's the thing: they actually make your AI better. When you're forced to explain why your AI needs each data point, you often realise it doesn't. Cleaner data, better models, fewer risks. Win-win-win.

The best part? Privacy reviews create a common language. When I sit with legal, compliance, and engineering, we all understand "data retention periods" and "processing purposes." Try explaining "model drift" to legal—I dare you! But "unauthorised data processing"? Everyone gets that immediately.

The Trust Triangle That Changed Everything

After years of implementing these systems, I developed what I call the "Trust Triangle." Picture this:

   Prevent Problems Before They Start
  /                                  \
 /                                    \
/                                      \
Detect Issues Fast -------------------- Fix Things Right
  • Prevent Problems Before They Start (learnt from payment systems):
    • Test exhaustively before going live
    • Write clear rules about access and usage
    • Ensure the right people have the right permissions
  • Detect Issues Fast (borrowed from fraud detection):
    • Watch for AI misbehaving in real-time
    • Alert when performance starts drifting
    • Flag unusual outputs before they cause damage
  • Fix Things Right (inspired by payment chargebacks):
    • Have a rollback plan (because things will go wrong)
    • Let humans override when necessary
    • Create clear appeals processes for affected people

Let's Talk About the Human Element

Here's what nobody admits—the most sophisticated technical framework means nothing if people don't understand it. I learnt this the hard way in payments. We'd build incredible security systems, and customers would still write their PIN on their card.

So here's what actually works: find your Trust Champions. These are the people who can explain why the AI made a decision to both the data scientist in their hoodie AND the Vorstand in their suit. They're like interpreters at a multicultural wedding—making sure everyone understands what's happening.

These champions don't just translate tech-speak. They build bridges between worlds. They turn "the model has a 0.93 AUC with demographic skew in these vectors" into "we need to fix this before it becomes a proper scandal."

The Future is Already Here (We Just Need to Connect the Dots)

Friends, AI is already making financial decisions. It's approving loans, detecting fraud, and managing investments from Frankfurt to Abuja. The convergence isn't coming—it's here. Organisations that realise this have serious Vorsprung:

  • Move schneller by adapting existing security playbooks
  • Sleep better knowing you're using proven governance approaches
  • Build trust quicker because stakeholders recognise the patterns

Think about how we went from "I'll write you a cheque" to "Just PayPal me" in basically one generation. That transformation required building massive trust infrastructure. AI is going through the same journey, just at German Autobahn speed.

So, What Now?

Look around your organisation. I guarantee you've got people who've solved trust problems before. Maybe they secured patient records at Charité, protected supply chains for Bosch, or built payment systems for Commerzbank. These folks are sitting on gold.

Here's my challenge to you: grab coffee (or Club Mate if you're feeling very Berlin) with them. Ask how they'd handle AI governance. The patterns that emerge will amaze you.

The frameworks we need aren't hiding in some expensive consultant's PowerPoint. They're in the systems we already trust with our money, our health records, our most sensitive data. We just need to connect the dots.

And here's the beautiful part—once you see these connections, you can't unsee them. Every payment security principle has an AI governance twin. Every compliance framework you've built can be adapted. Every trust relationship you've established can be leveraged.

The question isn't whether to build trust architecture for AI. It's whether you'll be among the first to realise we already have the blueprints.

Ready to build something trustworthy? Lass uns anfangen!

Adeteju Enunwa is an Engineering Program Lead who leverages emerging technologies to architect human-centric solutions and products, all built on a foundation of trust and responsible development.