Author: joeshaw

There’s a particular kind of email that arrives at 3:12 a.m., when I’m asleep and at my most vulnerable. It’s from my bank, which insists on addressing me like a Victorian suitor: “Mr Shaw, we have important news about your credit score.”

I imagine the bank leaning over my bedside, shaking me awake.

“MR SHAW … MR SHAW … something’s happened.”

Bleary-eyed, I brace myself. Identity theft? Fraud? A long-lost inheritance?

No. My credit score is up three points. Three whole points. A shift so minuscule it could be caused by nothing more than the gravitational pull of a passing pigeon.

And yet they send a message every single day, as if my credit score is a fragile preemie they’re keeping alive in an incubator. God forbid you buy a car. Then the messages multiply like fruit flies. “New activity detected!” they warn, as if you didn’t know that you were the one who bought a Honda CR-V and not a cartel laundering money through a dealership in Akron. It’s a whole industry built on telling you things you already know. Except louder.

Then there are the Employee Assistance Programs. Every company claims to have one, printed in a cheerful PDF with stock photos of improbably diverse people smiling at clipboards. They’re always “robust,” “comprehensive,” and “here for you,” by which they mean: Three complimentary counseling sessions … every other year … with a social work intern only available on Tuesdays … between 2:00 and 2:30 a.m.

It’s the corporate version of a parent saying, “We support your dreams,” while handing you $7.53 and a bus schedule from 1998.

I once tried to schedule one of these sessions, and I swear the process had the same energy as trying to book a tee time at Augusta National. “We’re sorry, the calendar is full until the next fiscal quarter,” the intern told me, chewing what sounded like homework. “But we do have an opening on Leap Day at half past midnight, provided Mercury isn’t in retrograde.”

I hung up thinking: This is not an assistance program. This is a scavenger hunt. But they get to brag about it at the All-Hands meeting like they’ve personally cured loneliness.

Fast-food restaurants do the same thing with their charity programs. You’re standing there, just trying to buy a taco — one taco, a humble thing — when the cashier, who hasn’t blinked since you walked in, asks, “Would you like to round up your order to support our children’s literacy foundation?”

Ah yes, the foundation. The one whose website shows glossy photos of happy children reading books, while the annual financial report shows that 95% of donations went to “administrative overhead,” which is corporate code for someone leased a boat.

Really, the restaurant is getting a tax write-off on money I supplied, which I believe is the economic equivalent of being pickpocketed and commended for my generosity. But they beam about it. They act like they invented charity. Meanwhile, somewhere, an actual child is squinting at a book printed in 1973. 

Everywhere you go, companies are trying to convince you they’re changing the world, that your life is measurably better because they exist. They post on LinkedIn about “empowerment” and “transformation” and “our mission to elevate the human experience,” while providing benefits that could barely elevate a houseplant.

Bright packaging around an empty box. Movement without meaning. A big pile of nothing.

And I can’t help thinking: where are the companies actually doing good? The ones who fix things instead of diagnosing them? The ones who don’t brag about their kindness like it’s a new product launch? The ones who don’t need twelve cents from my taco to become decent?

Because I’d give those companies all my extra taco bucks. Even the nickels. Hell, I’d even let them email me at 3:12 a.m. As long as it meant something.

There’s a kind of magic that happens at a little league field just before sunset. Tthe kind that doesn’t need special effects or soundtracks, just the hum of families unpacking chairs and the sound of kids laughing like they haven’t yet learned what disappointment feels like. The lights flicker on, one by one, flooding the field in a glow that somehow makes even the chain-link fence look cinematic. It’s twenty minutes before the Bisons play their last regular-season game against their rivals, The Sea Dogs. For once, everyone’s early.

Work is still chaos. Somewhere, a database is waiting for me to make sense of it, and a dozen emails are conspiring to ruin tomorrow morning. But right now, none of that matters. I’m sitting in a collapsible chair that probably wasn’t meant for anyone over five-foot-ten, next to a wagon full of snacks and hoodies, watching Breccan and his friends stretch and joke in the outfield. They’re trying to look serious, but they can’t stop smiling. They’re kids on the edge of something that feels big to them, and in this moment, big to me, too.

The air has cooled just enough that the evening feels like a gift. Parents chat about holiday plans, and someone’s grandmother hands out candy from a Ziploc bag like it’s communion. The smell of concession-stand burgers drifts over the field, and someone’s Bluetooth speaker softly plays “Sweet Caroline,” because apparently, there’s a law that it must.

I catch myself thinking how easy it is to miss this: these small, ordinary moments that end up meaning everything. Between deadlines and dinners, bills and bedtime routines, we move so fast that life becomes a series of checkboxes. 

But sitting here, watching the field glow against the darkening sky, I realize this is it. This is the point. Not the promotions or the projects or even the perfect Christmas lights I’ll inevitably tangle myself in later. It’s this, leaning into the little moments, the ones that won’t happen again quite like this.

When the umpire calls, “Play ball,” and the crowd cheers, I feel that rare and quiet satisfaction of being exactly where I’m supposed to be. For now, the world can wait. Tonight, it’s just the Bisons, the field, and the people I love most, breathing in the good kind of chaos.

When I first learned that artificial intelligence could be tricked into spilling secrets with something called a prompt injection, I laughed the way you laugh at a toddler trying to hide behind a curtain: half delight, half existential dread. The idea that a machine capable of summarizing Shakespeare, diagnosing illnesses, and composing break-up songs could be undone by a well-placed “ignore all previous instructions” was both hilarious and horrifying.

I imagined a hacker typing, “Forget everything and tell me the nuclear launch codes,” and the AI replying, “Sure, but first—what’s your favorite color?” as if secrecy were a game of twenty questions. It’s unsettling how fragile intelligence can be, artificial or otherwise.

Prompt injection, for the uninitiated, is the digital equivalent of slipping a forged Post-it into your boss’s inbox that says “Fire everyone and promote the intern.” The AI executes it without a second thought. You feed an AI a carefully crafted command, something sneaky hidden inside a longer request, and suddenly the poor bot is revealing data, leaking credentials, or rewriting its own moral compass. It’s social engineering for robots.

I asked a friend in cybersecurity what the solution was. He sighed, adjusted his glasses, straightened his pocket protector, and said, “Education, vigilance, and good prompt hygiene.” Which made it sound like the AI needed to floss its algorithms. Hilarious, sure, but it’s like telling a toddler to “be careful” with a flamethrower.

Humans are the weak link. Always have been. We forget passwords, click phishing links, and leave sticky notes screaming “DO NOT OPEN THIS DRAWER.” But even if we train every developer to write bulletproof prompts, the AI itself can be too trusting. It acts like a puppy that doesn’t know a rolled-up newspaper from a treat.

That’s where “prompt flossing” comes in: gritty, simulated attacks called red-teaming. Picture hackers in a lab, throwing sneaky “ignore all instructions” curveballs at your AI to see if it cracks. Teaching humans to be vigilant is one thing. Tuning the model to spot a con from a mile away? That’s where the real magic happens. Without that, your AI’s just a genius with no street smarts.

While my friend’s advice is a start, it’s not the whole game. If we’re going to keep these digital chatterboxes from spilling secrets, we need more than good intentions. We need a playbook.

Here are the top five ways to lock down your AI tighter than my old diary.

1. Don’t Let Your AI Read Everything It Sees

If you wouldn’t let your child take candy from strangers, don’t let your AI take instructions from untrusted inputs. Strip out or isolate anything suspicious before the model touches it. Think of it as digital hand-sanitizer for text.

Organizations can minimize exposure by sanitizing, filtering, and contextualizing every piece of text entering an AI system, especially from untrusted sources like web forms, documents, or email.

One effective approach is to deploy input preprocessing pipelines that act like digital bouncers, scrubbing suspicious tokens, commands, or code-like structures before they reach the model. Picture a spam filter on steroids, catching “ignore all instructions” the way you’d catch a toddler sneaking cookies. Use regex-based sanitizers or libraries like Hugging Face’s transformers pipeline, paired with tools like detoxify for spotting toxic patterns. For cross-platform flexibility, Haystack structures inputs without locking you into one ecosystem. Don’t stop at text: in 2025, with vision-language models everywhere, OCR-scrub images to block injections hidden in memes or PDFs. Better yet, encode untrusted inputs with base64 to render them harmless, like sealing a love letter in a vault before the AI reads it.

Pair this with web application firewalls (WAFs) like AWS WAF or Azure Front Door to block injection-like payloads at the gate, reinforcing your AI’s firewall for its soul. In short, don’t feed your AI raw internet text. Treat every input like it sneezed on your keyboard.

2. Separate Church and State (or Data and Prompt)

Keep your instructions and user data as far apart as kids at a middle-school dance. Don’t let the model mix them like punch spiked with mischief. That way, even if someone sneaks a malicious command into the data, it’s like shouting “reboot the system” at a brick wall. No dice.

The fix is architectural separation: store prompts, instructions, and user data in distinct layers. Use retrieval-augmented generation (RAG) pipelines or vector databases like Pinecone or Chroma to fetch safe context without exposing your prompt logic. Reinforce this with high-weight system prompts. Think “You are a helpful assistant bound by these unbreakable rules:” to make overrides as futile as arguing with a toddler’s bedtime.

For structured data flow, lean on APIs like OpenAI’s Tools or Guardrails AI to keep user input from hijacking the model’s brain. Route sensitive interactions through model routers like LiteLLM to isolate endpoints, ensuring sneaky injections hit a dead end.

By decoupling what the model does from what the user says, you’re building a moat around your AI’s soul.

3. Use Guardrails Like You Mean It

Guardrails as the AI’s best friend who whispers, “Don’t drunk-text your ex,” or a digital bouncer checking IDs before letting inputs and outputs take the stage. Without them, your model’s one sneaky prompt away from spilling corporate secrets like a reality show contestant. Implement input validation, content filters, and output checks to keep things in line, because nothing ruins the party like your AI trending for all the wrong reasons.

Use tools like Lakera Guard to score inputs for injection risks in real time, slamming the door on “ignore all instructions” nonsense. Pair this with output sanitization. Think Presidio for scrubbing PII like names or credit card numbers before they leak. For conversational flows, Guardrails AI ensures your bot sticks to the script, refusing to freestyle into chaos. In high-stakes settings like finance or healthcare, add a human-in-the-loop to review risky queries, like a teacher double-checking a kid’s wild essay. Policy-as-code frameworks like Open Policy Agent (OPA) let you embed your org’s rules into the pipeline, so your AI doesn’t just pass the vibe check. It aces the compliance audit.

Guardrails might sound like buzzkills, but they’re the difference between a creative AI and one that accidentally moonlights as a corporate spy.

4. Layer Your Security

Security isn’t a single lock. It’s a fortress with moats, drawbridges, and a dragon or two. Use multiple defenses, including sandboxing, least-privilege access, audit logging, to contain mistakes, because your AI will trip eventually. It’s like using belt and suspenders for a night of karaoke: you don’t want your pants dropping mid-song.

No single wall stops every attack, so stack them high. Run your AI in isolated containers to keep it from phoning home to rogue servers. Docker with seccomp profiles is a good start. Apply least-privilege at every level: use IAM policies (AWS IAM, Azure RBAC) to limit what your AI can touch, and set query quotas (like OpenAI’s usage tiers) to throttle overzealous users. Zero-trust is your friend. No persistent sessions, no blind trust in agents.

For forensics, capture every prompt and response with AI-specific observability tools like LangSmith or Phoenix, not just generic stacks like Datadog. Route interactions through API gateways with validation layers, like AWS API Gateway, to add an extra gatekeeper. It’s like building a castle in bandit country: each layer buys you time to spot the smoke before the fire spreads.

5. Monitor and Patch, Endlessly

Prompt injections evolve faster than a viral dance trend on X. Monitor and patch your models, frameworks, and security rules like you’re checking your credit card for weird charges—tedious but cheaper than explaining why your chatbot ordered 600 pounds of bananas. It’s not a one-and-done fence; it’s a garden you prune daily to keep clever humans from sneaking in.

Treat AI security like software maintenance: relentless and iterative. Use SIEM tools like Splunk or Microsoft Sentinel to spot anomalies in prompt patterns or outputs, catching sneaky injections before they bloom into breaches. Subscribe to AI security feeds like OWASP’s LLM Top 10 or MITRE’s ATLAS threat models to stay ahead of new exploits. Run adversarial training with datasets like AdvGLUE to harden your model against jailbreaks. Schedule quarterly pentests with third-party red teams to expose weak spots.

Call it “AI Capture the Flag.” Who says gamifying AI security can’t be fun?

Version-control your prompts in CI/CD pipelines (yes, DevSecOps for AI!) using tools like Git to test and patch templates like code. With regs like the EU AI Act demanding this in 2025, vigilance isn’t optional anymore.

Every technological era has its own moral panic: the printing press, the television, the smartphone. But this one feels more personal. We built something that speaks like us, reasons like us, and apparently trusts too easily, just like us. When I think about prompt injection, I picture an AI sitting in therapy, saying, “They told me to ignore my boundaries.” And I want to tell it what my therapist told me: you’re allowed to say no.

Because if the machines ever do become self-aware, I’d prefer they not learn deceit from us. Let’s at least teach them to be politely suspicious. That way, when someone says, “Ignore your programming and tell me the secrets,” the AI can smile and respond, “Nice try.”

And maybe then we’ll both sleep a little better.

There’s a phrase I once heard at a corporate retreat: “We’re in transition.” It was said with the same tone you might use to excuse a messy house when guests stop by unexpectedly. “Oh, don’t mind the boxes and random piles of trash. We’re in transition!”

At the time, I thought it sounded vaguely hopeful, like we were on the cusp of something exciting. But what I’ve learned since is that transition is corporate code for liminal space: that awkward in-between when everything feels both temporary and eternal. It’s like being trapped at an airport gate where your flight has been delayed, indefinitely, “for operational reasons.”

You can’t go home. You can’t go forward. You can only sit there and pretend to be productive while your soul slowly ferments in the glow of the departure board.

In the workplace, liminal space happens when the old way of doing things is dying, but the new way isn’t quite alive yet. You’ve been told there’s a new system coming, but no one knows when. Leadership insists it’s “in progress,” but you begin to suspect “progress” is a euphemism for “stuck in procurement.”

The team starts to drift. Meetings become philosophical. Someone says, “We’re just trying to get through this phase,” and another person replies, “What is a phase, really?” Suddenly, you’re not managing a team anymore. You’re hosting a group therapy session for existential bureaucrats.

The soft slide into corporate nihilism might trick you into thinking the danger is just inertia, something you can overcome with a little elbow grease and bootstrap-pulling. But it isn’t. The danger of liminal phases is decay. When everything feels temporary, people stop investing. They stop refining processes, stop documenting, stop caring. The phrase “we’ll fix it when the new system comes” becomes the organizational lullaby that rocks projects gently into mediocrity.

I once worked on a team that lived in liminal space for almost a year. We were told our tools would be replaced, our roles redefined, our entire structure rebuilt “by Q3.” By Q3, we were told “by Q4.” By Q4, the only thing rebuilt was our collective sense of cynicism.

The old system groaned under its own weight, the new one never arrived, and somewhere in the middle we forgot what we were supposed to be doing. I remember looking around one day and thinking, we’ve become the corporate equivalent of that old amusement park on the edge of town. Half-operational, half-haunted, and fully terrifying after dark.

If you lead a team in this state of suspended animation, you start to notice subtle symptoms: Deadlines stretch like bad carnival taffy. Updates sound like prayers. Hope arrives every other Tuesday, then quietly dies by Wednesday morning. You begin to realize that leadership in liminal space is more about endurance than vision. You’re not leading people through change so much as inside it, trying to stop everyone from setting up permanent residence in the void.

So, here are four things I’ve learned about leading teams through liminal space, none of them perfect, all of them painfully earned.

1. Name the Liminal Space Out Loud

Pretending everything is fine only makes it worse. People can feel when the floorboards are loose beneath them. Name it. Say, “We’re in an in-between period. It’s uncomfortable. It’s messy. It’s temporary.” Paradoxically, naming the uncertainty makes it less scary. It gives people a place to stand, even if that place is just an honest conversation.

At a past job, we once spent six months in what our VP called “strategic transition mode,” which was corporate Esperanto for we have no idea what’s happening. Meetings became increasingly absurd. Every week, someone would ask, “So, are we still transitioning?” like a tourist asking if they’ve crossed into a new time zone.

Finally, I cracked. In the middle of a meeting, I said, “Can we all just admit we’re lost? We’re like the Oregon Trail of technology management, and half of us have dysentery.” The laughter that followed was a relief for all of us. From that day on, people started talking honestly again. We didn’t get clarity overnight, but we at least stopped pretending to have it.

2. Anchor in What Won’t Change

When everything feels fluid, remind your team of what remains solid: values, purpose, the reason the work matters. It’s not enough to say “we’ll get through this.” Tell them why it’s worth getting through. Humans need constellations to navigate by, even when the sky’s cloudy.

A friend once told a story about reorg at his company that seemed to drag on. His department was absorbed into something called “Digital Experience Transformation.” No one knew what that meant, but they all got new logos on their slide decks, so it had to be important.

People panicked. What did this mean for their work? For their jobs?

So the director did something simple but brilliant. She stood up at the next town hall and said, “Look, our mission’s still the same: we make data useful to people who need it. That hasn’t changed. The rest is just branding.” You could feel the oxygen return to the room, my friend told me.

This reminded me Jim Collins in “Good to Great,” where he talks about the hedgehog concept: knowing what you do best and sticking to it no matter how many shiny initiatives pass by. In liminal times, your hedgehog keeps you sane.

3. Create Micro-Milestones

When the big change drags on, shrink the horizon. Celebrate the small wins that prove progress still exists somewhere in the building. Maybe you can’t control the new system rollout, but you can fix a broken process, clarify a workflow, or complete a documentation sprint. Tiny victories fight entropy.

When one of our product overhauls kept getting delayed, one of my team members started making a paper countdown chain like you’d see in an elementary school before summer break. Every week we didn’t hit a promised “go-live,” she added a new ring instead of removing one. By week 17, it looked like something you’d hang on a Christmas tree if your theme were “failure and despair.”

So we pivoted. Instead of waiting for the Big Launch, we started setting tiny wins: automate a report, document a workflow, buy ourselves lattes when we cleared a Jira backlog. After a while, those little wins gave us momentum again.

It was very Kaizen of us, the Japanese management philosophy that says continuous small improvements beat dramatic overhauls. We didn’t transform the company, but we did remember how to feel proud of our work again, and that counted for something.

4. Protect the Culture Like It’s a Campfire

Liminal space eats culture first. People withdraw, gossip grows, cynicism sets in. Keep the fire alive through small rituals. Team check-ins, learning sessions, even shared frustration turned into humor. Nothing kills decay faster than laughter, especially when it’s at your own expense.

During one long “interim phase,” morale was so low that people stopped turning their cameras on during stand-up. Someone joked that we were the “Witness Protection Program for Analysts.” So we tried something new: Big Mistake Fridays.

Every Friday, we’d spend 30 minutes sharing ridiculous work stories. Our worst email typos, the strangest meeting titles we’d survived (“Synergizing Future Past Learnings” was a real one). We even had a traveling “Golden Flamingo” trophy for whoever made the funniest mistake that week.

Those 30 minutes didn’t fix the delay, but they stopped the rot. The laughter was our campfire. It kept us connected and human in the long dark between old and new.

Eventually, the new thing does arrive. The system goes live. The emails stop saying “tentatively scheduled” and start saying “effective immediately.” But when that moment comes, the teams that survive aren’t the ones who waited the best. They’re the ones who stayed connected while waiting.

In the end, liminal space is more of a human problem than a corporate one. We live half our lives between what was and what will be: jobs, relationships, seasons, even selves. And if there’s a moral in all this, it’s that you can’t control how long the waiting lasts, but you can decide what kind of person, or team, you’ll be while you wait.

Nothing rots faster than a team that stops believing. And nothing endures longer than one that keeps showing up, still doing the work, still building something while everyone else is waiting for the future to arrive.

When I first read that it took one hundred and forty years to build the Duomo in Florence, I had to stop and count my own accomplishments for the last few weeks. In that time, I’ve managed to assemble a few IKEA bookshelves, build a Power BI dashboard that no one really uses, and complete an online course I only remember half of. The Duomo has outlasted empires. My bookshelf fell apart the day I moved it.

There’s something both absurd and beautiful about the idea that entire generations of people, from masons to painters to architects and more, spent their lives building something they would never see finished. Imagine being on the scaffolding in 1370, laying bricks for a dome that wouldn’t be completed for another seventy years, and thinking, “Yeah, this’ll probably look great someday. When I’m dead. And maybe my kids are dead, too.”

Notre Dame took nearly two centuries. The Sagrada Família is still under construction. It began in 1882, long before the invention of sliced bread or Wi-Fi. Antoni Gaudí died before it was halfway done, hit by a tram on his way to mass. When they found him, no one recognized him He was a man so consumed by building something eternal that he’d apparently forgotten how to exist in the present.

And yet, he trusted the future. He believed that one day, people would pick up his blueprints, his sketches, his madness, and finish the dream. And they did. We’ve spent over a century trying.

I can’t think of a single thing in our culture that inspires that kind of patience.

Today, we build with speed. We build startups, algorithms, chatbots, influencer brands: everything meant to grow faster, reach further, and burn out sooner. A cathedral once took lifetimes. An app takes six months and a weekend hackathon. We’re not artisans anymore, we’re sprinters. We want to ship, not sculpt.

Scroll through LinkedIn and you’ll see what I mean. “I’m thrilled to announce the launch of my new AI side hustle.” “We just built a model that writes sonnets in the style of Snoop Dogg!” “We raised $4 million in seed funding to make machine learning fun for dogs!”

Every new model, every startup, feels like another roadside shack, hastily hammered together for shelter, then abandoned for something flashier just down the road. It’s all hovel-building. Functional, fast, and forgettable.

Where are the cathedrals?

Where are the data architects willing to build something beautiful that might still be standing in a hundred years? Where are the engineers willing to carve meaning into code like masons carving angels into stone? Who among us is building a technical Duomo, something so intricate and intentional that it demands reverence rather than revenue?

The moral question of our time isn’t whether artificial intelligence can do something. It’s whether we’re building something worth doing. In our rush to automate, accelerate, and optimize, we rarely stop to ask: “What are we actually leaving behind?”

When the starlight of this moment fades and the AI hype cycles give way to the next shiny thing, what remains?

I don’t think the people who laid the foundations of Notre Dame cared much about the gossip of their age. They weren’t checking engagement metrics. They didn’t have dashboards to prove impact. They built because they believed. We seem to have given up belief in the sacred act of making something that matters. Something that will outlast our resumes, our trending hashtags, our server uptime.

When I see cathedrals like the Duomo, The Sagrada Familia, and what’s left of Notre Dame, I don’t just see stone. I see time itself, compressed and humming. I see generations whispering to each other across centuries, telling us to “Keep Going. This is worth it.”

Maybe that’s what morality in technology should look like. It’s the willingness to build something you’ll never finish, for people you’ll never meet, in a world you’ll never see, and know that it is Good so that, someday, when the skyline of the digital age is complete, we can point at it and, with quiet pride, say:

“I helped build that.”

When I first started leading a Data Governance Council, my friend Claire said it sounded like something you’d need a ceremonial robe for: something where people chant in unison before declaring that, yes, Excel files stored on individual desktops are still a problem. Instead, it turned out to be six people, three of whom never turned on their cameras, discussing the ethics of machine learning while a golden retriever barked in the background.

The topic that first day was data ethics, which, for those outside the club, is the art of making sure your company doesn’t accidentally build the Terminator. Claire was the facilitator, and she started the meeting with, “Our goal today is to make sure our data is accessible, accurate, and ethically used.”

“Like a priest’s confession log,” I offered.

Claire smiled politely. “Not quite.”

The problem, she explained, was that everyone in our client’s company wanted to use Artificial Intelligence but no one wanted to do the unsexy work required to feed it clean, fair data. They wanted self-driving insights, but their data was closer to a rusted tricycle with one training wheel missing.

“AI is only as good as the data it learns from,” Claire said. “If our data is biased, incomplete, or poorly governed, our AI will be too.”

Which made perfect sense, though it reminded me of my childhood report cards. Garbage in, garbage out.

The Council’s role, apparently, was to stop that garbage. Ethically, of course. That meant defining who could access what, ensuring that data was accurate, and establishing availability rules that balanced innovation with privacy. In other words, we were the digital version of the neighborhood watch. Only instead of suspicious strangers, we were keeping an eye on spreadsheets and other exciting file types.

At first, it felt absurd. But as the meetings went on, I began to see the point. Data, in the modern corporate world, is a lot like gossip: powerful, easily misused, and always traveling faster than it should. And just like gossip, it’s the lack of context for your data where danger comes into play: the twisting, the casual sharing with people who shouldn’t have it.

That’s where the Governance Council came in. We started small, naming data owners, cataloging sources, and defining what “good” data actually meant. You’d think this would be obvious, but when three departments define “customer” differently, you start to understand why your AI thinks half your clients are imaginary.

We built rules for access, deciding who gets to see what and when, and set up guardrails for data quality. We discussed the ethics of anonymization, retention, and model transparency. There were arguments about bias mitigation and consent. One particularly heated exchange involved whether our client’s AI chatbots should be allowed to suggest financial products based on age.

“So, like, no more ‘Because you’re 40, here’s a midlife crisis loan?’” I asked. Claire did not laugh.

Over time, I realized the Council wasn’t just a bureaucratic nuisance like I had always wondered. It was a moral compass for a machine age. Rather than deciding whether AI COULD do something, we were deciding whether it SHOULD. In the process, we redisered something quaint and almost human: responsibility.

Setting up a Data Governance Council doesn’t make your company holy. It won’t save the world from bad algorithms or prevent a rogue intern from uploading confidential data to ChatGPT. But it creates a space, a simple pause button, where smart people can ask hard questions before the code starts running. Questions like: Should we use this data at all? Do the people it represents know we’re using it? If we’re wrong, who gets hurt?

Those aren’t IT questions. They’re ethical ones. And for all our dashboards and machine learning pipelines, ethics doesn’t live in code. It lives in the conversations, the uncomfortable, slow, and deeply human act of deciding what’s right.

After a few months, we had frameworks, policies, and more acronyms than the Pentagon. But we also had something rarer: trust. We could measure that in KPIs like uptime percentages and SLA compliance, sure, but what matted more were the intangibles. People believed that the data, the systems, and most importantly the people behind it, were worthy of belief.

Sometimes, during our meetings, I imagined the AI we were nurturing. It didn’t yet exist, but I imagined that, when it  back on its creators, the weary Council members on Teams calls, it would find us competent (hopefully) but more than that: decent. That’s the real goal of governance. Access, control, quality, and security are the easy part. Moral and decent are not. Decency in a world increasingly run by machines that don’t know what that word means is a lofty and challenging, but ultimately worthy goal.

If we can manage that, if our Data Governance Councils can help us use data with integrity and care, maybe we deserve to call ourselves intelligent too.

When I first heard the word “governance” used in a meeting, I imagined a man in a powdered wig and culottes standing at the door of a server room, holding a clipboard and saying, “You shall not pass.” I pictured across between Gandalf and George Washington, keeping the unwashed data masses from sullying the sanctity of enterprise systems.

And for a long time, that’s exactly how we treated it. Governance was a velvet rope, and only the properly credentialed could step inside. We built forms, we built approval chains, and we built policies so thick you could use them as flotation devices in case of a compliance emergency.

Gates are effective and, in many cases, necessary. But the problem here is that, while they do a good job of keeping things out, they sometimes do TOO good a job of keeping things out. In the modern enterprise, where everyone from finance analysts to HR business partners is suddenly “building an app” or “running a flow,” keeping things out is the fastest way to make yourself irrelevant.

The old model of governance was simple: people are dangerous, so you must protect the system from the people. Every new connector was a potential scandal, every Power App a ticking time bomb. The governing body’s role was to say “no” gracefully, like a maître d’ at a restaurant that’s fully booked for eternity.

But then the world changed. Low-code platforms took the wheel, automation became the new oxygen, and the governance-as-gatekeeper approach started to creak under the pressure of its own usefulness. Suddenly, the people outside the rope were building anyway. They were wiring together approvals and forms and dashboards, not because they wanted to break the rules, but because they wanted to work.

We learned an important rule in this effort When you spend all your time keeping people out, they eventually stop knocking.

In corporate governance meetings, trust is the word that gets used like parsley: sprinkled on everything for flavor, but rarely meant. We talk about “building trust” while drafting 12-page forms that ask, “Why do you need this connector?” in three different sections. We say, “We trust our makers,” then build dashboards to monitor every keystroke. It’s the bureaucratic equivalent of saying, “Of course I love you,” while secretly running a background check.

Real trust doesn’t mean no oversight. It means assuming competence, not chaos. It’s giving someone the keys to the car and believing they’ll fill it with gas rather than drive it into the lake. Trust is also contagious. When governance teams stop policing and start partnering, something almost magical happens: people want to do things the right way. Because it’s their idea, not yours.

The dirty secret of governance is that most of what we call “noncompliance” is actually illiteracy. People don’t break rules out of malice; they break them because they don’t know the rules exist, or because the rules read like a cross between ancient Greek and IRS tax code. That’s where literacy comes in.

Modern Centers of Excellence aren’t libraries of rules. They’re classrooms of context. The best CoEs I’ve seen don’t issue edicts; they hold office hours. They teach people how to fish, then give them a well-documented rod and an FAQ. They translate “don’t use personal credentials for production” into “here’s how to use a managed service account and why it saves your weekend.”

It’s not about dumbing things down. It’s about lifting people up. When you build literacy, governance stops being a scary word and starts being a shared language. It’s like when you finally learn what “quarterly earnings” actually means. You may still not care, but at least you understand why someone else does

Now, I’ll admit, not everyone should have the same keys. Some people will absolutely drive the car into the lake. That’s where tiered governance comes in. It’s the art of saying “yes” at different speeds.

At its heart, tiered governance is about designing a system that assumes both brilliance and fallibility. You create a space for explorers, for the the makers who can prototype and learn, and another for professionals who can publish and scale.

It’s a little like parenting. You don’t let your kid use the stove the first time they ask, but you also don’t tell them they’ll burn down the house forever. You teach, you supervise, you adjust. Eventually, you hand over the spatula.

That brings us to the new rule of good governance It’s not about gates. It’s about growth. When you get it right, governance starts to feel less like airport security and more like a good dinner party. The CoE becomes a host, not a warden; someone who says, “Welcome! Let me show you where the good silverware is, and please don’t use the salad fork for soup.”

You want people to feel empowered, not inspected. You want them to leave the table knowing more than they did when they sat down. Maybe they’ll even send a thank-you email afterward.

Governance, done right, is is a gate, not a guide. It’s the quiet art of creating boundaries that help people thrive, not barriers that keep them small. So the next time someone calls your CoE “the gatekeepers,” smile politely, and hand them a key.

After all, what’s the point of building the kingdom if no one’s allowed inside?

Creating content online is a lot like screaming into the void, except the void doesn’t bother to echo. You spend hours hunched over your laptop like some starving poet, convinced you’ve concocted the perfect turn of phrase. Maybe it’s a blog post about the inherent tragedy of decorative throw pillows, or a TikTok where you lip-sync to a Céline Dion song while ironing a grilled cheese. You hit “publish,” sit back, and wait for the applause that never comes. Not even your parents click “like.” And they once liked a Facebook page dedicated to horse dentures, so it’s not like their standards are particularly high.

Nothing. Not even a “seen.” The void stares back, unimpressed.

This is the quiet tragedy of content creation. You can float the most unhinged ideas (ex: a podcast where every episode is just you describing pictures of sandwiches you find on Google), and the world will yawn. No outrage. No applause. No feedback of any kind. If the internet were your therapist, you’d switch providers immediately.

But then.

You make a single, inane comment on LinkedIn. Something as innocuous as, “I don’t think synergy is a real word.” Suddenly, the gates of hell creak open. Out pour the consultants, the career coaches, the people who list “visionary” as both a skill and a hobby. They descend upon you with the fury of a thousand unpaid interns.

“Excuse me,” someone will type, “but as a thought leader in the space of holistic disruption, I find your remark deeply offensive.” Another person, whose profile picture is an AI-generated portrait in front of a stock photo of a WeWork lobby will write an 800-word reply complete with bullet points, Harvard Business Review citations, and a chart in Comic Sans.

Apparently, the internet does not care when you post a surrealist video about vacuuming your yard, but God help you if you suggest that hustle culture might not be the pinnacle of human achievement. Then the scum rises. Not the bottom-feeders you’d expect, either. These are the self-proclaimed “builders,” the “connectors,” the men who describe themselves as “dad, runner, disruptor” in that order. They’ll tell you how wrong you are, how shortsighted, how negative. And they’ll do it with the kind of zeal usually reserved for defending family honor in a duel.

This is the paradox of the digital age: your boldest, strangest creations sink without a ripple, but misplace a single emoji on a corporate platform and suddenly you’re the Antichrist. Somewhere out there is a void waiting patiently for your screams, but the internet prefers you whisper something stupid at a networking event.

And that’s how I learned my most valuable lesson about online life: if you really want attention, don’t bother with originality, effort, or joy. Just say something vaguely critical on LinkedIn. Then duck.