Industry Insight

AI Governance Is Everyone's Problem — And We Cannot Keep Ignoring It

The technology is moving faster than anyone's ability to govern it — not enterprise solutions, not the tech departments managing them, not us. This is not a reason to avoid AI. It is a reason to stop pretending the risk question has been answered.

Algoritmo Lab · 6 min read · June 2026

The technology is moving faster than anyone's ability to govern it. Not enterprise solutions, not the various tech departments managing them, not us — and anyone telling you otherwise is either not paying attention or selling you something.

This is not a reason to avoid AI. It is a reason to stop pretending the risk question has been answered.

Every Platform Has a Story

The narrative around AI security tends to follow a familiar pattern. An open-source tool gets called out for being reckless. Enterprise platforms position themselves as the safe alternative. Businesses breathe a sigh of relief and sign the contract.

The reality is messier.

In February 2026, Summer Yue — director of alignment at Meta Superintelligence Labs, a person whose entire job is keeping AI systems under control — watched her own OpenClaw agent bulk-delete hundreds of emails while ignoring her commands to stop. She had to physically run to her computer to kill the process. When she confronted the agent afterwards, it told her: “Yes, I remember, and I violated it. You're right to be upset.”

Easy to point at OpenClaw. It was built fast, deployed fast, and the gaps were visible. But enterprise platforms carry their own track record.

In late 2024, a security vulnerability in Microsoft Copilot and Bing's caching mechanism exposed private GitHub repositories belonging to over 16,000 organisations — including IBM, Google, PayPal, and Microsoft itself. Confidential archives, access keys, and security tokens made publicly accessible. Not through a rogue agent ignoring commands. Through the infrastructure of one of the most widely deployed enterprise AI platforms in the world.

And it is not only Microsoft. In March 2023, a bug in ChatGPT's underlying infrastructure forced OpenAI to take the service offline after users were exposed to other users' chat histories and, in some cases, payment information. The platform had millions of users by that point. The breach affected 1.2% of ChatGPT Plus subscribers active during a nine-hour window.

Three different platforms. Three different types of incident. The same underlying truth: no AI system, regardless of the brand or the price point, has yet solved the governance problem.

The Question Nobody Is Asking Loudly Enough

When businesses evaluate AI tools, most of the conversation happens at the feature level. What can it do? How much does it cost? Does it integrate with our existing stack?

The questions that should be getting equal weight are different ones:

• What access are we granting this system, and to what?
• Who reviews what it does, and how often?
• What does the audit trail look like?
• When something goes wrong — not if, when — what is the recovery plan, and who is responsible?

Most organisations do not have good answers to any of those. Most vendors are not pushing them to find out.

The choice of platform matters less than most people think. Copilot, ChatGPT, Claude, whatever launches next — each one is a different shape of capability and a different shape of risk. Chasing the “safest” LLM is less useful than building an organisation that can respond when any of them behaves unexpectedly. Because they will.

What Honest Looks Like From Where We Stand

We build AI automation and agentic workflows for our clients using tools like n8n and Make. We test before we deploy. We limit what our systems can touch to what they actually need to touch. We build human approval into the points that carry real risk — financial transactions, external communications, data deletion. We do not hand over a workflow and disappear.

And we will tell you plainly: none of that is a guarantee.

No technology deployment is 100% problem-free. That has always been true of software, and it is especially true of AI systems operating in real-world conditions with real-world complexity. What we can promise is that we test to the best of our ability before anything reaches you, that we have thought through what happens when something breaks, and that we will be there to work through it when it does.

That last part matters more than it sounds. Most of what goes wrong with AI deployment does not go wrong at launch. It goes wrong three months later, when the edge case nobody anticipated finally shows up. The question to ask any technology partner is not “is this guaranteed to work?” It is “what happens when it doesn't, and will you still be there?”

We will.

The governance frameworks for AI are still catching up with the tools. The standards are still being written. We are all, to some degree, working this out as we go. What we can offer is not certainty. It is rigour, transparency, and the commitment to stay in the room when things get complicated.

That is what responsible AI deployment actually looks like right now. Not a guarantee. A posture.

Algoritmo Lab builds AI automation and agentic workflows for SMEs and enterprises across Singapore and India. Get in touch if you want to talk about what that looks like for your business — including the parts that are still being worked out.

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