Let’s be brutally honest for a moment. For the past few years, the C-suite has been utterly mesmerised by the glittering promise of AI. They see streamlined operations, uncanny predictions, and, of course, ballooning profits. What they conveniently ignore is the plumbing. The messy, complex, and frankly boring work of governance. This willful blindness has created vast AI governance gaps, and as we hurtle towards 2026, these gaps are no longer just theoretical risks. They are gaping holes in the hull of the enterprise ship, threatening to sink an organisation’s entire cybersecurity strategy.
The conversation has moved on from “if” to “how,” but we’re asking the wrong “how.” We’re not asking, “How do we govern this beast?” We’re just asking, “How quickly can we plug it in?” This is not just a technological oversight; it’s a catastrophic failure of enterprise risk management. As an insightful piece from bobsguide.com recently highlighted, a perfect storm is brewing. It’s a toxic cocktail of algorithmic opacity, evaporating government support, and creaking old tech. The time for wishful thinking is over. It’s time for a reality check.

What Are We Even Talking About with ‘AI Governance Gaps’?

So, what exactly are these AI governance gaps? Put simply, they are the chasms between what an AI system is programmed to do and what an organisation actually understands, controls, and is accountable for. It’s the digital equivalent of giving a teenager the keys to a supercar with no driving lessons, no insurance, and no idea how the engine works. What could possibly go wrong?
These gaps aren’t just about a model producing a biased outcome, although that’s certainly part of it. It’s about a fundamental lack of a framework for accountability. When an AI trading algorithm goes rogue, who’s at fault? The developer who wrote the code? The manager who deployed it without sufficient testing? The data scientist who fed it biased historical data? Without clear governance, the answer is a corporate shrug, followed swiftly by a regulatory fine and a public relations nightmare.
The urgency is growing because AI is no longer a sandboxed experiment. It’s being deeply embedded into core business functions, from credit scoring to medical diagnostics. As T Rabi Sankar, the Deputy Governor of the Reserve Bank of India, rightly warned, “unattended AI could pose unprecedented threats”. This isn’t scaremongering; it’s a statement of fact from someone who understands systemic risk. Ignoring the governance aspect is like building a skyscraper without bothering with the blueprints for the foundation. The cracks might not show on day one, but an earthquake is coming.

The Fading Shadow of Regulation

You would think that with threats mounting, governments would be stepping up to provide clear cybersecurity regulations and a framework for co-operation. You would be wrong. In a move that beggars belief, the U.S. Cybersecurity Information Sharing Act (CISA), a key piece of legislation that facilitated intelligence sharing between the government and the private sector, has been allowed to expire. This isn’t just a minor policy hiccup; it’s a self-inflicted wound at the worst possible time.
The analysis in the bobsguide article is stark, projecting a potential 80% drop in threat intelligence sharing as a direct result. Think about that. Just as our adversaries are arming themselves with AI-powered attack tools, we’ve decided to dismantle one of our most effective early-warning systems. This creates a dangerous intelligence vacuum. Companies are now, more than ever, on their own.
This legislative backsliding puts the onus squarely on industries to self-organise. It also makes the role of global regulators, like the U.S. Office of the Comptroller of the Currency (OCC) and their international counterparts, even more critical. They are signalling loud and clear that they expect financial institutions to have their houses in order. India’s Finance Minister, Nirmala Sitharaman, has been vocal about the need for robust frameworks around AI ethics and algorithmic bias. The message is unmistakable: “Regulate yourselves, or we will do it for you, and you won’t like it.” Proactive governance is no longer a ‘nice-to-have’; it’s a survival imperative.

Your Creaking IT Is Your Biggest AI Risk

Let’s talk about the elephant in the room: your ancient IT infrastructure. Many large enterprises, particularly in finance, are running their shiny new AI models on systems that belong in a museum. These legacy IT challenges represent a ticking time bomb at the heart of modern business. Deploying advanced AI on top of a brittle, outdated core system is like trying to run a 4K movie on a black-and-white television from the 1960s. The problem isn’t the movie; it’s the hardware’s inability to handle it.
We don’t need to look far for a cautionary tale. Consider the recent operational failure at Metro Bank, which resulted in customers facing unauthorised overdrafts and duplicate card payments. This wasn’t a sophisticated nation-state attack. It was a failure of the basic plumbing. The bank’s systems simply couldn’t cope. Now, imagine a similar failure triggered not by a simple glitch, but by an unpredictable AI model interacting with that same fragile infrastructure. The potential for systemic chaos is immense.
Modernising legacy IT isn’t a glamourous project. It doesn’t get the same boardroom applause as a new AI initiative. But it is fundamentally more important. You cannot build a secure, resilient, and governable AI strategy on a foundation of decaying code and unsupported hardware. Addressing the legacy IT challenges is the first, non-negotiable step in closing the AI governance gaps. Ignoring it is an act of corporate negligence.

A Three-Point Plan for Not Imploding

So, what’s to be done? Wringing our hands won’t help. The situation requires a clear-eyed, strategic response. Here are three actionable priorities for any enterprise leader who wants their organisation to still be standing in 2026.

1. Demand Algorithmic Accountability

It’s time to pop the bonnet on your AI/ML models. “It’s a black box” is no longer an acceptable answer. Regulators and customers alike are demanding transparency. You must be able to explain why your model made a particular decision. This means conducting rigorous audits, stress-testing for bias, and implementing what T Rabi Sankar calls “safety by design” from the very beginning.
* Ask the hard questions: Why was this loan application denied? What factors led to this investment recommendation?
* Invest in explainability: Put resources into tools and talent that can interpret and explain model behaviour in plain English.
* Establish a clear line of ownership: Someone, a real human being, must be accountable for the model’s performance and its outcomes.

2. Don’t Go It Alone: Embrace Collaboration

With government-led intelligence sharing on the wane, the private sector must fill the void. This is where bodies like Information Sharing and Analysis Centres (ISACs) become indispensable. These industry-specific groups provide a trusted forum for sharing threat intelligence and best practices, away from the prying eyes of competitors.
Cybersecurity is not a competitive sport. A breach at one bank weakens the entire financial ecosystem. Sharing information about new attack vectors, vulnerabilities, and defensive tactics is an act of collective self-preservation. In this new landscape, your strongest ally might just be the company you compete with every day.

3. Make Operational Resilience Your Religion

Fix your plumbing. It’s as simple and as difficult as that. The legacy IT challenges cannot be kicked down the road any longer. Prioritising operational resilience means treating your core systems with the respect they deserve.
– Stress-test relentlessly: Don’t wait for a crisis. Proactively simulate failures to understand your system’s breaking points. What happens if a key database goes offline? How does the system react to an unexpected surge in AI-driven queries?
– Plan for failure: Assume things will break. Have robust, tested recovery plans in place. Resilience isn’t about preventing every failure; it’s about surviving them.
– Modernise strategically: A “rip and replace” approach is often unrealistic. Instead, focus on a phased modernisation plan, decoupling critical functions from monolithic legacy systems and rebuilding them on modern, resilient platforms.

The Governance Imperative

We are at a critical inflexion point. The allure of AI is powerful, but it has blinded many leaders to the foundational work required to deploy it safely and effectively. The widening AI governance gaps, compounded by faltering cybersecurity regulations and persistent legacy IT challenges, have created a landscape fraught with peril.
Financial institutions and other major enterprises face a clear choice: either get serious about building robust governance frameworks now, or wait for a catastrophic failure to force their hand. The work is hard, and it isn’t flashy, but it is essential. The question you should be asking in your next board meeting isn’t “What new AI can we buy?” but “How are we governing the AI we already have?”
What’s your biggest concern when it comes to AI governance in your own industry? Are you seeing these gaps appear, and what steps are being taken to address them? The conversation is just beginning.