The cloud resilience deficit: how AI exposes the limits of digital governance
In 2025, three hyperscalers suffered global outages that disrupted millions of users and costed billions. AI is amplifying both the potential - and the fragility - of the cloud.
1. The year the cloud cracked
2025 will be remembered as the year the cloud blinked.
Between June and October, AWS, Azure, and Google Cloud each suffered global outages - incidents that rippled across hospitals, airports, banks, and public systems.
Global impact:
- 150M+ people affected
- Estimated losses exceeding $1 billion
- Thousands of organisations disrupted
Each failure had a different trigger - a DNS fault, a configuration error, an API misfire - but together they revealed a single truth: the backbone of the digital economy is showing strain.
These weren’t isolated glitches. They were signals of systemic dependency in an infrastructure that has grown faster than its governance.
2. The dependency problem
Nearly 60% of global workloads now run on the same three cloud providers. For Europe, that concentration is even starker: roughly 75% of its cloud capacity and 70% of its workloads sit on AWS, Google, or Azure.
Regional dependency:
- Northern Virginia: global control hub.
- Western Europe: EMEA gateway.
- Asia-Pacific: regional backbone.
A single regional failure can cascade globally.
Even systems marketed as “EU-hosted” often rely on U.S.-based control regions, meaning the operational and legal authority behind them remains foreign. Collectively, EU and sovereign cloud providers hold under 10% of the market.
Dependency itself isn’t new, but its scale and invisibility are. The deeper Europe’s industries integrate AI workloads into hyperscaler infrastructure, the more fragile the system becomes.
3. The governance gap
Cloud adoption was supposed to simplify resilience. Instead, it has made it abstract.
The data behind the pressure:
- +21% cloud spend YoY, reaching $723B (Brightlio)
- +18% increase in major outages vs. 2024 (Parametrix)
- 40% of failures caused by human or configuration error (Uptime)
AI has amplified the strain. Every new model, workload, or training cycle consumes compute and storage at exponential rates — and multiplies the operational risk surface.
Yet governance hasn’t kept pace. Most organisations treat resilience as a provider guarantee, not a leadership responsibility.
When cloud infrastructure fails, it doesn’t just break systems. It breaks assumptions - about continuity, control, and accountability.
4. When systems fail, governance is tested
When the cloud goes down, the damage hits financial and strategic lines:
✅ Continuity: Transactions, logistics, and AI-driven operations grind to a halt.
✅ Reputation: Failures are read as governance gaps, not IT mishaps.
✅ Financial: Providers cap their liability; uninsured losses fall on customers.
✅ Regulatory: Under DORA and the EU AI Act, Boards are accountable for operational resilience.
✅ Strategic: Dependence on foreign infrastructure equals exposure.
In an economy where AI runs inside cloud systems, every interruption becomes a governance stress test.
5. Building resilience: from infrastructure to intent
There is no silver bullet for cloud dependency. But there are disciplines that define whether an organisation can absorb the shock - or amplify it.
Four disciplines for resilient leadership:
1️⃣ Design for continuity - Distribute workloads across providers and regions; keep critical data in hybrid or on-prem systems.
2️⃣ Govern configuration risk - Treat every major cloud change as a board-level decision; enforce approval, testing, and rollback protocols.
3️⃣ Map dependencies - Know where workloads run, who owns recovery, and how resilience metrics feed into Board reporting.
4️⃣ Build organisational readiness - Test outages, clarify decision ownership, and align communications.
Resilience, in the end, is as much culture as it is architecture.
Resilience it’s not built by redundancy alone, but by governance clarity - by leaders who understand that digital dependency is also business dependency.
6. Signal to action
AI is pushing cloud ecosystems to their limits, exposing how governance lags behind adoption. Boards and executives need visibility, accountability, and readiness - not after an incident, but before the next one.