Every organisation adopting AI in 2026 faces a simple question:
Do you have the governance to match the capability – or are you just adding another attack surface?
From national grids to retail, the pattern is the same: AI is being rolled out faster than cyber, risk and people can keep up.
The AI rush: capability is outpacing control
Over the last two years, AI has moved from pilot projects to being embedded in core workflows – from content generation and customer service to fraud detection and OT monitoring. Boards hear the ROI story, but often underestimate the new classes of risk: model abuse, data poisoning, prompt injection and quiet dependency on third‑party models.
Global guidance from security agencies now explicitly warns that deploying AI systems securely requires treating them as high‑value assets, with hardened infrastructure, network controls and continuous testing – not just “another app in the cloud”.
Where AI governance is failing (and how to fix it)
The governance gap usually shows up as:
- No clear owner. AI sits between IT, security, data and business – which often means nobody truly owns end‑to‑end risk.
- Weak mapping to existing controls. Organisations deploy AI tools without aligning them to existing ISO 27001, NIST or NIS2 style governance, so they can’t explain how AI fits into their risk register or control framework.
- Over‑indexing on compliance checklists. Many boards treat AI as a policy exercise, but guidance now emphasises secure deployment architecture, Zero Trust, hardened configs and continuous model validation.
What works better in practice:
- Treat AI like critical infrastructure. Secure deployment environments, segmentation, strong identity and robust logging are now being recommended as baseline for AI systems, especially in regulated or high‑risk environments.
- Make cyber and AI a board‑level sport. Leading CISOs now frame AI and cyber in financial and operational terms – uptime protected, fraud avoided, response times cut – and track resilience as a KPI.
- Integrate AI into existing ISMS. Align AI projects with your existing ISO 27001/NIS‑style ISMS, risk registers and incident playbooks instead of inventing a parallel governance universe.
Lessons from critical infrastructure and SOCs
Coming from environments like national grids, gas networks and 24/7 SOCs, a few practical lessons translate directly into AI programmes. These points tend to perform well on LinkedIn because they are concrete, not theoretical.
- Design for failure from day one. In OT and national infrastructure, you assume components will fail and build for resilience; AI systems need the same mindset with fallbacks when models misbehave or become unavailable.
- Run AI incident simulations. The same way cyber teams now run tabletop exercises, leading organisations are starting to simulate AI‑specific incidents – data leakage via prompts, model manipulation, or AI‑driven fraud – and tune their response.
- Close the capability‑governance gap early. High‑performing security leaders are already being asked by their boards: “Can our governance keep up with our AI ambitions?” – and those who answer in business language, not only technical jargon, are getting budget and support.
The human and “inner” side of AI
The most shared AI content on LinkedIn also speaks to how AI is changing work, identity and meaning, not just technology. As AI automates more tasks, the differentiators become judgement, values and self‑awareness – the human operating system underneath the technology.
In practice, that means:
- Leaders who cultivate reflection and psychological resilience are better at making calm, ethical decisions when AI output conflicts with intuition or values.
- Teams with a strong sense of purpose adapt faster, because they see AI as an amplifier of meaningful work, not a threat to their existence.
For organisations, the next competitive advantage is not just “more AI”, but more integrated humans using AI with clarity, ethics and courage.
Measuring the Gap
How are you measuring the gap between your AI capability and your AI governance today?
And at a more human level – what practices are you putting in place so people stay grounded, not overwhelmed, as AI becomes part of their daily decision‑making?