• UK leaders now rank AI and automation as the top drivers of organisational resilience.
  • Despite that, most firms still define “resilience” mainly as cyber security, not broader AI or operational risks.
  • This mismatch is creating a growing cyber gap that could leave organisations exposed as AI use expands.
  • Experts say businesses must broaden resilience planning, invest in skills and align cyber and AI strategies.

What the research found

New research shows a clear shift in how UK business leaders view the foundations of resilience: AI and automation are now seen as the primary factors that will keep organisations running through disruption. At the same time, however, many firms continue to equate resilience almost exclusively with cybersecurity measures — a narrow definition that fails to capture the new risks and operational changes introduced by AI.

Why the mismatch matters

Treating resilience as only a cybersecurity problem creates blind spots. AI and automation change how services are delivered, how decisions are made and how rapidly operations can scale. Vulnerabilities can appear not just as classic cyberattacks, but as failures of models, data pipelines, supply chains or automation workflows. If planning and budgets remain focused mainly on perimeter defence, organisations may be unprepared for these different failure modes.

Where the cyber gap is widening

The gap is both strategic and practical. Strategically, decision-makers may not yet have revised policies, governance and risk frameworks to include AI-specific threats such as model drift, data poisoning or automation logic errors. Practically, teams often lack the skills and tooling to monitor and respond to incidents that involve AI components rather than only network or endpoint breaches. The result is a resilience posture that looks strong on paper but can fail in real-world AI-driven incidents.

What leaders should do now

  • Broaden the definition of resilience: include AI robustness, data integrity, automation recovery and supplier continuity alongside cybersecurity.
  • Update governance and risk assessments to cover AI-specific failure modes and operational impacts.
  • Invest in skills and cross‑team cooperation: security, data science, engineering and operations must plan together.
  • Adopt observability and testing for automated systems — continuous monitoring helps detect model or pipeline problems early.

Why this matters to customers and partners

Customers and partners will increasingly expect proof that organisations can safely run AI-enhanced services. Failing to demonstrate resilience across AI and automation—not just classic cyber hygiene—can erode trust, lead to compliance issues and create business continuity risks.

Looking ahead

The research signals a turning point: leaders recognise AI’s central role in resilience, but many organisations still need to catch up on how to secure and sustain AI-driven operations. Closing the cyber gap will require deliberate policy changes, targeted investment and stronger collaboration between security and AI teams. Firms that act now gain a practical advantage — and reduce the chance of being blindsided by the next disruption.

Image Referance: https://itbrief.co.uk/story/uk-firms-see-ai-as-key-to-resilience-cyber-gap-grows