From AI Regulation to Resilience Practice: What Continuity Teams Need to Do Next

• 03 Nov 2026
• Daniel

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Artificial intelligence governance is moving quickly from principle to operational requirement. The EU AI Act[1] introduces obligations on risk management and human oversight; NIS2 covers incident handling, business continuity and supply-chain security; DORA links ICT risk management to recovery and third-party dependencies; and the NIST AI Risk Management Framework treats AI risk as a continuous lifecycle through to incident response and recovery.

Read separately, these requirements can look like a new compliance burden. For continuity and resilience professionals, however, much of the underlying logic is familiar: knowing what is critical, what it depends on, how it recovers, who can change it, and what gets reported when it fails.

The opportunity is not to build a separate resilience structure for AI. It is to extend the controls organisations already trust to the AI systems now embedded in critical business processes.

This is not a capability gap. The BCI's Operational Resilience Report 2026[2] found that 72.5% of organisations already run a resilience programme, usually led by the business continuity manager rather than a purpose-built AI function. For most organizations - especially those without a dedicated AI governance function - the fastest, lowest-cost route to defensible AI governance is extending controls already in place, not building new ones.

Start with criticality and dependency

The first question should not be "Do we use AI?" but "Which critical services now depend on AI?" An AI capability may sit several layers below the service the business cares about - inside a fraud-detection process, a supply-chain platform, a customer-service workflow or a decision-support tool.

Existing business impact analysis can be extended to identify where AI supports a critical service, what data and third parties it depends on, what happens if the model becomes unavailable or unreliable, and whether a non-AI fallback exists.

Treat AI changes as operational changes

Models can be retrained, replaced, connected to new data sources or given new tools, and a system that was low-risk six months ago can become operationally significant once embedded in a critical workflow. AI changes should enter existing change-management processes, answering the same questions asked of any other change: what changed, what it affects, whether the dependency map and recovery plan still hold, and who approves deployment.

Bring AI into incident and recovery planning

An AI failure may not look like a conventional outage - the system can stay available while producing degraded or unsafe output. Continuity teams need thresholds that separate ordinary model variation from an incident requiring escalation. Recovery assumptions need re-examining too: a recovery time objective set for an internally hosted system may not hold once it depends on an external model or a third party's own recovery capacity, so recovery plans should be tested against that dependency, not assumed.

A practical starting checklist:

• Map which critical services depend on AI, and on what data and third parties.
• Route material AI changes through existing change management.
• Define thresholds for AI-related incident escalation.
• Test recovery and human-oversight arrangements before a crisis.

AI regulation may be new, but most of the controls needed to make AI resilient are not. The resilience profession already has mature disciplines for criticality, dependency, change, incident response and recovery. The task now is making sure those disciplines follow AI as it moves from experimentation into business-critical operations.