Artificial intelligence is now an integral part of European business strategies. For executives, the challenge is to harness AI’s resilience-enhancing capabilities while ensuring full compliance with strict EU business continuity and disaster recovery regulations. The Digital Operational Resilience Act (DORA), the NIS2 Directive, and GDPR all demand accountability, transparency, and demonstrable resilience. AI can be an asset, but it can also introduce new compliance gaps if not governed correctly.
EU regulatory anchors for continuity
- Digital Operational Resilience Act (DORA): Financial institutions and critical ICT providers must demonstrate their ability to withstand and recover from ICT disruptions. This includes ICT risk management, incident reporting, resilience testing, and oversight of suppliers.
- NIS2 Directive: Broadens cyber resilience requirements across sectors such as energy, healthcare, transport, and digital infrastructure.
- GDPR: Requires that personal data remain confidential, integral, and available. This implicitly ties AI initiatives to robust continuity plans, as AI models often process sensitive data that must be safeguarded during disruptions.
How AI strengthens continuity and recovery
AI brings tangible improvements in resilience planning:
- Faster anomaly detection and response across IT and supplier ecosystems.
- Automated recovery orchestration, accelerating service restoration.
- Scenario simulation at scale, enabling stress tests aligned with DORA requirements.
- AI-enhanced cyber defence, improving intrusion detection and incident response.
As noted in our ‘Why Resilient Network Infrastructure is Crucial in the Age of AI’ blog post, networks must adapt, self-monitor, and detect threats. AI strengthens these capabilities, making continuity plans more credible to regulators.
Where AI complicates compliance
While AI enhances resilience in many ways, its adoption also introduces risks that boards cannot ignore. These challenges are particularly acute in regulated EU markets, where transparency, accountability, and verifiability are central to supervisory expectations.
Opaque AI decisions
Many AI systems, particularly those based on machine learning, operate as “black boxes.” If recovery actions are triggered without a clear explanation of why, auditors may consider continuity plans non-compliant. Regulators such as the European Banking Authority have already stressed the need for explainability in automated risk management systems. Boards must therefore insist on AI models that provide documented decision paths, ensuring that AI-driven actions can be justified during inspections or after a disruption.
Model fragility and adversarial risks
AI models can fail in unexpected ways. Fragility arises when models trained on narrow data cannot generalise to novel crises, such as simultaneous cyber and physical disruptions. Adversarial risks occur when attackers deliberately manipulate inputs to mislead systems — for example, disguising a malicious network event to bypass detection. In both cases, continuity assumptions may collapse if AI models behave unpredictably under pressure. To mitigate this, boards should require stress testing of AI under “edge case” disruption scenarios, not just routine incidents.
Third-party dependencies and concentration risk
DORA explicitly warns against excessive reliance on critical ICT service providers. If a continuity strategy hinges on a small number of AI vendors or hyperscale cloud providers, the organisation faces concentration risk. A disruption or regulatory action against a provider could cascade into systemic outages. Boards must therefore catalogue dependencies, develop exit strategies, and diversify AI supply chains, aligning with DORA’s third-party oversight requirements.
Model security vulnerabilities
AI models are now themselves attack targets. Techniques such as data poisoning (corrupting training data), model theft, or prompt injection can compromise continuity systems. If threat actors succeed, recovery tools could malfunction or provide false assurance, expanding the attack surface. Under NIS2, which requires proportionate technical and organisational measures, boards must ensure that AI systems are hardened through continuous monitoring, threat modelling, and adversarial resilience testing.
These risks reflect a broader theme: AI is not inherently compliant. Without board-level governance, the same technology that strengthens resilience can introduce new points of failure. This is why we emphasised in our ‘Predictions for 2025 for Communications and Cybersecurity’ blog post that AI adoption must go hand-in-hand with vendor risk management, regulatory alignment, and robust security practices.
Executive-level actions to align AI with EU mandates
- Embed AI into governance frameworks. Map AI use cases directly to DORA, NIS2, and GDPR controls. Require transparency and auditability.
- Maintain hybrid resilience. Keep human-led recovery processes alongside AI automation to satisfy redundancy expectations.
- Test to validate. Run crisis scenarios with AI in the loop and document outputs as evidence for supervisors.
- Manage vendor dependencies. Catalogue AI suppliers, define fallback strategies, and monitor compliance.
- Harden AI models. Apply red teaming and monitoring to protect AI pipelines and ensure regulatory alignment.
Our ‘Where the European CCaaS Market Is Heading in 2025’ blog post reinforces that major public sector and enterprise transformations will face heightened regulatory scrutiny. Embedding AI resilience and compliance into vendor contracts and workflows is now a board-level necessity.
Looking ahead
European regulators are signalling that AI will be scrutinised as closely as any other critical ICT capability. The question for boards is not whether AI enhances continuity, but whether it does so in a transparent, auditable, and compliant manner.
Now is the time to:
- Commission an AI resilience readiness review aligned to DORA, NIS2, and GDPR.
- Challenge your CIO, CISO, and CRO to present evidence of explainability, redundancy, and supplier oversight in AI continuity planning.
- Engage trusted partners like Damovo to benchmark your resilience framework against EU best practice and close compliance gaps before supervisors identify them.
Resilient leaders will treat AI not as a shortcut, but as a strategic capability that strengthens both trust and competitiveness. To discuss how Damovo can help you build AI-enabled resilience that meets regulatory requirements and protects your enterprise, please get in touch with us.
Andrew Hay is a results-driven and execution-focused executive with a proven track record in leading and scaling international operations for cybersecurity companies. He brings deep expertise in driving operational excellence, optimising processes, and delivering exceptional client satisfaction. Andrew has successfully led and expanded global engineering, research, sales, marketing, and security teams, contributing to the growth of companies from their early stages through to acquisition.