What is the difference between DORA and broader EU regulations such as NIS2 and the EU AI Act?

DORA, NIS2 and the EU AI Act: different legal problems, different control expectations

DORA is not a broad cybersecurity law and it is not an AI governance regime. Its purpose is to ensure that financial entities can withstand, respond to and recover from ICT disruption, including disruption created by third-party technology providers. By contrast, NIS2 sets a wider baseline for cybersecurity and incident management across many essential and important entities, while the eu ai act regulates how AI systems are placed on the market and used, especially where they are prohibited or classed as high risk. The distinction matters because the same organisation may face all three, but each law asks a different question about resilience, security and accountability. The European Commission’s EU AI Act regulatory framework is useful for seeing how AI obligations sit alongside, rather than replace, sectoral operational rules.

Practitioners often misread the overlap and assume one programme can satisfy all three without tailoring evidence, scope and ownership.

Where DORA stops and the wider EU regime begins

DORA is sector-specific: it applies to financial entities and concentrates on ICT risk management, incident reporting, resilience testing, third-party risk and information sharing. NIS2 is broader in sector coverage and is built around cybersecurity governance, incident handling, supply-chain security and business continuity for a much wider set of organisations. The EU AI Act is different again because its centre of gravity is not generic security posture but AI governance, risk classification, prohibited practices, conformity obligations and post-market oversight.

That difference changes how teams scope controls. Under DORA, the main organising unit is operational resilience in a regulated financial environment. Under NIS2, the organiser is cybersecurity governance for essential and important entities. Under the EU AI Act, the organiser is the AI system itself, its intended use and the obligations attached to its risk class. A bank deploying a high-risk AI model may therefore need one control set for DORA resilience, another for NIS2-style security governance where applicable, and a separate AI governance track for the model’s lifecycle and use restrictions.

The practical mistake is to collapse all three into a single compliance register without preserving the distinct legal tests, evidence types and reporting triggers that each regime expects. That is why the European Union’s NIS2 Directive - official EU legal text remains important reading for teams that need to separate cybersecurity duties from financial resilience duties.

• DORA asks whether the financial service can continue to operate through ICT disruption.
• NIS2 asks whether the organisation has appropriate cybersecurity governance and incident handling.
• The EU AI Act asks whether the AI system is permitted, classified and controlled appropriately for its risk level.

Where organisations blur those lines, they usually discover the gap only during audit preparation or incident response, when the required evidence no longer matches the legal duty.

How to integrate the three regimes without mixing their obligations

The most effective operating model is to build one joined-up governance structure with three distinct obligation tracks. The first track covers resilience and technology service continuity for DORA. The second covers enterprise cybersecurity governance, reporting and operational controls where NIS2 applies. The third covers AI inventory, classification, documentation, human oversight and change control for systems within the EU AI Act scope. The point is not to run separate organisations, but to avoid confusing one law’s evidence with another law’s proof requirements.

In practice, this means using common sources of truth such as asset inventories, supplier registers, incident logs and control testing records, but tagging each artefact to the regime that consumes it. For example, a third-party ICT service review may support DORA’s outsourcing expectations, while the same supplier may also be relevant to NIS2 supply-chain security and to the EU AI Act if it provides an AI component or model service. A single governance forum can oversee the portfolio, but the legal interpretation still needs regime-specific accountability. For an authoritative baseline on broad cybersecurity structuring, NIST’s NIST Cybersecurity Framework 2.0 is helpful as a cross-cutting control language, even though it is not a substitute for EU law.

• Use one inventory, but separate legal tags for DORA, NIS2 and AI Act scope.
• Use one incident process, but preserve different reporting thresholds and timeframes.
• Use one supplier governance model, but distinguish ICT resilience duties from AI system obligations.
• Use one assurance calendar, but keep test evidence aligned to the regime being assessed.

This guidance breaks down when organisations try to treat AI governance as a checkbox inside cybersecurity, because AI Act obligations often depend on system classification, intended purpose and lifecycle controls that security teams do not normally own.

Practitioner Guidance

What to prioritise: Build a scope matrix that separates entity-level coverage from system-level coverage. DORA is entity and service resilience driven, NIS2 is organisational cybersecurity driven, and the EU AI Act is AI-system driven; if those scopes are mixed, controls will look compliant while the underlying legal duty remains ambiguous.

What to verify: Check whether the same artefact is being reused for different legal tests without revalidation. A supplier assessment, incident register or model register can support all three regimes, but only if it explicitly records which obligation it satisfies and what evidence standard applies.

Common mistake: Treating “overlap” as equivalence. Overlap means some controls can be shared; it does not mean the regimes are interchangeable, and that difference becomes material when regulators ask for regime-specific accountability and reporting evidence.

Practitioner takeaway: The safest operating model is shared governance with separated legal logic: one control environment, three distinct compliance interpretations.

Related resources from NHI Mgmt Group

• What is the difference between transparency controls and high-risk AI controls under the EU AI Act?
• What is the difference between prohibited AI practices and high-risk AI systems under the EU AI Act?
• What is the difference between NIST AI RMF, ISO 42001, and the EU AI Act?
• What is the difference between the UK Code of Practice for AI Cyber Security and the EU AI Act?