Topic starter
06/11/2026 6:37 pm
TL;DR: NIS2 does not create a special AI category, but DeepKeep’s analysis shows AI used in covered services, connected systems, or company data is already inside the directive’s risk scope, along with management oversight, proportionate controls, and incident timelines. Governance now has to cover AI usage, data paths, tool access, and evidence collection end to end.
Editorial analysis by NHI Mgmt Group, based on content published by DeepKeep: “NIS2 Compliance for Enterprise AI”.
Key questions
Q: How should teams determine whether an AI service falls under NIS2?
A: Teams should classify an AI service under NIS2 by checking whether it is part of the essential or important entity’s network and information systems, supports a covered business function, and is operated or materially controlled by the organisation.
Q: Why do AI agents create new governance risk for GRC programs?
A: AI agents can request access, process data, and generate trust artefacts faster than human review cycles can keep up.
Q: How do runtime controls reduce AI tool abuse?
A: Runtime controls reduce AI tool abuse by limiting what an agent or model can do at the moment of use.
Practitioner guidance
- Map AI services to NIS2 scope Classify each AI capability by the covered service it supports, the data it reaches, and the systems it can influence, then record the national scope decision for each operating jurisdiction.
- Extend asset inventories to AI workflows Include browser assistants, embedded copilots, coding agents, retrieval sources, and external AI services in the same inventory process used for other production applications.
- Test prompt-injection and tool-abuse paths Run repeatable tests that show whether an AI workflow can be steered into calling tools, exposing data, or crossing intended data boundaries under realistic input conditions.
Bottom line: NIS2 can already cover enterprise AI when the system supports a regulated service, reaches operational systems, or processes company data.
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06/11/2026 6:41 pm
NIS2 makes AI governance an enterprise cyber control issue, not a model-management side topic. The directive does not need an AI-specific chapter to matter here, because it already captures the business service, the data path, and the operational dependency. That means AI inventories, access decisions, and exception handling now sit inside a regulated governance model. Practitioners should treat AI use as part of the covered service stack, not as an innovation exception.
A few things that frame the scale:
- Enterprise AI use rose from 55% of organisations in 2023 to 88% in 2025, according to McKinsey’s Global Surveys on the State of AI.
A question worth separating out:
Q: What evidence should teams preserve after an AI security incident?
A: Teams should preserve prompts, model inputs and outputs, tool calls, system messages, access logs, API token use, policy decisions, and the affected model or agent configuration. That evidence shows what the AI system saw and did, which permissions it had, whether data was exposed, and whether the incident came from misuse, compromise, or control failure.
👉 Read our full editorial: NIS2 reaches AI systems through governance, data and telemetry