An AI register is not a spreadsheet listing chatbots. It is the control point that tells a compliance team what AI exists, who is accountable for it, what it does, which legal obligations apply, and whether evidence can be produced when an auditor or regulator asks. Without that record, risk classification, monitoring and board assurance rest on assumption rather than proof.
For organisations deploying AI across business functions, understanding how to register AI systems means building an operational governance process, not completing a one-off procurement questionnaire. The register must remain current as models, suppliers, use cases and decision-making arrangements change.
Start with the right meaning of “register”
There are two distinct registration requirements that are often confused.
First, every organisation needs an internal AI systems register. This is its authoritative inventory of AI systems in development, procurement, testing, deployment or retirement. It supports the EU AI Act, ISO/IEC 42001, data protection governance, supplier assurance and internal risk management.
Second, the EU AI Act requires registration in the EU database for specified high-risk AI systems. Under Article 49, providers must register high-risk systems before placing them on the market or putting them into service, subject to the Act’s detailed scope and exemptions. Certain public authorities or EU institutions acting as deployers also have registration duties for high-risk systems they use. An internal register does not replace this external legal registration, and an EU database entry does not replace a complete internal governance record.
The practical rule is simple: register every material AI system internally; determine, through a documented classification, whether an EU database registration or another sector-specific notification is required.
Set the register boundary before collecting records
A register fails when teams only record tools that carry an obvious AI label. The boundary should cover systems that infer, predict, recommend, generate content, rank, classify, optimise or make decisions using machine learning, logic- and knowledge-based approaches, or other techniques within the EU AI Act definition.
Include externally purchased software with embedded AI, internally developed models, generative AI assistants, automated decision-support tools, APIs connected to foundation models, and systems operated by suppliers on the organisation’s behalf. Shadow AI matters too. A marketing team’s unsanctioned transcription tool may create lower regulatory exposure than an automated recruitment screening product, but both belong in the inventory.
Do not make the scope so broad that it becomes unworkable. Standard software features with no meaningful AI functionality may be captured through an initial intake and closed with a recorded rationale. The objective is a defensible decision trail, not an inflated catalogue.
Assign an accountable owner for every system
A system without a named owner is not governed. Registration should identify both the business owner, who is accountable for the purpose and use of the system, and the technical owner, who can explain its architecture, data flows, configuration and changes. Procurement, information security, data protection and legal teams should have defined review responsibilities rather than becoming default owners.
The record should also state the organisation’s role under the EU AI Act. You may be a provider, deployer, importer, distributor, authorised representative or more than one of these across different systems. This distinction changes the obligations. A business using a supplier’s AI product is usually a deployer, but substantial modification, rebranding or placing a system on the market under its own name can shift responsibility towards provider obligations.
Ownership needs a review cadence. If a product owner leaves, a supplier changes, or a system moves from a pilot to production, the register should trigger reassignment and reapproval. Annual attestation alone is rarely enough for higher-risk use cases.
Capture the evidence needed to classify the system
The first registration record should be structured enough to support classification without asking teams to write a legal memo. Collect the system name, supplier or development team, business purpose, users, affected individuals, deployment locations, model type, input and output data, integrations, and whether human decisions rely on the output.
Then capture the facts that determine regulatory exposure. Does the system interact with people? Does it process personal or special category data? Is it used in employment, education, creditworthiness, essential services, law enforcement, migration, justice, biometric identification or another area listed in Annex III? Is it a safety component of a product covered by Annex I legislation? Could it materially affect an individual’s access to an opportunity, service or right?
This information supports a recorded decision against the EU AI Act’s prohibited practices, transparency obligations, general-purpose AI provisions and high-risk categories. It also gives the organisation a basis for its ISO/IEC 42001 AI system impact assessment and risk treatment process.
Classification is not a label selected once and forgotten. A customer-service assistant may initially be a limited-risk transparency case. If it is later connected to account eligibility, complaint outcomes or vulnerable customer triage, the classification must be reconsidered. Register changes should therefore initiate reassessment, not merely update a description.
Treat high-risk classification as a control gateway
Where a system is classified as high risk, the register should open a more demanding workflow. The legal analysis needs to identify the applicable category, the organisation’s role, the basis for the decision, and any claimed exemption. A conclusion that a system falls outside a high-risk category because it does not pose a significant risk must be supported by evidence, not a bare assertion.
For providers, the high-risk workflow should connect the register to the required quality management, risk management, technical documentation, record-keeping, data governance, human oversight, accuracy, robustness, cybersecurity and post-market monitoring evidence. For deployers, it should connect to instructions for use, human oversight arrangements, input-data controls, monitoring, incident escalation and any required fundamental rights impact assessment.
The EU AI Act applies in phases. Organisations should monitor the current applicability dates, implementing measures and guidance relevant to their systems rather than assuming all provisions take effect at the same time. A dated legal assessment in the register makes that position reviewable when the schedule or guidance changes.
Link registration to approvals, not just discovery
An inventory built through surveys is useful, but incomplete if it is disconnected from operational decisions. Make registration a mandatory gate in procurement, software onboarding, development lifecycle controls and change management.
A new supplier should not progress from evaluation to production without an AI intake. Internal development teams should register a proposed system before training, testing with production data or releasing it to users. Material changes – such as a new model provider, expanded user group, new data source, altered decision logic or deployment in another jurisdiction – should prompt an update and proportionate reassessment.
This approach avoids a common failure mode: the compliance team discovers an AI system after it has already been integrated into a critical process. It also reduces friction. Teams answer a defined set of questions once, and the resulting record can support security review, DPIA screening, vendor assurance and board reporting.
Record controls and residual risk in the same place
A register that ends at classification creates another spreadsheet problem. Each system record should show the controls required by its risk profile, the control owner, evidence location or artefact, implementation status, review date and outstanding actions.
For example, a generative AI tool handling confidential information may require contractual restrictions, access controls, approved-use guidance, prompt and output handling rules, training, logging and supplier monitoring. A high-risk decision-support system will require stronger evidence around human oversight, performance testing, bias and data-quality controls, traceability, incident management and change control.
The point is not to apply every control to every tool. Proportionate governance is more credible and more sustainable. A low-impact drafting assistant should not consume the same assessment effort as AI used to influence recruitment, lending or healthcare decisions. The register should make that distinction visible.
Maintain an audit trail that can survive scrutiny
Auditors and regulators will ask how the organisation knows its inventory is complete, who approved a classification, what evidence was reviewed, and whether controls were operating at the relevant time. Free-text notes and versionless files make those questions expensive to answer.
Keep a time-stamped history of record creation, classification decisions, approvals, control tests, incidents, material changes and retirement. Retain the assessment rationale even when a system is judged outside the EU AI Act’s high-risk scope. Negative decisions are evidence too.
A dedicated system of record such as Endaxi AIG can centralise this workflow across inventory, legal classification, assessments, controls, board reporting and audit access. The value is not a larger dashboard. It is the ability to produce a consistent, reviewable evidence pack without stitching together procurement folders, risk registers and disconnected spreadsheets.
Make the register useful to the board
Senior stakeholders do not need a list of model names. They need a clear view of exposure: how many systems are active, which are high risk, where ownership is missing, what controls are overdue, which suppliers create concentration risk, and whether deployments are progressing without approval.
Use the register to report exceptions and decisions, not just totals. A board pack should show the systems that require action, the residual risks accepted by management, and the resources needed to close material gaps. That turns AI registration from an administrative exercise into accountable governance.
A well-run AI register is never finished, because the systems it governs are not static. Build it as a living control: complete enough to support legal decisions, disciplined enough to drive approvals, and accessible enough that the business will actually use it when the next AI tool arrives.