AI Due Diligence in Romania: What Foreign Investors Should Check
Foreign investment • AI governance • Romania
AI due diligence in Romania asks whether a target company’s AI use, data, contracts and public claims support the investment case. It is not a technical demonstration of a model and it is not a generic AI policy review. For a buyer, investor or lender, the question is whether the target has identified the systems it uses, can lawfully operate them, owns or can use the assets it relies on, and has a credible plan for the risks that will remain after closing.
What is AI due diligence in a Romanian transaction?
AI due diligence is a transaction-focused legal and commercial review. It identifies whether the target’s use of AI creates liabilities, restrictions, missing rights or implementation costs that could affect price, risk allocation or post-closing operations.
For a Romanian target, the review should cover the target’s Romanian operations and any AI outputs used in the European Union. The EU AI Act is directly applicable across the EU and operates alongside the GDPR where personal data is processed. A seller’s statement that it uses only a third-party AI tool does not end the inquiry: the target may still be a deployer, customer, controller, employer or regulated business with its own duties.
This guide has a different purpose from our analysis of AI vendor contracts in Romania, which focuses on the agreement with a supplier, and from DPIA vs FRIA in Romania, which focuses on assessment triggers for a deployment. Here, the investor is deciding what must be verified before, at and after a deal.
Start with the target’s actual AI footprint
Do not begin with a broad question such as “Does the company use AI?” Ask what system or model is used, for which decision, with which data, by whom, and whether the target sells, deploys, develops, fine-tunes or merely accesses the tool.
A useful data-room request separates customer-facing products from internal tools. It should identify models, APIs, software providers, hosting and cloud dependencies, integrations, datasets, prompts or knowledge bases, material outputs, users and the decisions influenced by each use case. The inventory should also record planned products or features that have not yet launched but are material to the investment thesis.
Deal-side navigator
Select a deal question to see the first evidence to request
Click or tap a card. Keyboard users can Tab to a card and press Enter or Space. Each selection stays visible until you choose another one.
Request a system inventory, product descriptions, roadmaps, supplier contracts, architecture summary and evidence of the target’s material AI claims. Compare marketing language with the technology and operating model actually in use.
Map the target’s role under the AI Act separately from its GDPR role. Identify prohibited practices, potential high-risk use cases, transparency duties, personal-data processing, automated decisions and any existing assessments.
Trace contractual rights in datasets, software, model access, fine-tuning, confidential information and output. Do not assume that a statement that the company “owns the AI” proves rights to each component or its intended commercial use.
Translate confirmed findings into a remedy: further diligence, remediation before closing, a post-closing plan, targeted warranties, disclosure, indemnity, price adjustment or a condition. The appropriate mechanism depends on materiality, control and the deal structure.
Which legal and commercial issues should an investor test?
Click or tap a row to highlight the diligence takeaway. On a small screen, swipe the table sideways.
| Review area | What to test | Possible deal response |
|---|---|---|
| Whether the target’s product, sales material and internal inventory describe the same systems, functions, limits and dependencies. | Correct the diligence scope, ask for technical confirmation and qualify representations that are broader than the evidence. | |
| Whether a use case may be prohibited, high-risk, subject to transparency rules or linked to a general-purpose AI model supply chain. | Obtain a classification record, identify compliance timing and budget for any remediation or implementation work. | |
| Data flows, controller/processor roles, legal bases, Article 22 issues, DPIA screening, security measures and cross-border transfers. | Require a privacy remediation plan, review the DPA and test whether the target can continue the relevant processing after closing. | |
| Whether datasets, prompts, files or customer information may lawfully be used for development, fine-tuning, testing or supplier improvement. | Limit or stop impermissible use, obtain consents or contractual permissions where appropriate, and reserve a specific risk allocation. | |
| Ownership and licences for software, open-source components, models, training materials, brand assets, output and third-party claims. | Confirm chain of title, address licence conflicts and tailor warranties or indemnities to the assets that support the valuation. | |
| Model, cloud and subprocessor dependencies, location, termination, audit evidence, model changes and service continuity. | Seek consent, amendment, transition assistance or a post-closing migration plan if a dependency cannot support the buyer’s intended use. | |
| Policies, ownership, AI literacy, logs, testing, monitoring, incident response, complaint handling and escalation records. | Set a post-closing governance plan with owners, deadlines and evidence requirements rather than relying on a generic policy. |
The European Commission describes the AI Act as a risk-based framework for developers and deployers. It identifies employment, credit scoring and access to essential services among the examples that may be high-risk. See the Commission’s AI Act overview and application timetable.
AI Act review: classify before you value the risk
Do not treat “AI Act compliant” as a sufficient diligence answer. Contractual, sector-specific, data-protection and AI Act obligations must be assessed separately. The investor should identify the system, its intended purpose, the target’s role and the rules that apply now or later. The relevant date can affect both risk allocation and integration planning.
The AI Act’s prohibitions, AI literacy obligations, governance rules, GPAI-model obligations and transparency rules have different application dates from the rules for high-risk systems. The Commission states that Annex III high-risk use cases, including employment and credit-scoring examples, are scheduled to apply from 2 December 2027, while high-risk systems embedded in regulated products have a later date of 2 August 2028. A diligence report should distinguish obligations already applicable from future obligations that could require a funded implementation plan. At the time of publication, the applicable AI Act timetable should be verified against the latest EU legislation and implementation guidance, including the official AI Act Service Desk timeline.
The analysis should also ask whether the target is developing an AI system, placing it on the market, deploying it in its own business, importing it, distributing it or using a third-party service. These labels are not interchangeable with GDPR controller and processor roles. For the contract-facing part of that review, see AI Vendor Contracts in Romania.
GDPR and data review: look beyond the privacy policy
The decisive question is what happens to personal data at each stage of the AI lifecycle. A target may process personal data in training, testing, deployment, monitoring, logs, prompts, support and human review, even where the product is marketed as automated or anonymised.
The review should map the data categories, purposes, retention, recipients, access, transfer mechanisms and contractual roles. Where the use involves profiling, recruitment, credit, insurance, pricing or decisions that may significantly affect individuals, check the actual decision flow and safeguards rather than relying on a generic human-review statement. Article 35 GDPR requires a DPIA where processing is likely to result in a high risk to the rights and freedoms of natural persons; Article 22 has separate rules for certain solely automated decisions.
The European Data Protection Board has also confirmed that the anonymity of an AI model trained with personal data must be assessed case by case. That matters for a target relying on a statement that a model, dataset or output is anonymous. Read EDPB Opinion 28/2024. For the broader framework, see our guide to GDPR compliance when using AI in Romania.
Data, intellectual property and contract rights
AI value is often dependent on rights that sit outside the target’s own code. The investor should trace the legal basis for using data, third-party models, cloud infrastructure, open-source components, output and confidential information.
Review the complete contract suite, not only the signed master agreement. Order forms, online terms, acceptable-use policies, data-processing agreements, security schedules, open-source notices and API terms can all affect the target’s rights. In particular, check whether a provider can use customer or target data for model training, whether the provider can change the model or service unilaterally, and whether the target can export its data and configurations on exit.
Ownership language for AI-generated output should be read carefully. A contractual promise may create a licence or allocation between the parties, but it does not necessarily guarantee exclusivity or copyright protection in every output. The copyright status of AI-generated content may vary depending on the level of human creative input and the applicable jurisdiction. The review should identify the use the target needs to make of the output and whether third-party rights, human authorship requirements, confidentiality or contractual restrictions could limit that use.
What should the investor request in the data room?
- Request a current AI inventory. Include internal tools, customer-facing products, models, APIs, plugins, fine-tuning, integrations and material planned features.
- Obtain a use-case map. Record intended purpose, users, affected people, decisions, data inputs, outputs, human review and country of deployment.
- Collect AI governance records. Ask for role assessments, policies, training records, system documentation, risk logs, testing, monitoring and incident procedures.
- Review AI Act screening. Identify prohibited practices, potential high-risk systems, transparency obligations, GPAI dependencies and the applicable timetable.
- Map personal-data processing. Review privacy notices, legal bases, Article 22 analysis, DPIAs, processor arrangements, security controls and transfers.
- Trace data rights. Check source, licence, consent or other permission for data used in development, testing, fine-tuning and ongoing service delivery.
- Review the contract stack. Read supplier, customer, cloud, API, DPA, security, outsourcing and change-control documents together.
- Confirm IP and open-source position. Request code provenance, licences, notices, ownership assignments, third-party claims and output-use restrictions.
- Test material statements. Compare product marketing, investor materials and customer commitments against the available technical and legal evidence.
- Assign the deal response. Separate issues requiring price, warranty, indemnity, condition, remediation, disclosure or post-closing integration action.
How should findings affect the transaction documents?
Translate each material finding into an owner, timing and remedy. A diligence report is useful only if the SPA, investment agreement, disclosure process and integration plan reflect the issues that have been identified.
The appropriate response will depend on the transaction structure and the seller’s ability to remediate. A buyer may need targeted warranties concerning data rights, AI-related regulatory compliance, ownership, contract compliance, absence of claims or material incidents. Confirmed gaps may justify a specific indemnity, a pre-closing remediation covenant, a post-closing plan, a condition or a tailored disclosure. The drafting should not assume that a general compliance warranty captures the actual issue.
Post-closing planning is equally important where the buyer will integrate systems, move data, introduce a new group policy, change suppliers or expand the target’s use case. These changes can alter the GDPR and AI Act analysis. If an assessment is required, the timing should be addressed before the relevant processing or deployment begins. Our DPIA vs FRIA guide explains why those two assessment routes must be screened separately.
When does a separate specialist review become necessary?
A focused AI legal review should be coordinated with corporate, technical, information-security, employment and commercial due diligence when the target develops AI products, relies on proprietary datasets, makes regulated-sector decisions, uses AI in recruitment or credit processes, processes sensitive personal data, markets compliance claims, or has important dependencies on a small number of providers. The workstreams should share the same factual inventory, but each should retain its own legal questions and conclusions.
How Atrium Romanian Lawyers can assist
Atrium Romanian Lawyers can coordinate the legal workstream for AI-related due diligence in a Romanian investment, acquisition or internal reorganisation. The review can cover AI Act role and use-case screening, GDPR and data-contract questions, supplier and customer terms, intellectual property, employment and operational governance, and the translation of findings into transaction documents or an integration plan.
Client experience
AI due diligence during the acquisition of a Romanian technology company
An international investor considered acquiring a Romanian technology company that relied extensively on AI-enabled software products and third-party AI services.
During the due diligence process, the buyer requested confirmation regarding AI Act compliance, data rights, intellectual-property ownership and the target’s dependencies on external AI providers.
The review identified gaps between the target’s public marketing materials and its internal documentation, uncertainties regarding the scope of rights over certain datasets, and contractual limitations affecting the use of third-party AI services after closing.
Atrium Romanian Lawyers coordinated the legal review of the AI use cases, supplier contracts, GDPR implications and intellectual-property position. The findings were translated into targeted warranties, disclosure items and a post-closing remediation plan.
The transaction proceeded with a clearer allocation of regulatory, contractual and operational risks and with a structured roadmap for post-closing compliance measures.
This example has been anonymised and simplified for publication. The appropriate legal analysis depends on the system, data, contract structure and facts of each matter.
Frequently asked questions
Does every investment in a Romanian company need AI due diligence?
No. The scope should be proportionate to the target’s actual use of AI and the importance of that use to the transaction. A company using a limited internal tool may require a focused review. A target selling AI-enabled products, using sensitive data or making decisions affecting people may need a deeper legal and technical workstream.
Is AI due diligence the same as an AI Act compliance audit?
No. AI Act compliance is one part of the review. Transaction diligence also considers ownership, licences, customer commitments, personal data, confidentiality, technical dependencies, product claims, change control and what the buyer will need after closing. The correct scope follows the investment thesis and the target’s operating reality.
Can a seller rely on a supplier’s AI compliance statement?
Supplier information can be relevant evidence, but it does not by itself establish that the target’s own deployment is compliant. The investor should check whether the statement identifies the actual system, model, purpose, data, users, territory, contract terms and responsibilities relevant to the target’s use case.
Should a buyer ask for the target’s DPIAs?
Where the target operates AI systems involving personal data and has conducted or screened for a DPIA, the relevant material should be reviewed subject to confidentiality controls. The question is not simply whether a document exists, but whether it reflects the current processing, risks, safeguards, changes and any residual issues requiring follow-up.
Can AI findings be addressed after closing?
Sometimes. The decision depends on the nature of the issue, legal exposure, urgency, operational dependency and the buyer’s ability to control remediation. A defensible post-closing plan should identify the owner, evidence, budget, deadlines and the effect on continued use. Some issues may need to be resolved before closing or before a planned deployment.
What is the most common gap in AI diligence?
A frequent gap is that the target has a high-level AI policy or vendor contract but no reliable inventory linking systems, use cases, data, roles, evidence and decision owners. The first practical step is usually to build that factual map before drawing legal conclusions or negotiating transaction protection.
