Introduction
The acquisition landscape for AI companies in Southeast Asia has shifted dramatically. In 2025, we saw over $4.2 billion in AI-related M&A activity across ASEAN, with Singapore serving as the primary hub for deal structuring and regulatory coordination. As we enter 2026, acquirers are becoming significantly more sophisticated in their technical due diligence — and founders who are not prepared for this scrutiny risk leaving substantial value on the table or, worse, seeing deals collapse at the eleventh hour.
This checklist is built from our direct experience advising on over 30 AI-related transactions. It covers the unique due diligence considerations that do not appear in traditional M&A playbooks — the issues that are specific to companies whose core value resides in trained models, proprietary datasets, and algorithmic IP.
IP & Model Provenance
The single most common deal-breaker we encounter in AI acquisitions is unclear model provenance. Sophisticated acquirers now routinely engage technical advisors to trace the lineage of your models — from the foundational architecture (and its licensing terms) through every fine-tuning iteration to the production model that generates revenue.
You must be able to demonstrate clear chain of title for every component. If your model is built on top of an open-source foundation model, you need to verify that your use complies with the applicable license. Models released under Apache 2.0 provide broad commercial rights, but models under variants of the LLaMA Community License, RAIL licenses, or the OpenRAIL framework carry use restrictions that can directly impact an acquirer's intended deployment. We have seen a $120M acquisition delayed by three months because the target company could not confirm that its fine-tuned model complied with the base model's acceptable use policy.
Beyond licensing, acquirers will examine whether all contributors to the model — employees, contractors, researchers — have executed proper IP assignment agreements. Under Singapore law, the default position under Section 30(6) of the Copyright Act 2021 is that the employer owns copyright in works created by employees in the course of employment. However, this presumption can be rebutted, and it does not cover independent contractors. Every person who has contributed to your model's development should have a written, executed IP assignment agreement. If you engaged researchers at A*STAR or university collaborators, the IP ownership terms in those collaboration agreements must be reviewed carefully.
Data Rights & Privacy
Training data is the foundation of your AI company's value, and it is also the area where legal risk is most concentrated. Acquirers will want to understand three things about your training data: how you obtained it, what rights you have to it, and whether you can transfer those rights in a transaction.
Under Singapore's Personal Data Protection Act (PDPA), if your training data includes personal data, you must have obtained consent for the specific purpose of AI model training — or be able to rely on one of the legitimate exceptions under the PDPA. The 2021 amendments to the PDPA introduced a "business improvement" exception that may cover certain uses of personal data for AI training, but this exception has limits. The Personal Data Protection Commission (PDPC) has issued advisory guidelines clarifying that automated decision-making using personal data remains subject to the consent framework, and the PDPC's enforcement actions in 2024 and 2025 demonstrate that regulators are actively scrutinizing AI companies' data practices.
For cross-border data transfers — which are almost inevitable if your training data originates from multiple jurisdictions — you need to comply with the PDPA's data transfer provisions. Singapore adopted a pragmatic approach through its data transfer mechanisms, including contractual arrangements, binding corporate rules, and reliance on the APEC Cross-Border Privacy Rules (CBPR) system. However, if any of your training data originates from the EU, you must also comply with GDPR transfer requirements, which are significantly more restrictive.
Regulatory Compliance
Singapore's regulatory environment for AI is among the most developed in ASEAN, and compliance with local frameworks is increasingly a prerequisite for acquirers. The Model AI Governance Framework, first published by IMDA in 2019 and updated in 2020, provides a voluntary but highly influential set of principles for responsible AI deployment. While the framework is not legally binding, we advise every AI founder to implement its core recommendations — particularly around explainability, transparency, and human oversight — because acquirers view compliance as a proxy for operational maturity.
IMDA's AI Verify toolkit, launched in 2023, provides a testing framework for assessing AI systems against internationally recognized governance principles. Having your models tested and verified through AI Verify is not mandatory, but it is a powerful signal to acquirers that your AI governance is robust. In our experience, companies that have completed AI Verify testing achieve 10-15% higher valuations in M&A negotiations because acquirers perceive lower integration risk.
Looking beyond Singapore, founders must also consider the EU AI Act, which entered into force in August 2024 with a phased implementation timeline. If your AI system is deployed in the EU or if your acquirer intends to deploy it in the EU, you will need to demonstrate compliance with the applicable risk tier. High-risk AI systems face extensive requirements around data governance, transparency, human oversight, and accuracy — requirements that can take 12-18 months to fully implement if you are starting from scratch.
Practical Recommendations
Based on our experience closing AI acquisitions in Singapore and across ASEAN, here are our top recommendations for founders preparing for due diligence. First, conduct an internal IP audit at least six months before engaging with potential acquirers. Map every model component, every dataset, and every contributor. Ensure that all IP assignments are executed and that all open-source licenses are complied with. Second, engage a specialized data privacy counsel to audit your data practices under the PDPA and any other applicable data protection regimes. Address any gaps before they become due diligence findings. Third, implement the Model AI Governance Framework and, if possible, complete AI Verify testing. The investment in compliance will pay for itself many times over in deal valuation and speed to close. Finally, prepare a clean data room that documents your model provenance, data lineage, regulatory compliance, and IP chain of title. Acquirers who receive a well-organized data room move faster and negotiate less aggressively on price.