75 Regulatory Context: Data Governance and Privacy Although Israel did not enact a standalone AI statute, regulatory developments materially affect AI transactions. Amendment No. 13 to the Protection of Privacy Law, entered into force on August 14, 2025, strengthening enforcement authority and modernizing compliance obligations relating to personally identifiable information (“PII”) processing. More traditional AI diligence is often, in practice, data diligence. Training datasets may contain PII, proprietary information, regulated categories of data, or enterprise data subject to contractual confidentiality constraints. The transactional question is operational: whether the buyer can continue to use, refresh, and scale the dataset pipeline post-closing without violating legal or contractual restrictions. In physical AI systems, these questions intensify. Sensor and telemetry data may be central both to model improvement and to safety monitoring. Data governance therefore intersects with product liability risk, reinforcing the need for integrated legal and technical diligence. Looking ahead, the EU AI Act’s phased implementation through 2026 and 2027 will impose new compliance obligations on high-risk AI systems, many of which are developed by Israeli companies for European markets. Practitioners should expect AI diligence to become as routine and rigorous as cybersecurity or environmental review. AI Risk as an Allocation Problem AI transactions consistently present a core allocation question: which party bears the risk that the AI asset is not legally durable, not operationally maintainable, or not safely deployable at scale? From a seller’s perspective, documentation readiness becomes critical. AI businesses often evolve iteratively, with datasets, tools, and workflows changing rapidly. Yet acquisition agreements increasingly require detailed disclosure of AI tools, products, and training datasets. Sellers must therefore treat AI exit readiness as governance readiness, ensuring that tool terms are reviewed, internal policies are adopted, and datasets are mapped in advance of a transaction. Sellers also face pressure to provide R&Ws regarding ownership of models and outputs, and to confirm, inter alia, that AI tool providers retain no residual rights. These commitments depend not on abstract doctrine but on the granular terms of platform licenses and the structure of training workflows. Buyers, for their part, increasingly treat dataset usability as the new analogue to IP ownership. A model trained on data that cannot lawfully be refreshed or integrated into the buyer’s global R&D environment may degrade in value. Third-party AI tools and hosted model providers can also become hidden operational dependencies, affecting integration and scalability. In physical AI transactions, buyers frequently extend this analysis into safety governance – the objective is not only to confirm performance at signing, but to understand the durability of that performance post-closing. Contractual Evolution: AI Representations in Practice Transaction documentation increasingly reflects these diligence themes. Two categories of AI R&Ws have become particularly salient in Israeli M&A practice: AI “For transactional counsel, the key insight is that governance and value are no longer separable.” ISRAEL — MERGERS & ACQUISITIONS
RkJQdWJsaXNoZXIy MjgzNzA=