AI Due Diligence

Investment decisions involving AI-native companies or AI-dependent assets demand due diligence methodologies calibrated to the distinctive characteristics of algorithmic technologies. Traditional assessment frameworks—designed for conventional software businesses with deterministic code bases and clear IP boundaries—prove inadequate when evaluating enterprises whose value derives from trained models, proprietary datasets, and emergent capabilities. Our practice has developed comprehensive AI due diligence protocols that examine the full spectrum of legal, regulatory, and operational risks specific to artificial intelligence.

Intellectual property assessment in AI contexts extends well beyond patent and copyright inventories. The core value often resides in model weights—mathematical parameters learned through training processes—whose legal status remains unsettled across jurisdictions. We examine the provenance of training data, assessing whether data acquisition complied with applicable terms of service, scraping restrictions, and data protection requirements. Licensing chains for foundation models require particular scrutiny, as many open-source AI licenses impose conditions—including use restrictions and compliance obligations—that may materially impact commercial deployment strategies.

Regulatory compliance assessment has become central to AI due diligence. The EU AI Act's extraterritorial reach means that targets with European market exposure—whether direct or through customer deployments—face substantial compliance obligations. We evaluate the target's current compliance posture against applicable requirements, identifying gaps that may require post-acquisition investment or create ongoing liability exposure. Sectoral regulations from financial services, healthcare, and other domains add additional layers requiring systematic examination.

Data governance practices warrant intensive scrutiny in AI due diligence. Training data quality directly impacts model performance and commercial viability, while data acquisition methods may create latent legal exposure. We examine data processing agreements with suppliers, consent mechanisms for personal data, and compliance with sector-specific data protection requirements. The DPDPA's provisions on data principal rights—including data portability and erasure—may impose ongoing obligations that affect data asset valuations. Cross-border data transfer mechanisms require assessment against evolving regulatory frameworks.

Algorithmic risk assessment examines the technical characteristics of AI systems for legal and regulatory implications. Bias audits evaluate whether systems produce discriminatory outcomes across protected categories—exposure that creates litigation risk and regulatory vulnerability. Explainability documentation assesses whether model decisions can be adequately explained to end users and regulators as required under emerging transparency mandates. Incident histories reveal patterns of system failures, user complaints, or regulatory interactions that may indicate underlying technical or governance weaknesses.

Commercial sustainability of AI assets depends on factors beyond immediate technical capabilities. Model maintenance requirements—including ongoing data acquisition, retraining cycles, and infrastructure costs—impact unit economics and margin profiles. Competitive moat analysis examines whether technological advantages are defensible against rapidly evolving capabilities from well-resourced competitors. Key person dependencies in AI teams create succession risks that may affect asset value retention post-transaction.

Our due diligence deliverables provide investment committees and transaction teams with actionable intelligence. Executive summaries highlight material findings requiring immediate attention. Detailed workpapers document our review methodology and evidence base. Risk matrices quantify identified exposures with estimated remediation costs and timelines. Specific purchase agreement provisions address identified risks through representations, warranties, indemnities, and condition precedent structures tailored to the target's AI-specific risk profile.