This topic covers the intersection of AI infrastructure compliance and export risk: how organizations must govern model access, data flows, and vendor relationships to meet export controls, sanctions, and cross‑border data rules as of 2025‑12‑19. No external articles were provided; the overview synthesizes vendor descriptions and prevailing regulatory trends. Enterprise AI assistants (IBM watsonx Assistant, Microsoft 365 Copilot), no‑code agent platforms (Cimba.AI, Anakin.ai, Automaited), industry‑specific agents (Kay AI) and customer chatbots (Verifast) accelerate automation but expand the attack surface for export, privacy and supply‑chain controls. Key compliance exposures include uncontrolled model weights or API access across jurisdictions, transfer of regulated datasets, ingestion of sanctioned‑party data, and lack of auditable provenance for model tuning or dataset lineage. Practical mitigation strategies include strong contractual SLAs and export clauses, region‑locking or on‑prem/private‑cloud deployments, granular access controls and key management, rigorous data classification and anonymization before ingestion, provenance logging and model watermarking, and regular vendor due diligence (security certifications, attestations, SOC/ISO reports). Tools that emphasize auditable governance (Cimba.AI) or enterprise controls (watsonx, Microsoft 365 Copilot) can reduce risk when configured for tenancy isolation, DLP and logging. Platforms with large app marketplaces (Anakin.ai) or agent orchestration (Automaited) require added lifecycle controls and testing. For regulated sectors (insurance, e‑commerce), integrate sanctions screening, human‑in‑the‑loop review, and automated compliance checks into CI/CD and model governance pipelines. The practical goal is to align technical controls, vendor contracts and operational governance so AI agents deliver value without creating unmanaged export or regulatory exposure.