Privacy and Security in AI

AI tools have accelerated development, but they have also exposed enterprise data, business logic, and code assets to unprecedented risks. Mainstream AI development platforms (Replit AI, Lovable, Copilot, etc.) rely on cloud-based models, meaning user input, code snippets, and business processes flow into third-party systems. For startups, this is tantamount to outsourcing their future core competencies.

Data from multiple sources points to the same trend: enterprises are slowing down, rather than accelerating, their adoption of AI due to privacy concerns.

According to a 2025 survey by Bedrock Security, 82% of security teams reported poor visibility of sensitive data across multiple data stores. By 2027, over 40% of AI data breaches will originate from cross-border GenAI misuse. Data stored in hybrid environments such as multiple environments and multiple clouds is more likely to become a target for leakage. 40% of leakage incidents involve data stored in multiple environments, and these leakage incidents result in significantly higher average losses.On the other hand, AI application security tools are not yet widely adopted: only about 34% of organizations have begun using or implementing AI application security tools.In a Microsoft report,43% of companies are focused on preventing sensitive data from being uploaded into AI apps.

These data reveal a crucial fact: the speed advantage of AI is being offset by privacy anxieties.Existing tools, in pursuit of speed, almost entirely sacrifice privacy.

This exacerbates the core contradiction in AI development: the more powerful the tool, the more sensitive the input information. The more sensitive the information, the more extreme the privacy requirements. However, all current mainstream AI tools run in the cloud, are unverifiable, and do not truly belong to the user.

Therefore, enterprises face a dilemma: either accept the increased development speed or preserve their data assets and forgo the efficiency benefits of AI.This is precisely the fundamental problem that NativelyAI aims to solve: enabling teams to build applications and products at extremely high speeds without leaking data or uploading code.Other AI technologies cannot achieve this, but NativelyAI can guarantee privacy and security.