How embedded AI features and third-party integrations are exposing sensitive data
SaaS platforms have become indispensable to modern business. From collaboration to CRM, from project management to cloud storage, teams rely on Software-as-a-Service to move faster, stay connected, and remain productive across distributed environments. But beneath the convenience lies a growing set of risks, many of which are unseen, unmanaged, and underestimated.
The rapid evolution of SaaS has brought with it a wave of AI-powered features embedded directly into the tools employees use every day. Many of these integrations offer clear value: AI-powered writing suggestions, predictive analytics, automated scheduling, smart search, and chatbot support. But they also introduce data flow complexity that traditional security frameworks were never designed to handle.
The real problem isn’t that SaaS tools are inherently insecure. It’s that AI is increasingly built-in by default, often without the knowledge or approval of IT, security, or compliance teams. For example, a project management platform may now summarize meeting notes using a third-party LLM API. A customer service app may route queries through a machine learning engine hosted on a separate cloud provider. Meanwhile, employees may not know what data is being collected, where it’s going, or how it’s stored.
This is the rise of Shadow AI, not because the tools themselves are unknown, but because the AI components inside them are undocumented, unmonitored, and uncontrolled. Unlike Shadow IT, where unsanctioned apps are installed without permission, Shadow AI hides in plain sight, packaged as enhancements within “trusted” platforms.
Compounding the risk is the increasing use of third-party API integrations. SaaS vendors often rely on external services for core functionality, which means your data could be processed, cached, or shared with entities you’ve never vetted. In regulated environments, such as government, defense, healthcare, or finance, this kind of data exposure can quickly become a compliance failure, or worse, a breach.
For cybersecurity teams, this creates a major challenge. Traditional security monitoring tools aren’t equipped to track AI behaviors or API-level data interactions. And since many SaaS platforms are managed outside the core infrastructure, organizations struggle to apply the same controls they would to on-premise or internally developed systems.
So what’s the solution?
First, organizations must treat embedded AI as part of their threat surface. That means understanding what AI features exist in your SaaS stack, what third-party services are involved, and how data flows between them. Second, vendor management needs to evolve beyond contract terms and SLAs, it should include AI model transparency, data usage policies, and encryption standards. Finally, organizations must adopt security by design, ensuring teams, from IT to procurement to legal, are aligned on assessing risk before tools are deployed.
At Aperio Global, we work with mission-critical organizations to map, evaluate, and secure their SaaS ecosystems, including AI-embedded features and third-party integrations. We help build visibility into where sensitive data is going, who is processing it, and whether those processes align with operational and compliance expectations.
The future of cybersecurity isn’t just about protecting infrastructure, it’s about understanding where your data lives, how it’s interpreted, and who (or what) is making decisions with it.
In a world of AI-enhanced convenience, visibility is the new perimeter. And trust must be earned at every layer.