AI Is the Missing Link in Securing the SaaS Sprawl

In today’s enterprise world, SaaS is everywhere. According to recent research, the average organization uses a staggering 112 different SaaS applications. Each platform brings its own unique set of configurations, permissions, and potential misconfigurations—making SaaS one of the most complex and overlooked layers in the cybersecurity stack. For security teams, manually monitoring these systems is like trying to solve a Rubik’s Cube with a blindfold on. And while traditional security tools have served organizations well in the past, they’re no match for this sprawling, dynamic attack surface.

That’s where AI comes in.

“Traditional security approaches normally rely on pre-defined rules or signatures, like a more simplistic form of discovering known behaviors,” says Melissa Ruzzi, Director of AI at AppOmni. “With attacks growing in volume and complexity, this is simply not enough anymore. It’s becoming practically impossible to pre-define all possible combinations and possible behaviors of an attack.”

Ruzzi’s perspective highlights a critical gap in legacy security solutions: they are static in a world that demands adaptability. SaaS platforms are not only multiplying, they’re constantly evolving—with new features, integrations, and user behaviors introduced weekly. This flux generates mountains of data, and buried in that noise may be the subtle indicators of a misconfiguration or breach.

“Dealing with this high volume and complexity of data is what AI thrives in,” Ruzzi explains. “AI can uncover unknowns, meaning deviations from baselines and nuanced correlations, and also adapt better to the constantly changing threat landscape.”

But not all AI is created equal. For organizations looking to deploy AI in their SaaS security stack, the effectiveness of the solution hinges on two key factors: domain expertise and data quality.

“The key capabilities are grounded in how the platform handles data analytics,” says Ruzzi.

“Proper AI application comes only with domain expertise, due to the vastness of algorithms and different approaches to develop AI, and from the richness of the data available. Having AI but not having the domain expertise nor the necessary data depth will not deliver the expected benefits.”

In practical terms, this means organizations should look for AI-powered platforms that don’t just run generic machine learning algorithms, but are purpose-built for SaaS security. That includes understanding the unique challenges of misconfigurations, overly permissive user roles, and third-party integrations that may be exposing sensitive data.

AppOmni, where Ruzzi leads AI efforts, has integrated generative AI capabilities directly into its platform—not just for detection, but also for user experience. “Some specific examples of capabilities are: A GenAI chatbot that can answer questions about the product, your data and SaaS security,” she says. “Ideally, this chatbot will also be running AI analytics for answers related to data so it can provide correlation among security observations to find potential threats that otherwise may have been missed.”

That kind of intelligent interface can significantly reduce response times for security teams, surfacing the “why” behind an alert and providing narrative summaries that drive quicker action.

In a field where seconds matter and context is everything, these capabilities aren’t just nice to have—they’re essential.

“SaaS security is a specific complex topic in cybersecurity that really benefits from AI,” Ruzzi emphasizes. “Proper coverage comes not only from threat detection, but, most importantly, from identifying security risks due to potential misconfigurations, which can be very complex in nature.”

As the enterprise stack continues to shift toward the cloud and SaaS-first strategies, AI isn’t just a useful tool for cybersecurity—it’s the only one capable of keeping up.