16-07-2025
Managing Risk As SaaS And AI Become One Connected Challenge
As AI and SaaS merge in the enterprise, security leaders face a new, rapidly evolving landscape ... More where visibility and control matter more than ever.
For years, software-as-a-service has shaped business transformation. Critical data and workflows now run through hundreds of cloud apps. But just as organizations have finally adjusted to that reality, a new wave is gathering momentum: AI-powered agents, copilots and automated workflows. These systems operate at machine speed, connecting SaaS environments in ways that simply weren't possible before.
This collision of SaaS and AI is bringing a fresh set of security challenges—challenges that can't be solved by legacy tools or a siloed mindset. The productivity upside is clear, but so is the speed at which the risk landscape is evolving.
The Hidden Risks of Converging SaaS and AI
In the early days, SaaS security was mostly about managing user access and monitoring behavior. Now, AI agents are embedded within business applications—handling sensitive information, making decisions and triggering actions. In many organizations, these AI tools hold the same permissions as human users and in some cases, even broader access.
Analyst projections reinforce how quickly this is happening. By 2028, Gartner expects 33% of enterprise software applications to include agentic AI—up from less than 1% in 2024. These AI agents are already initiating transactions, transferring data between systems and spinning up integrations at machine speed. The result is a collection of new, often invisible, risks:
This new attack surface is broad and dynamic. A breach might not come from an obvious point of failure, but from a shadow integration, an over-permissioned API key, or an AI agent quietly transferring data.
Why Old Approaches Fall Short
Security teams typically manage SaaS and AI risks in isolation, with dedicated tools and policies for each. But, incidents are not always caused by one poorly configured chatbot or a single risky app. More often, they involve a mix of shadow integrations, excessive permissions and undetected data flows—problems that are hard to spot and even harder to manage with siloed solutions.
Point solutions can help, but they rarely provide the context needed to see the full picture. The rise of AI automation blurs the line between human and machine actions. Without unified oversight, organizations are left with gaps in visibility, unsure of where their data is or who—or what—can access it.
Unified SaaS and AI Security Platform
Security leaders generally want a big-picture view—a live map of users, apps, integrations and AI agents. Real-time alerts for abnormal activity, context for investigations and the ability to respond in minutes are must-haves, not nice-to-haves. And all this needs to happen without putting the brakes on innovation.
Unified security solutions are stepping in, promising context-rich visibility and control across both SaaS and AI ecosystems. They aim to continuously discover sanctioned and unsanctioned AI usage, map sensitive data flows and separate routine activity from risky behavior—regardless of whether the actor is human or machine.
Against this backdrop, Vorlon has launched a platform positioned as the first to unify security oversight for SaaS and AI-powered systems. The solution continuously discovers both sanctioned and shadow AI usage, maps data flows between SaaS apps and AI agents and provides real-time, explainable alerts for unusual activity.
Amir Khayat, Vorlon's co-founder and CEO, believes this convergence fundamentally changes the risk equation. 'Agentic AI is software that's designed to pursue goals and make decisions on its own. The key difference? It's officially been given the green light by the organization to act on its behalf, making choices and taking action without needing a human in the loop every time. From a cybersecurity standpoint, that's a nightmare waiting to happen. If compromised, this AI doesn't just leak data, it acts with your full organizational permissions, turning every decision it makes into a potential breach at machine speed. That's why AI oversight is so critical.'
Vorlon's approach reflects a broader shift in the security industry: away from point tools and toward unified platforms that offer context and control across the expanding digital enterprise.
Securing the Next Chapter of Innovation
AI is now part of the fabric of business, not just an add-on—and the line between SaaS and AI is vanishing. Security's next frontier is not about picking the right tool for each risk, but about building trust and control across the whole, connected ecosystem.
Enterprises that unify their oversight—connecting SaaS, AI, users and data—will be best positioned to innovate confidently and meet regulatory demands as digital risks accelerate. The question is not whether to adapt, but how quickly organizations can move from fragmented tools to a coherent, unified strategy.
