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Model Security Is the Wrong Frame – The Real Risk Is Workflow Security. As AI copilots and assistants become embedded in daily work, security teams are still focused on protecting the models themselves. But recent incidents suggest the bigger risk lies elsewhere: in the workflows that surround those models. Two Chrome extensions posing as AI helpers were recently caught stealing ChatGPT and DeepSeek chat data from over 900,000 users. Separately, researchers demonstrated how prompt injections hidden in code repositories could trick IBM's AI coding assistant into executing malware on a developer's machine. Neither attack broke the AI algorithms themselves. They exploited the context in which the AI operates. That's the pattern worth paying attention to. When AI systems are embedded in real business processes, summarizing documents, drafting emails, and pulling data from internal tools, securing the model alone isn't enough. The workflow itself becomes the target. AI Models Are Becoming Workflow Engines To understand why this matters, consider how AI is actually being used today: Businesses now rely on it to connect apps and automate tasks that used to be done by hand. An AI writing assistant might pull a confidential document from SharePoint and summarize it in an email draft. A sales chatbot might cross-reference internal CRM records to answer a customer question. Each of these scenarios blurs the boundaries between applications, creating new integration pathways on the fly. What makes this risky is how AI agents operate. They rely on probabilistic decision-making rather than hard-coded rules, generating output based on patterns and context. A carefully written input can nudge an AI to do something its designers never intended, and the AI will comply because it has no native concept of trust boundaries. This means the attack surface includes every input, output, and integration point the model touches. Hacking the model's code becomes unnecessary when an adversary can simply manipulate the context the model sees or the channels it uses. The incidents described earlier illustrate this: prompt injections hidden in repositories hijack AI behavior during routine tasks, while malicious extensions siphon data from AI conversations without ever touching the model. Why Traditional Security Controls Fall Short These workflow threats expose a blind spot in traditional security. Most legacy defenses were built for deterministic software, stable user roles, and clear perimeters. AI-driven workflows break all three assumptions. Securing AI-Driven Workflows So, a better approach to all of this would be to treat the whole workflow as the thing you're protecting, not just the model. How Platforms Like Reco Can Help In practice, doing all of this manually doesn't scale. That's why a new category of tools is emerging: dynamic SaaS security platforms. These platforms act as a real-time guardrail layer on top of AI-powered workflows, learning what normal behavior looks like and flagging anomalies when they occur. Reco is one leading example. As shown above, the platform gives security teams visibility into AI usage across the organization, surfacing which generative AI applications are in use and how they're connected. From there, you can enforce guardrails at the workflow level, catch risky behavior in real time, and maintain control without slowing down the business. Request a Demo: Get Started With Reco.