As AI copilots and assistants become deeply embedded in everyday business operations, security strategies have struggled to keep pace. Many organizations continue to focus primarily on protecting AI models themselves. However, recent security incidents highlight a more pressing concern: the workflows surrounding these models are now the primary attack surface.
In several high-profile cases, attackers never touched the underlying AI algorithms. Instead, they exploited how AI systems interact with users, data, and third-party tools—revealing a fundamental shift in where AI risk truly resides.
Recent Incidents Reveal a Broader Problem
In one case, malicious Chrome extensions masquerading as AI productivity tools were discovered harvesting chat data from ChatGPT and DeepSeek users, exposing information from more than 900,000 accounts. In another, researchers showed how prompt injections hidden inside public code repositories could manipulate IBM’s AI coding assistant into executing malware on a developer’s machine.
These attacks did not compromise the AI models themselves. Instead, they exploited the context and workflows in which the AI operated—demonstrating how attackers increasingly target integrations, inputs, and outputs rather than model internals.
AI Has Evolved Into a Workflow Engine
Modern AI systems no longer operate in isolation. They act as connective tissue across enterprise environments, linking applications and automating tasks that were previously manual.
For example:
- An AI assistant may retrieve sensitive files from SharePoint and summarize them in an email.
- A chatbot may query internal CRM data to respond to customer inquiries.
- A coding assistant may pull instructions from repositories and execute actions on a developer’s system.
These workflows blur traditional boundaries between applications, users, and data. Unlike conventional software, AI systems rely on probabilistic reasoning rather than strict rules. Carefully crafted inputs can influence AI behavior in unintended ways, and the model lacks an inherent understanding of trust boundaries.
As a result, every prompt, integration, and output channel becomes part of the attack surface.
Why Traditional Security Controls Don’t Work Well for AI
AI-driven workflows expose gaps in legacy security approaches, which were designed for predictable systems and well-defined roles.
Key challenges include:
- No clear distinction between trusted and untrusted input:
To an AI model, malicious instructions hidden in a document look no different from legitimate text.
- Normal-looking data access patterns:
AI systems often process large volumes of data as part of standard operations, making exfiltration difficult to detect using traditional monitoring.
- Context-dependent behavior:
Security rules are typically binary—allowed or blocked. AI outputs depend on context, making it hard to define policies such as “never summarize sensitive data externally.”
- Rapidly changing workflows:
AI integrations evolve constantly. Permissions, data sources, and capabilities can change faster than periodic security reviews can keep up.
These factors make it clear that protecting the model alone is no longer sufficient.
Shifting the Focus to Workflow Security
To address these risks, organizations need to secure the entire AI-driven workflow, not just the underlying model.
Key steps include:
- Gain visibility into AI usage:
Identify where AI tools are deployed, including official platforms and unsanctioned browser extensions. Many organizations underestimate the number of AI services operating across their environment.
- Apply guardrails outside the model:
Use middleware or policy layers to restrict actions, such as preventing an internal AI assistant from sending external emails or scanning outputs for sensitive data before sharing.
- Limit permissions aggressively:
Treat AI agents like any other service account. Scope OAuth tokens narrowly and monitor for unusual access patterns.
- Educate users and vet integrations:
Unreviewed extensions, copied prompts, and third-party plugins can introduce risk. Any tool interacting with AI inputs or outputs should be considered part of the security perimeter.
How Modern Platforms Address AI Workflow Risk
Manually managing these controls does not scale, especially in large enterprises. This has led to the rise of dynamic SaaS security platforms designed specifically for AI-enabled environments.
These platforms provide real-time visibility into generative AI usage, map how AI tools connect to enterprise data, and enforce behavioral guardrails at the workflow level. By learning what “normal” looks like, they can detect anomalies without disrupting productivity.
Platforms like Reco exemplify this new approach by helping security teams discover AI applications in use, monitor interactions, and maintain control over rapidly evolving AI workflows.
The Takeaway
As AI becomes a core component of business operations, the definition of security must evolve. The most significant risks no longer stem from flaws in AI models themselves, but from how those models are embedded into workflows that touch sensitive data and critical systems.
Organizations that continue to focus solely on model security risk missing the bigger picture. In 2026 and beyond, workflow security will determine whether AI accelerates productivity—or becomes a new vector for enterprise-scale breaches.
