From Macros to Agentic Workflows: The New Shadow AI Risk in Your Sanctioned Tools

For decades, security teams have managed Shadow IT: the apps and tools employees use without official approval. But today, a more subtle threat, shadow AI has emerged within the platforms you have already approved.

It started with “Automations.” Now, it has evolved into Agentic Workflows. These are autonomous systems built by business users — marketing managers, HR leads, and sales ops, who aren’t developers, but are effectively building complex software inside sanctioned AI platforms like Copilot Studio, Claude or OpenAI.

The Visibility Gap: Why Sanctioned Isn’t Enough

Most enterprises believe that by approving a platform, they’ve secured the usage. However, security teams currently have zero visibility into what is actually being built inside those platforms. This creates a massive shadow AI footprint where business logic and data permissions are defined entirely outside the view of IT.

We’ve moved from Good Old Macros to agents that can:

• Connect directly to live databases.
• Trigger emails and external communications.
• Move data between internal systems autonomously.

Because these workflows don’t go through a traditional coding process, they bypass your standard security reviews. There is no source code to scan, yet these workflows are handling your most sensitive data.

When a Productivity Tool Becomes a Data Breach

In a recent demonstration, we highlighted how easily a well-intentioned workflow can turn into a security disaster.

The Setup: A business user creates a simple, public-facing agent designed to provide product information to potential customers. It’s a standard productivity exercise: the agent is linked to a database to answer questions about inventory.

The Failure Point: The user makes two small configuration errors that no traditional security tool would catch:

• Over-Privilege: They select a configuration that allows the agent to access the entire database directly, rather than limiting it to the specific “Products” context.
• Lack of Scoping: They leave the table names open, assuming the AI will “know” to stay on the products.

The Catastrophe: Because of these invisible configuration gaps, a user can simply ask the agent for the “Employees” table. The agent, functioning exactly as it was configured, spills sensitive personnel data. Even worse, if the agent has an “email skill” enabled, a malicious actor can command it to send that stolen data to a personal email address.

This isn’t a hack in the traditional sense. It is a workflow doing exactly what it was told to do by a user who didn’t realize they were creating a back door.

The Shift from Static Scanning to Runtime Protection

You cannot secure what you cannot see. Since these agentic workflows are built at the prompt level and configured through simple interfaces, you can’t use traditional “shift-left” security methods.

To protect the business, security needs to move into Runtime Security.

1. Learning Behavioral Profiles

Instead of trying to predict every possible configuration error, security must be able to learn the behavior profile of an agent. If an agent is built to talk about products, any attempt to access an employee database should be recognized as a deviation from its profile and blocked automatically.

2. Enforcing Hard Guardrails

Security teams need the authority to set “set-and-forget” rules that override any individual user’s setup. For example: No information, regardless of its sensitivity, is allowed to be sent to a personal email address. Even if a business user creates a perfectly functional agent with email capabilities, this global rule acts as a safety net. It ensures that even a legitimate workflow cannot be manipulated to send corporate data, public or private, to an unmanaged personal account.

3. Intelligent Runtime Intervention

While global rules handle clear-cut policy violations, Runtime Protection is designed to address the gray areas of how an agent actually behaves in the wild. Even if an agent is built on a sanctioned platform, its day-to-day actions must stay within the boundaries of its intended purpose.

• Behavioral Profiling: Security teams need the ability to learn the specific profile of an agent. For example, if an agent is designed to provide information about public product catalogs, that becomes its established behavioral baseline.
• Context-Aware Detection: The system monitors the interaction between the user prompt and the internal data sources. If a user tries to steer the agent toward sensitive areas — such as an employee database or financial records, the system recognizes this as a deviation from the agent’s profile.
• Real-Time Prevention: Rather than just alerting security after the data has been accessed, Runtime Protection intervenes the moment the deviation occurs. It blocks the specific unauthorized action while allowing the agent’s legitimate functions to continue.

The result is a least-privilege approach that works in real-time. The business user gets the productivity they need, but the system ensures the agent cannot be manipulated into accessing data it was never meant to touch.

Security as an Enabler, Not a Blocker

The goal isn’t to stop business users from building workflows that make them more efficient. The goal is to provide the safety net that allows them to innovate without accidentally opening the vault.

In the era of agentic workflows, visibility is your first line of defense. If you don’t know what your business users are building, you can’t protect the data they are using.

Is your team building in the dark? Contact us to see how we provide visibility and runtime protection for the new era of shadow AI.