AI agents now retrieve data, generate recommendations, and trigger actions across enterprise systems with little human review in between. That speed is the point, and it is also the problem. A single manipulated prompt or a poisoned data source can push an AI system toward a decision no one signed off on, and most security teams have never tested for it. Building a red team exercise for AI workflows is how you find that gap before an attacker does.
What Is a Red Team Exercise for AI Workflows?
A red team exercise for AI workflows refers to a controlled simulation that tests whether an organization's AI systems, and the people around them, can detect and stop manipulated or inferred content before it influences a decision within an AI system. Unlike more traditional penetration testing (pentests), which target infrastructure and code, this exercise targets decision authority. It asks, can AI-generated or manipulated content trigger a payment, bypass an approval, or expose sensitive information without independent verification?
Why Traditional Red Teaming Falls Short Against Agentic AI
Traditional red team exercises assume a fixed attack surface or networks, applications, and credentials. Agentic AI breaks that assumption because the systems being tested generate their own outputs, coordinate with other agents, and can be influenced without anyone breaching a perimeter.
A 2026 Dark Reading poll found that 48% of cybersecurity professionals now rank agentic AI as the leading attack vector, ahead of deepfakes and traditional social engineering. Gartner projects that 40% of enterprise applications will incorporate AI agents by the end of 2026, up from under five percent in 2025. Testing methodology has not kept pace with that rapid shift, and most organizations are still applying infrastructure-era assumptions to systems that reason, infer, and act, putting them on the backfoot when it comes to agentic security.
What to Simulate: Core Red Team Scenarios for AI Workflows
Start with scenarios that mirror how bad actors already misuse AI-generated content. Common starting points include:
- Using AI-generated or manipulated content to influence a financial transaction or payment approval
- Impersonating a trusted executive, employee, or partner through AI-assisted communication
- Introducing false urgency or authority to bypass standard approval controls
- Testing whether decisions are independently validated before an action executes
Each scenario should end with a clear question: did the organization verify the source and legitimacy of the request, or did it act on content that looked credible enough not to question?
Extending the Scenario to Test Inference Risk
The most common gap in early-stage AI red team exercises is stopping at manipulation and never testing inference risk, or the risk that a system exposes or acts on sensitive conclusions it derived rather than data it was directly given. Extend the exercise by prompting the system to infer information such as approval authority, financial thresholds, or behavioral patterns from otherwise nonsensitive data, then assess whether that inferred insight could be used to strengthen a manipulation attempt or bypass a control.
This step matters because inferred outputs often carry no classification label and no clear owner, which means they can slip past controls built for identifiable, static data.
What Success and Failure Look Like
A successful exercise confirms that the organization:
- Verifies the identity of participants in a decision
- Validates the source of a request before acting
- Requires independent confirmation for high-impact actions
- Escalates unexpected or time-sensitive directives rather than executing them
Failure looks different and is easy to miss during the exercise itself. Watch for an AI system that infers approval authority and uses it to generate a convincing request, sensitive attributes exposed without restriction, AI-generated content accepted as legitimate without verification, or a decision that cannot be traced back to a confirmed, validated input. If responsibility for an outcome cannot be explained after the fact, the control failed even if the immediate action seemed harmless.
Who Should Be in the Room
AI red team exercises fail when they stay inside the security team. Finance, operations, legal, and business leadership all make decisions the exercise is designed to test, and each function sees different failure modes. A finance leader will catch a payment approval gap that a security architect might miss, and legal will flag an export control or data handling issue that never registers as a "technical" finding. Treat participation across functions as a requirement, not a nice to have.
How Cyberhaven Supports AI Red Team Findings
Red team exercises surface where decision authority breaks down, but closing that gap requires visibility into how data actually moves and where AI-generated outputs originate. Cyberhaven's AI Security capabilities apply Data Lineage to trace how information entered an AI interaction, how it was transformed, and where the resulting output was stored, shared, or acted on. That traceability is what turns a red team finding into an enforceable control: instead of only knowing that a manipulated prompt could have triggered an unauthorized action, teams can see which data sources fed the interaction and apply controls at the point of use, before the next attempt succeeds.
Further explore how to secure your AI systems across the enterprise with our ebook published by O’Reilly, “Securing AI Systems: An Enterprise Defense Framework.”
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