AI workflow red teaming: are your decision controls keeping up?

TL;DR: AI workflows can be pushed toward unauthorized decisions by manipulated prompts, impersonation, or inferred sensitive context, and most teams have not tested those failure modes, according to Cyberhaven. The real gap is decision authority, because traditional red teaming assumes fixed infrastructure while agentic systems can reason, coordinate, and act without independent verification.

NHIMG editorial — based on content published by Cyberhaven: How to Build a Red Team Exercise for AI Workflows

By the numbers:

• 48% of cybersecurity professionals now rank agentic AI as the leading attack vector, ahead of deepfakes and traditional social engineering.
• 40% of enterprise applications will incorporate AI agents by the end of 2026, up from under five percent in 2025.

Questions worth separating out

Q: How should security teams red team AI workflows that can trigger business actions?

A: Start with the actions, not the model.

Q: Why do local admin rights create a governance problem for IAM and PAM teams?

A: Because local admin rights are durable privilege on the device, even when central identity controls look strong.

Q: What breaks when organisations do not test inference risk in AI systems?

A: The organisation may assume only explicit prompts or data are dangerous, while the system is quietly deriving sensitive context that can be reused for manipulation or bypass.

Practitioner guidance

• Define decision-critical AI workflows Inventory the workflows where AI outputs can trigger payments, approvals, disclosures, or access changes, then mark them as decision-critical and subject to independent verification.
• Scope service identities and tool permissions Review the service accounts, API keys, and delegated tokens used by AI workflows, and reduce them to the minimum permissions needed for each task.
• Test inference risk explicitly Add scenarios that ask the system to infer authority, thresholds, or behavioural patterns from normal data, then check whether those inferences can influence downstream actions.

What's in the full article

Cyberhaven's full blog covers the operational detail this post intentionally leaves for the source:

• A red team scenario structure for AI workflows that security and business teams can adapt.
• Specific examples of manipulation, impersonation, and inference risk in AI-driven decisions.
• How Cyberhaven's Data Lineage approach traces where AI inputs came from and where outputs were used.
• The ebook reference on securing AI systems across the enterprise for teams ready to move from testing to control design.

👉 Read Cyberhaven's guide to red teaming AI workflows for decision authority risk →

AI workflow red teaming: are your decision controls keeping up?

Explore further

View Full Forum →  |  NHI Foundation Course →