Define the workflow
Agree on the agent, tools, data boundaries and actions inside the assessment scope.
AI Red Teaming
Find the weaknesses.
Before they find production.
Assess how an agent could be redirected, misuse a tool or cross an authorization boundary. Turn the findings into a focused plan for reducing risk.
The practical view
What is AI red teaming?
AI red teaming tests an AI application with adversarial scenarios to uncover behavior that ordinary functional tests can miss. For agents, that means examining tool use, data access and downstream actions as well as model responses.
From question to control
A focused path forward.
Test the boundaries
Generate threat scenarios informed by business requirements and application context.
Turn findings into controls
Review evidence and use findings to shape application policies, guardrails and remediation.
Assessment scope and authorization are agreed before testing. Results describe the evaluated workflow; they do not establish that an application is free of risk.
A few useful answers
Before you
take the next step.
How is AI red teaming different from ordinary testing?
Functional testing asks whether the application does what it should. Red teaming explores how it might do something it should not, including when prompts or external content try to redirect it.
How can we evaluate this on our own workflow?
Start with a conversation about the agent, tools, data and controls in scope. A guided pilot can then focus on a defined use case and the evidence needed to evaluate it.
Make your next agent a considered decision.
Start with your
agent workflow.
Tell us what your agent does and where you need more control.