The question we're answering
“Can our AI application be manipulated, abused, or cause harmful actions?” Standard testing checks whether a system works as intended. Red teaming checks what happens when someone deliberately tries to make it do something it shouldn't, which is the scenario that actually shows up in an incident.
Who this is for
Organizations with AI systems in production, or close to it, who need evidence of resilience against deliberate abuse, not just functional test results.
What this covers
Red teaming exercises adversarial scenarios across the full system, including:
- Prompt injection and jailbreak attempts against production-realistic configurations
- Agent tool-permission abuse: pushing an agent toward actions outside its intended scope
- Data exfiltration attempts through the model or its integrations
- Evidence and reporting suitable for a board or regulator, not just an engineering ticket queue
Outcomes & deliverables
- A realistic picture of what a motivated adversary could actually achieve
- Prioritized findings ranked by business impact, not just technical severity
- Remediation guidance your team can act on directly
- A report suitable for board, customer or regulator review
How we approach it
Define target AI systems, rules of engagement, and success criteria.
Adversarial testing across realistic abuse and manipulation scenarios.
Prioritized findings, business impact, and remediation guidance.
Regulatory readiness
Governance work here feeds directly into regulatory evidence, not just internal policy.
Industries & use cases
Proof
50 active enterprise clients, 100+ SMB clients, and 1,000+ assessments delivered per year: this isn't our first engagement like yours.
Expert reviewer
