Overview
AI doesn’t just fail when it’s attacked. It can fail under everyday conditions.
At HCLTech, AI red teaming is more than testing for adversarial threats. Our teams simulate both intentional attacks and unintended outcomes that arise from normal use or edge cases such as prompt injections, data leakage, hallucinations, bias or toxic outputs.
By exploring how AI systems behave in realistic and high-stress scenarios, we help enterprises uncover vulnerabilities, hidden biases and reliability gaps before they affect customers, compliance or brand trust.
Our red teaming framework
We’ve built a repeatable, high-impact methodology that delivers results in as little as four weeks.
Phase 1
Scope and Sample
- Objective: Define the scope and model under test.
- Deliverable: Produce a sample report at no cost, offering initial insights with no obligation to continue.
Phase 2
Technique Selection
- Objective: Identify relevant testing tactics and real-world or edge-case scenarios tailored to your AI system.
- Deliverable: A customized test plan defining tools, tactics and evaluation metrics.
Phase 3
Execute
- Objective: Conduct targeted red teaming using advanced AI security tools and simulations.
- Deliverable: A detailed vulnerability assessment highlighting system weaknesses and behavioral risks.
Phase 4
Recommendation
- Objective: Analyze results and provide actionable mitigation strategies.
- Deliverable: A comprehensive final report with prioritized recommendations and a completion certificate.
Low-risk investment Timelines of engagement around 4-weeks per AI System.
Every vulnerability found before launch saves millions after deployment.
Our clients report:
- Reduced compliance costs through pre-emptive risk discovery.
- Faster time-to-market due to streamlined testing and governance.
- Enhanced customer trust with demonstrable AI safety and transparency.
In modern AI, governance isn’t a brake — it’s the engine.
Proactive red teaming transforms AI risk into a competitive advantage.
Talk to our experts