AI Red Teaming

Overview

At HCLTech, our expert teams simulate both intentional attacks and unintended outcomes that can emerge under everyday use or edge-case conditions such as prompt injections, data leakage, hallucinations, bias or toxic outputs.

By exploring how AI models, platforms and systems behave in realistic and high-stress scenarios, we help enterprises uncover vulnerabilities, hidden biases and reliability gaps before they impact customer experience, compliance posture or brand trust.

4-Overview

What is AI Red Teaming?

AI systems don’t fail loudly; they fail subtly.

Traditional red teaming focuses on system security, while AI red teaming focuses on model safety, behavior, and misuse resilience. Both simulate adversaries, but they target different vulnerabilities, use different methods, and protect against different kinds of harm.

Through structured simulations, we expose weaknesses before they lead to real-world issues such as model hallucinations or security vulnerabilities.

Outcome: AI that’s safer, more resilient and aligned with Responsible AI principles by design.

Our Express Red Teaming Offering

We’ve built a repeatable, high-impact methodology that can deliver results in as little as four weeks, depending on the complexity and scope of the AI system.

Low-risk investment: Flexible engagement timelines designed to fit client needs.

Phase 1

Scope and Sample

  • Objective: Define the scope and model under test.
  • Deliverable: Produce a sample insights report, providing an initial view of findings and opportunities for further engagement.

Phase 2

Technique Selection

  • Objective: Identify relevant testing tactics and real-world or edge-case scenarios tailored to the 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.

Key deliverables

We provide a detailed report that includes findings, mitigation strategies and a verified completion certificate.

It will cover a comprehensive set of vectors and tests, along with validation aligned to the tenets of Responsible AI.

A data-driven roadmap to help enhance AI systems and boost confidence among stakeholders across various industries.

Real-World Use Cases

Disclaimer

The examples and outcomes presented on this page are illustrative and based on representative client scenarios. Actual results may vary depending on engagement scope, system complexity and risk environment.

Fortifying AI for a Global Pharmaceutical Leader

To secure LLM-powered applications, we implemented a custom framework based on the OWASP LLM Top 10, integrating prompt-injection testing, jailbreak defenses and leakage protection.

Result: Major reduction in attack surface, reusable internal testing modules and continuous AI assurance.

Red Teaming for a Technology and Transportation Pioneer

Our team executed adversarial prompt testing and identified significantly more vulnerabilities than standard testing methods, resulting in measurable operational improvements.

The ROI of AI Red Teaming

Every vulnerability found before launch can prevent significant downstream risks and financial impact after deployment.

Our clients report reduced compliance costs through pre-emptive risk discovery, faster time-to-market due to streamlined testing and governance and enhanced customer trust with demonstrable AI safety and transparency.

In modern AI, governance isn’t a brake. It is the engine. Proactive red teaming reduces AI risk and improves competitive advantage.

The ROI of AI red teaming

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