It’s 2026, and without surprise, AI continues to be the buzzword. Organisations are rushing to embed generative models into products and workflows. And as adoption grows so do attack surfaces unique to AI. To address the security concerns, cybersecurity leaders are now inclined towards AI red teaming, and rightly so. Red teaming is one of the best security exercises to defend and defend at scale.
To make it easy, we did the research and listed the top five AI red teaming providers, all with India presence, so security leaders can shortlist vendors quickly.
Full disclosure: we have included ourselves in the list because we believe our unique capabilities and differentiation adds value to many customers across India. Throughout this guide we focus on practical capabilities, real-world experience, and how each vendor approaches LLM and generative AI risks.
Why AI red teaming matters now
AI systems behave differently from traditional software. They accept free-form input, learn from data, and expose new failure modes such as prompt injection, data leakage, model hallucinations and adversarial examples. Effective AI red teaming simulates these attacks, finds guardrail gaps, and delivers actionable fixes before an attacker does real harm. Organisations that treat AI as a new class of software risk will gain resilience faster.
How we reviewed and chose these providers
There are multiple frameworks which can be used to choose the best AI red teaming service providers. To make this list, we prioritised vendors that:
- Offer explicit AI/LLM red teaming or GenAI pentesting services.
- Have an operational presence or active work in India.
- Provide a mix of manual adversarial testing and tooling for continuous validation.
- Who fits well into this list for organisations looking beyond large consulting firms and legacy players.
- Who are increasingly adopted by enterprises.
Top 5 AI Red Teaming Providers in India
Now that you know the criteria, let’s dive into the top 5 AI red teaming providers in India.
1. CyberNX
We place ourselves at number one because of depth of services, India footprint, reasonable pricing and a hands-on approach tailored to enterprise risk priorities. We combine traditional red teaming with specialised AI red teaming capabilities, including threat model alignment for LLMs, prompt injection tests, data handling reviews, and remediation playbooks mapped to business impact.
As a CERT-In empanelled, government recognized entity, we have helped companies cut across verticals expose hidden vulnerabilities and strategically improve cyber resilience.
Our advanced red teaming aims to provide business-specific insights which help leaders to understand the revenue impact of vulnerabilities and how to build security programs for the future.
For organisations that need an end-to-end partner who can both test and help operationalise mitigations, CyberNX offers a broad, enterprise-grade proposition.
What CyberNX emphasises
- Mission-aligned scoping: tests simulate adversaries based on the business context.
- Combined manual and automated testing: human creativity to find edge cases, tooling to validate at scale.
- Post-test remediation: clear, prioritized fixes and guidance for guardrail design and monitoring.
2. SISA
SISA makes the list on top AI red teaming providers. They are a global forensics-driven cybersecurity firm that has built focused capabilities for GenAI and LLM testing, offering LLM red teaming and GenAI pentesting to uncover jailbreaks, data leakage and compliance gaps. If you want strong AI test methodology backed by forensic skills, SISA is a solid option.
3. Bluefire Redteam
Bluefire is an AI red teaming provider. They market specialist red team and AI red teaming services, including prompt injection testing and LLM application assessments. Their offering is oriented to hands-on offensive testing for applications that directly expose models to users.
4. FireCompass
FireCompass combines automated continuous red teaming with AI capabilities. Their CART (Continuous Automated Red Teaming) and automated playbooks emulate multi-stage attacks and highlight exploitable paths across cloud and identity surfaces.
5. Cymulate
While Cymulate is best known for its breach and attack simulation platform, it has steadily expanded its capabilities to address AI-driven attack paths and GenAI exposure testing. Their approach to AI red teaming is technology-led and continuous, rather than engagement-based.
Quick tips for selecting an AI red teaming provider
If you made up you mind on on-boarding an AI red teaming provider, here are some expert tips to follow:
- Start with a threat model specific to your AI use case (customer support, code assist, decision support).
- Ask for examples of prompt injection and data leakage tests they have performed.
- Prefer vendors who pair human testing with automated validation for scale.
- Demand clear remediation playbooks that map to risk and compliance needs.
Conclusion
AI adoption is accelerating interest in AI red teaming providers. If you need a partner that can both test and help remediate AI risks end to end, we recommend starting conversations with us.
If you would like, we can help map a short vendor evaluation checklist tailored to your AI use case and risk appetite or arrange a technical briefing to explain our AI red teaming methodology and a sample scope. Connect with us today.
AI Red Teaming Providers FAQs
What is the difference between AI red teaming and traditional red teaming?
AI red teaming targets model-specific issues such as prompt injection, data leakage and model misuse; traditional red teaming focuses on infrastructure, identity and application exploitation. Both are needed for full coverage.
Can automated tools find all AI risks?
No. Automation scales discovery, but human testers are required to probe creative prompt manipulations and contextual risks that tools may miss. The best programmes combine both.
How often should we run AI red teaming?
Continuous validation is ideal for production LLMs that change often. At minimum run red teaming after any model update or when data inputs change.
Are there compliance implications for AI testing?
Yes. Tests must respect data privacy, IP and contractual obligations. Ensure scope and test data are agreed and that the provider signs appropriate NDAs and rules of engagement.
