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. It 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 failure modes that conventional security testing was never designed to catch – prompt injection, data leakage, model hallucinations, adversarial manipulation and agent overreach.
The scale of the problem is growing rapidly. Global cybercrime costs reached $9.5 trillion in 2024 and are forecast to exceed $10.5 trillion through 2025, with AI-driven attack methods contributing directly to that trajectory. Real-world incidents make the risk worse: a financial services firm that deployed a customer-facing LLM without adversarial testing saw it leak internal content within weeks – triggering $3 million in remediation costs and regulatory scrutiny (VentureBeat, December 2025).
The OWASP Top 10 for LLM Applications (2025 edition) reflects how materially the threat landscape has shifted. Three new vulnerability categories were introduced: System Prompt Leakage, Vector and Embedding Weaknesses, and Misinformation. Sensitive Information Disclosure jumped from sixth to second position, driven by production incidents, not theoretical scenarios. Prompt Injection retained the top spot for the second consecutive year. These rankings are not academic – they reflect what is actually being exploited in deployed AI systems today.
The stakes are higher still for organizations deploying agentic AI. When an AI agent has access to enterprise tools – ticketing systems, cloud consoles, CRM, email – a single prompt injection or tool abuse scenario can cascade into unauthorized data access, financial fraud, or infrastructure compromise. Organizations that treat AI as a new and distinct class of software risk will be far better positioned than those applying traditional security assumptions to systems that don’t behave traditionally.
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 the depth and breadth of our AI security offering, our India footprint, and a hands-on approach tailored to enterprise risk priorities. Our AI red teaming goes well beyond prompt injection tests – we cover every layer of the modern AI stack: models, applications, RAG and knowledge systems, agent and tooling layers, and Model Context Protocol (MCP) integrations.
What we test
Our testing methodology covers:
- Prompt injection and indirect prompt injection: including hidden instructions embedded in documents, emails, tickets, and knowledge base articles that redirect model behaviour toward attacker intent
- RAG and vector database attacks: data poisoning, retrieval manipulation, malicious document instructions, and access control gaps across knowledge sources
- Agentic AI and tool abuse: over-privileged agents, unsafe function calls, SSRF chains, command execution, and allowlist failures across tooling layers
- MCP layer security: authorization design, tool permission boundaries, unintended data disclosure, and server hardening for organizations adopting Model Context Protocol
- Sensitive data leakage: from system prompts, API keys, embeddings, logs, connectors, and cached context
- Insecure output handling: model outputs flowing into code interpreters, shells, SQL, or webhooks without validation
Our testing is mapped to OWASP LLM Top 10 (2025) and MITRE ATLAS – ensuring findings connect to widely recognized frameworks your security and compliance teams can act on.
Regulatory alignment
For organizations operating in India, we align findings to the Digital Personal Data Protection Act (DPDPA, 2023), MeitY advisory guidelines on AI/LLMs, and RBI requirements for financial sector AI deployments. For globally operating organizations, we map evidence to the EU AI Act, NIST AI RMF 1.0, and ISO/IEC 42001:2023 – producing audit-ready documentation your compliance teams can reuse directly.
As a CERT-In empanelled entity, our engagements follow government-recognized standards. We have helped enterprises across BFSI, healthcare, e-commerce, and government sectors identify and remediate AI-specific vulnerabilities before they reached production.
What CyberNX emphasises
- Mission-aligned scoping: test scenarios are built around your business context and the specific ways your AI is deployed – not generic LLM attack checklists
- Combined manual and automated testing: human creativity to find edge cases, automated fuzzing at scale (10,000+ prompt injection variants)
- Post-test remediation: prioritized fixes, guardrail design guidance, blue team detection rules, and retesting to confirm closure
For organizations that need a partner who can both test and help operationalize mitigations end to end, CyberNX offers a comprehensive, enterprise-grade AI security proposition.
2. SISA
SISA is an India-based cybersecurity firm that offers LLM red teaming and GenAI security testing services. Their AI security work covers adversarial testing of LLM-enabled systems, including jailbreak scenarios, data leakage checks, and compliance gap identification. Their background in forensics informs their approach to AI security assessment.
3. Bluefire Redteam
Bluefire Redteam is an India-present cybersecurity firm offering AI red teaming services alongside traditional red team capabilities. Their AI security work includes prompt injection testing and LLM application assessments, with a focus on applications that directly expose models to end users.
4. FireCompass
FireCompass offers automated continuous red teaming capabilities with AI components. Their platform emulates multi-stage attacks and highlights exploitable paths across cloud and identity surfaces through automated playbooks and ongoing attack simulation.
5. Cymulate
Cymulate is a breach and attack simulation platform that has expanded its capabilities to include AI-driven attack paths and GenAI exposure testing. Their approach to AI security is technology-led and continuous rather than engagement-based, suited to organizations seeking ongoing automated validation of their AI posture.
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.
- If you are deploying AI agents or MCP integrations, require explicit coverage of agentic attack paths. Agents with access to enterprise tools come with risks that go far beyond chatbot security. Verify that the provider can test tool abuse, over-privileged function calls, and MCP authorization boundaries specifically.
Conclusion
AI adoption is accelerating, and so is the attack surface that comes with it. Choosing the right AI red teaming provider means finding a partner who understands not just how to probe a chatbot, but how to test the full AI stack – models, applications, RAG systems, agents, and the tool integrations that connect AI to your most sensitive enterprise data.
If you need a partner that can test comprehensively and help you remediate AI risks end to end – with findings mapped to DPDPA, RBI, EU AI Act, and other frameworks relevant to your operations, we recommend starting a conversation with us.
We can help map a vendor evaluation checklist tailored to your AI use case and risk appetite or arrange a technical briefing to walk through our methodology and a sample scope. Connect with us today to check out our AI red teaming services.
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.
