5 Top AI Red Teaming Companies for Enterprise Security Teams

AI is transforming how enterprises build software, automate workflows, and interact with data. But every AI-powered chatbot, copilot, RAG application, and autonomous agent also introduces new security risks that traditional penetration testing was never designed to detect. 

Attack techniques such as prompt injection, jailbreaks, data leakage, and unauthorized tool use have created an entirely new attack surface.

AI red teaming helps organizations uncover these weaknesses before attackers do. By simulating real-world adversarial attacks, it evaluates how AI systems behave under malicious inputs and whether they can be manipulated into exposing sensitive data, bypassing safeguards, or performing unintended actions.

In this article, we'll explain how to evaluate AI red teaming companies, compare the leading providers for enterprise security teams, explore their key capabilities, and discuss what a comprehensive AI red teaming report should include.

How Enterprise Teams Should Evaluate AI Red Teaming Companies

Not every AI red teaming company offers the same type of value. Some provide automated testing platforms. Some provide expert-led assessments. Some focus on model safety. Some focus on LLM application security. 

Some are stronger for agents and RAG. Others are better for compliance evidence or AI governance. Enterprise buyers should evaluate vendors across several areas. Here’s a list of them: 

1. AI-Specific Attack Coverage: A strong provider should test attack categories that are specific to AI systems. These include direct prompt injection, indirect prompt injection, jailbreaks, data exfiltration, tool misuse, unauthorized retrieval, system prompt extraction, policy bypass, sensitive data exposure, toxic or unsafe output, agent manipulation, and multi-turn attack chains.
2. Application and Agent Context: Testing should cover the actual AI application, not just the underlying model. Enterprise AI risk often appears in the interaction between the model, prompts, data sources, user permissions, tools, and business workflows.
3. Continuous Testing: AI systems change quickly. A strong vendor should support repeatable testing, regression testing, CI/CD integration, or continuous reassessment so security teams can retest when prompts, models, retrieval sources, or tools change.
4. Exploitability and Impact: Enterprise security teams do not need endless lists of theoretical model failures. They need to know which findings create real exposure. A useful vendor should explain business impact, severity, reproduction steps, affected workflows, and remediation priorities.
5. Remediation Guidance: AI red teaming should not stop at discovery. The provider should help teams understand how to reduce risk. That may include guardrail changes, prompt hardening, retrieval controls, permission changes, logging improvements, tool restrictions, human approval paths, or monitoring recommendations.
6. Evidence and Reporting: Enterprise teams need reports that satisfy security, engineering, risk, compliance, and leadership stakeholders. Strong reporting should show what was tested, how attacks were attempted, what succeeded, what failed, what the impact was, and what needs to change.
7. Human Expertise: Automation matters, but expert review still matters. AI systems can fail in subtle ways. Human red teamers can identify novel attack paths, assess context, and interpret risk in a way that automated testing alone may miss.

Top AI Red Teaming Companies for Enterprise Security Teams

Let’s explore some of the top AI red teaming companies: 

1. Novee

Novee is a leading AI red teaming company that helps enterprise security teams identify and validate risks across AI applications. Its platform specializes in AI penetration testing, prompt injection, jailbreaks, adversarial prompts, data exfiltration, and AI agent security. 

Unlike many AI security tools that highlight theoretical weaknesses, Novee focuses on vulnerabilities that are reproducible and have measurable business impact. It evaluates the complete AI application stack, including models, prompts, tools, data sources, permissions, and workflows, to uncover real-world attack paths. 

This makes it well suited for organizations deploying LLM applications, AI agents, RAG systems, coding assistants, and enterprise copilots that require continuous security validation as AI environments evolve.

Key Capabilities

• AI penetration testing
• AI red teaming for LLM applications
• Prompt injection testing
• Jailbreak testing
• Adversarial prompt generation
• Data exfiltration testing
• AI agent workflow manipulation testing
• Continuous attacker-level validation
• Exploitability-focused reporting
• Remediation guidance for enterprise teams

2. Enkrypt AI

Enkrypt AI is a leading AI red teaming company for enterprises that need to secure AI agents, multimodal systems, RAG applications, tool ecosystems, and MCP-connected workflows. 

Its Agent Red Teaming solution continuously tests AI systems across text, images, audio, agents, tools, and retrieval pipelines, delivering prioritized remediation guidance and evidence-ready reports. Unlike solutions focused only on chatbot security, Enkrypt AI evaluates complex AI workflows where multiple data sources, inputs, and tool calls interact. 

Its broad coverage of agents, RAG, multimodal AI, and enterprise integrations makes it a strong choice for organizations looking to identify real-world AI risks and strengthen the security of production AI applications.

Key Capabilities

• Prioritized remediation guidance
• Evidence-ready reporting
• Continuous testing workflows
• Enterprise AI security support
• Testing for real AI system failure modes

3. Prompt Security

Prompt Security is a strong option for enterprises that want AI red teaming connected to runtime protection, discovery, and remediation. Its AI red teaming solution is positioned around identifying risks such as prompt injection and data exposure, while its broader platform focuses on securing generative AI use across enterprise environments.

Prompt Security is especially relevant for companies that have a growing number of AI tools but limited centralized visibility. Employees may use public GenAI tools, internal AI assistants, third-party copilots, and custom AI applications. 

Security teams need to know where AI is being used, what data is involved, and which systems create risk. Red teaming becomes more valuable when it is connected to this broader AI security visibility.

Key Capabilities

• Remediation workflows
• Runtime protection support
• Enterprise AI usage visibility
• Policy enforcement
• GenAI security controls
• AI risk reduction workflows

4. Mindgard

Mindgard is a strong AI red teaming and AI security testing company for enterprises that want a combination of automated testing, offensive security expertise, and AI research depth. The company positions itself around identifying exploitable vulnerabilities in AI models, agents, and applications before attackers do.

Mindgard is especially relevant for enterprises that need testing across both AI systems and the processes around them. AI red teaming is not only a technical scan. 

It often requires understanding how the model is used, how users interact with it, what data it can access, which tools it can call, what guardrails are in place, and how business impact should be measured. Mindgard’s combination of automated red teaming and expert-led services can help with this more nuanced assessment.

Key Capabilities

• Adversarial testing
• AI vulnerability assessment
• Defense validation
• Stakeholder reporting
• Compliance support

5. Haize Labs

Haize Labs is a smaller AI red teaming company focused on adversarial testing for AI systems. It is positioned around helping enterprises move AI initiatives from proof of concept into production by proactively discovering weaknesses through red teaming and adversarial evaluation.

Haize Labs is especially relevant for organizations that are still developing their AI security operating model. Many companies have AI pilots, prototypes, and internal tools that are moving quickly toward production. 

Security teams may not yet have a mature AI testing framework, but they still need to identify risks before deployment. Haize Labs can help fill that gap with targeted red teaming and adversarial testing.

Key Capabilities

• Failure mode discovery
• AI application risk review
• Enterprise AI deployment support
• Red teaming for production readiness
• Focused AI security expertise

What a Strong AI Red Teaming Report Should Include

A useful enterprise report should not be a list of prompts that worked. It should explain risk in a way that security and engineering teams can act on. A strong report should include:

• Executive summary
• Tested systems and scope
• Attack methodology
• Attack categories covered
• Successful and failed attack paths
• Reproduction steps
• Evidence of impact
• Severity and likelihood
• Business risk explanation
• Affected data, tools, or workflows
• Remediation guidance
• Recommended guardrail changes
• Retesting plan
• Mapping to AI security frameworks
• Open questions and residual risk

The best reports help teams make decisions. They should answer whether the system is ready for production, what must be fixed first, what can be accepted as residual risk, and what should be monitored after deployment.

The right AI red teaming company depends on the systems being tested, the maturity of the AI security program, and the type of evidence the enterprise needs. The most important goal is not to prove that AI can fail. It is to identify the failure modes that matter, fix them before deployment, and keep validating as the AI environment changes.

Final Thoughts

As AI adoption continues to grow, AI red teaming has become an essential part of enterprise security. It helps organizations identify vulnerabilities that traditional security testing often misses, from prompt injection and jailbreaks to AI agent manipulation and data leakage. 

The right provider should deliver more than vulnerability reports by offering actionable remediation guidance, continuous testing, and validation across real-world AI applications. 

By choosing an AI red teaming company that aligns with your security goals and AI environment, you can reduce risk, strengthen trust in AI systems, and deploy generative AI with greater confidence.