Top 7 Requirements for Effective AI Red Teaming

Enterprises across the globe are racing to deploy AI across every business workflow, but with accelerated adoption comes a completely new set of risks – one that conventional security tooling was never designed to mitigate. LLMs hallucinate, misinterpret intent, overgeneralize policies, and behave unpredictably under adversarial pressure. Today, most organizations deploying LLM-powered systems at scale have little visibility into how their models fail or where real vulnerabilities are emerging.

This is the reality customers now face: dozens of AI apps in production, hundreds more being developed, and virtually no scalable way to understand or mitigate the risks. This is where AI red teaming becomes essential – and where Zscaler differentiates itself from every available solution in the market.

The Hidden/Unknown Risks Behind LLM-Powered Systems

LLMs have introduced a range of vulnerabilities that cannot be uncovered through static code scanning or manual testing efforts. Organizations today struggle with: 

• Undiscovered exposure to prompt injection, jailbreaks, bias, and harmful outputs
• Hallucinations and trust failures that impact business decisions
• No repeatable process to validate behavior across scenarios
• Lack of on-domain testing coverage that reflects real user behavior
• Manual red teaming that takes weeks to complete and still lacks critical failure modes

As enterprises deploy AI globally and across different languages, modalities, and business units, the risks multiply. AI red teaming must be proactive, continuous, scalable – and deeply contextual.

Top 7 Requirements for Effective Enterprise AI Red Teaming

Early read teaming solutions have suffered from a number of limitations, including lack of depth, limited operational scale, and tools that fail to reflect real-world threats. Here are some key requirements to look for when building a modern, enterprise-grade AI red teaming solution:
 

1. Domain-Specific Testing with Predefined Scanners (Probes)

AI red teaming solutions should include a large number of predefined probes that test across major categories, such as security, safety, hallucination and trustworthiness, and business alignment. These should not be simply generic tests – but instead modeled after real enterprise scenarios and reflect how regular users, employees, and adversaries interact with AI systems.
 

2. Full Customizability for Comprehensive Testing Depth

Users should be able to provide structured context about their AI system and create fully customized probes:

• Create custom probes through natural language
• Upload custom datasets with predefined test cases (Bring your own dataset)
• Simulate business-specific attack paths

Basic red teaming solutions lack this close alignment with enterprise environments.  
 

3. A Large, Continuously Updated AI Attack Database

A robust AI attack database is critical to a successful red teaming solution. This includes continuously updating the database through:

• AI security research
• Real-world exploitation patterns

A comprehensive attack database ensures organizations can always test against the current AI threat landscape.
 

4. Scalability –  Simulate Thousands of Test Cases in Hours

A robust AI red teaming platform should be able to run thousands of on-domain test simulations in hours, not weeks. This makes enterprise-wide AI risk assessments across hundreds of different use cases achievable.
 

5. Multimodal and Multilingual Testing Coverage

AI red teaming solutions should test across:

• Text, voice, image, and document inputs
• Testing in more than 60 supported languages

Global deployments require global testing standards and multilingual support.
 

6. Modular Out-of-the-Box Integrations for any Enterprise AI Stack

Robust AI red teaming solutions should support a wide range of built-in connector types (REST API, LLM providers, cloud platforms, enterprise communication platforms). This enables seamless integration into any enterprise AI architecture.
 

7. AI Analysis with Instant Remediation Guidance

Identifying issues is only the start. AI red teaming solutions should also provide analysis that can explain extensive testing results in plain language, highlighting the most critical jailbreak patterns, and generating actionable remediation guidance.
 

Accelerate Your AI Initiatives with Zero Trust

AI red teaming isn't just about showing failures – it’s about understanding them, learning from them, and operationalizing AI protection at needed scale. With its recent acquisition of SPLX, Zscaler delivers a most complete, scalable, and deeply contextual platform, turning AI risk into something measurable, manageable, and most importantly - fixable. 

Learn more about Zscaler’s newest addition to its AI security portfolio, including the unveiling of exciting new capabilities in our exclusive launch: Accelerate Your AI Initiatives with Zero Trust

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