As organizations increasingly embed AI into their products and operations, securing AI systems has become a top priority for SecOps. But securing AI is not like securing traditional software—AI is an ecosystem of models, data pipelines, code, APIs, and third-party integrations, all introducing new security and compliance risks.
If your AI applications run in the cloud, these risks become even more challenging. Cloud-hosted AI models dynamically interact with external datasets, APIs, and users, making them more susceptible to data poisoning, prompt injection, and adversarial attacks.
Traditional security testing isn’t enough to deal with AI's expanded and complex attack surface. That’s why AI red teaming—a practice that actively simulates adversarial attacks in real-world conditions—is emerging as a critical component in modern AI security strategies and a key contributor to the AI cybersecurity market growth.
With AI security regulations tightening and AI adoption skyrocketing, organizations must adopt AI red teaming to stay ahead of evolving threats and new opportunities.
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What is AI red teaming?
AI red teaming is a cybersecurity practice that simulates attacks on AI systems to identify vulnerabilities under real-world conditions.
Unlike standard safety benchmarks and controlled model testing, AI red teaming goes beyond evaluating model accuracy and fairness. It scrutinizes the full AI lifecycle and supply chain—from AI models and data pipelines to cloud-hosted AI services and user-AI interactions, ensuring that every component is resilient against potential adversaries.
By taking an adversarial stance, AI red teaming proactively uncovers hidden security weaknesses—whether introduced through model training, inference pipelines, or real-time user interactions. It goes beyond static model evaluations and ensures that AI systems remain resilient in dynamic, real-world conditions.
What testing is typically performed for AI red teaming?
Effective AI red teaming requires a comprehensive approach that spans both technical and operational aspects to cover the expanded attack surface of enterprise deployments. Key testing areas include:
Bias & fairness testing: Evaluates whether AI models produce discriminatory or biased outputs, including when under stress or adversarial pressure
Data privacy violations: Identifies risks of data leakage or unauthorized access, ensuring that sensitive information is safeguarded across the entire data pipeline
Human-AI interaction risks: Tests how AI systems respond to malicious or unexpected user inputs and usage, which is critical for detecting vulnerabilities like prompt injection
Adversarial ML defense: Assesses the ability of AI systems to withstand targeted adversarial attacks, such as prompt injection and data poisoning
Additional testing areas include performance under stress, integration vulnerability analysis, and scenario-specific threat modeling.
These tests must account for the ever-evolving nature of AI systems, where continuous retraining and model drift demand dynamic and adaptive security measures. Because AI is constantly evolving, organizations should also invest in robust measurement and mitigation strategies to stay ahead of AI security risks.
What is the aim of AI red teaming?
AI red teaming aims to protect users and businesses from the misuse of AI by highlighting (and fixing) flaws in AI systems so that AI systems are resilient and trustworthy. Key objectives of AI red teaming include:
Risk identification: Detecting and addressing AI vulnerabilities before attackers exploit them
Resilience building: Strengthening AI models and infrastructure against adversarial threats
Regulatory alignment: Meeting compliance requirements, including those from the EU AI Act and the US White House executive order on AI
Public trust: Ensuring AI is safe, reliable, and aligned with ethical standards
Integrating AI red teaming into a broader AI risk management strategy and AI governance framework is critical to achieving long-term and proactive security across your organization.
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How does red teaming for AI differ from traditional red teaming?
While both AI red teaming and traditional red teaming focus on identifying vulnerabilities before attackers can exploit them, they differ fundamentally in scope, methodologies, and objectives.
Traditional red teaming: Focused on infrastructure, networks, and applications
Traditional red teaming simulates real-world cyberattacks against an organization’s IT infrastructure, applications, and employees. The primary goal is to assess how well security defenses hold up against adversaries by targeting:
Network security: Exploiting misconfigurations, privilege escalation, lateral movement
Application security: Identifying web app vulnerabilities like SQL injection (SQLi), remote code execution (RCE), and XSS (cross-site scripting)
Social engineering: Manipulating employees into revealing credentials or clicking phishing links
Traditional red teaming is well-defined, following industry standards like MITRE ATT&CK, NIST 800-53, and OSSTMM. The vulnerabilities found often have clear-cut fixes (patching software, updating configurations, improving user awareness).
AI red teaming: Expanding the attack surface beyond traditional security
AI red teaming expands beyond traditional security concerns to account for the unique risks posed by AI systems. Instead of just securing the infrastructure where AI runs, it simulates adversarial attacks on the AI model itself, its data pipeline, APIs, and real-time interactions.
Key differences
Data-driven threats: Unlike traditional software vulnerabilities, AI threats originate from data manipulation, model poisoning, and prompt injection.
Evolving attack surface: AI models change dynamically as they re-train, requiring continuous security assessments.
Security & ethics overlap: AI vulnerabilities include bias, misinformation, hallucinations, and trustworthiness issues, which aren't typical concerns in traditional cybersecurity.
How AI red teaming differs from standard AI model testing
Most AI testing focuses on accuracy, bias detection, and responsible AI principles. AI red teaming, however, simulates real attack scenarios to uncover security gaps beyond performance benchmarks.
| AI Model Testing | AI Red Teaming |
|---|---|
| Evaluates model fairness, accuracy, explainability | Simulates real-world adversarial attacks |
| Uses controlled datasets & scenarios | Tests AI in live, unpredictable environments |
| Focuses on ML robustness | Assesses entire AI supply chain & infrastructure |
| Ensures responsible AI compliance | Validates security, privacy, and resilience |
By integrating AI red teaming into AI risk management and security governance, organizations can stay ahead of emerging threats, ensure compliance with evolving regulations (like the EU AI Act), and maintain public trust in AI-driven applications.
Common vulnerabilities and real-world use cases of AI red teaming
Despite AI’s complexity, real-world attacks are often surprisingly simple—exploiting misconfigurations, overlooked weaknesses, or poor AI security hygiene. Some of the most common AI attacks include:
Backdoor attacks: Hidden triggers inserted into AI systems can let attackers secretly manipulate outputs, creating avenues for unauthorized control.
Prompt injection: By crafting malicious inputs, attackers can subtly alter AI responses or even trigger unintended data leaks, much like slipping a Trojan horse into a trusted system.
Data poisoning: Injecting corrupt training data can slowly skew AI behavior, effectively teaching it to act in ways that favor an attacker’s agenda.
Integration weaknesses: Vulnerabilities in APIs and cloud connections can expose systems to exploitation, allowing attackers to bypass security measures and gain access to critical data.
These vulnerabilities aren’t just theoretical. Let’s explore real-world cases, uncovered by the Wiz Research team, that have brought these risks into sharp focus:
DeepSeek database leak: Integration flaws in the latest DeepSeek model led to the exposure of sensitive AI training data.
🔍This real-world example showcases how… new AI threats can emerge from overlooked API and model access misconfigurations.
SAP AI vulnerabilities: Misconfigurations in SAP’s AI systems created hidden backdoor risks, potentially allowing attackers to manipulate AI outputs.
🔍This real-world example showcases how…even well-established enterprise AI platforms can suffer from security blind spots.
NVIDIA AI vulnerability: Weaknesses in NVIDIA’s AI container toolkit enabled prompt injection attacks, exposing gaps in AI security at the infrastructure level.
🔍This real-world example showcases how…attackers can manipulate AI behavior through input-based attacks, impacting AI-driven decisions and outputs.
Hugging Face model risks: Data poisoning vulnerabilities in Hugging Face’s popular AI-as-a-service platforms allowed adversaries to introduce subtle, malicious alterations to the training data.
🔍This real-world example showcases how…even widely trusted AI services are susceptible to adversarial data manipulation, emphasizing the need for continuous security testing.
When it comes to AI security, even the simplest missteps can have profound consequences. Your organization needs continuous, proactive AI red teaming to catch and fix these issues before they escalate into full-blown security breaches.
Best practices for AI red teaming: A 5-step framework
To effectively red team AI systems, organizations need a scalable, repeatable, and continuously evolving security framework. AI models dynamically re-train and update, making static security measures ineffective. A well-structured AI red teaming process ensures AI remains resilient against adversarial attacks, bias exploits, and misconfigurations.
Step 1: Define the scope of AI red teaming
Before testing AI security, organizations must define:
What AI components need testing?
Model robustness, API integrations, cloud-based AI security, training data integrity
What are the attack scenarios?
Adversarial ML attacks (evasion, poisoning), API abuse, prompt injection, supply chain risks
What security & compliance requirements apply?
OWASP AI Security, NIST AI RMF, EU AI Act, SOC 2, GDPR
Step 2: Select and implement AI adversarial testing methods
AI red teaming goes beyond penetration testing—it requires adversarial ML techniques to simulate real-world AI threats.
Model-centric testing (AI robustness assessment)
Adversarial perturbation testing: Generates inputs to trick AI into misclassification
Model inversion & extraction: Attempts to reconstruct private training data from AI responses
Data pipeline security testing
Data poisoning simulations: Tests if injecting malicious training data skews AI behavior
Bias & fairness testing: Evaluates if adversaries can exploit AI model bias for manipulation
Human-AI interaction & API security
Prompt injection attacks: Tests if AI ignores safeguards via manipulated inputs
API abuse testing: Explores AI model’s API vulnerabilities (e.g., unrestricted data retrieval)
Step 3: Automate AI red teaming for scalability
Manually testing AI vulnerabilities across cloud-scale deployments is inefficient. Automation helps simulate large-scale adversarial attacks.
Use AI security & adversarial testing tools
garak: Open-source adversarial testing tool for LLM security
PyRIT (Python Risk Identification for Generative AI): Simulates evasion and model extraction attacks
Microsoft Counterfit: AI security testing for machine learning models
Adversarial Robustness Toolbox (ART): Simulates adversarial AI attacks and defenses
Step 4: Implement continuous AI risk monitoring & response
AI red teaming isn’t a one-time test—it must continuously evolve as AI models update and retrain.
Ongoing AI red teaming strategies
Establish AI threat intelligence sharing: Track evolving threats from MITRE ATLAS and the OWASP AI Top 10.
Adopt continuous AI security testing: Integrate adversarial testing in CI/CD pipelines.
Develop automated risk scoring for AI: Prioritize high-risk AI vulnerabilities for remediation.
Step 5: Align AI red teaming with governance and compliance
Beyond security, AI red teaming must support regulatory and ethical AI guidelines to ensure compliance.
Key AI security & compliance standards
NIST AI Risk Management Framework (AI RMF): AI security best practices
EU AI Act: Compliance requirements for high-risk AI applications
SOC 2, GDPR, CCPA: Protect AI-driven personal data
Integrate AI red teaming into enterprise risk management (ERM)
Report findings to AI governance teams: Align with ethics and responsible AI principles.
Cross-functional collaboration: Engage security, data science, and compliance teams in AI risk management.
How does Wiz enhance your AI security?
Wiz provides a comprehensive cloud security platform that extends its capabilities to secure AI infrastructure with its AI security posture management (AI-SPM).
Through its centralized AI security dashboard, Wiz AI-SPM offers you:
An AI bill of materials (AI BOM): A detailed map of your AI components and dependencies, offering clear visibility into your entire ecosystem
Misconfiguration detection: Automated identification of security gaps across AI pipelines and cloud services, helping you address vulnerabilities before they escalate
Attack path analysis: Visualization of potential routes that attackers could use to exploit AI security risks, enabling more informed risk management
By integrating these capabilities, Wiz AI-SPM not only implements AI security best practices but also streamlines continuous monitoring and automated risk management for your organization—ensuring robust AI governance.
What’s next?
AI red teaming is becoming a critical security function for organizations committed to safeguarding their AI adoption, especially as regulatory demands increase. Although the field continues to evolve, challenges such as complex attacks, contextual interoperability, and lack of standardization persist.
As automation and security tools advance, the human element—characterized by expertise, cultural competence, and emotional intelligence—remains indispensable.
A security platform like Wiz can help you stay ahead of AI security best practices by bootstrapping your defenses and ensuring continuous improvement. Ready to learn more? Visit the Wiz for AI webpage, or if you prefer a live demo, we would love to connect with you.
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