Automated Red Teaming for AI
Find safety and security failure modes that traditional testing can’t.
AI Is Moving From Language To Action. Are You Ready?
See what recent attacks revealed and how to secure agents in 2026.
What Red Teaming Surfaces
Expose context-specific risk under real adversarial pressure.
- Application-Specific risks Surface vulnerabilities unique to your AI’s architecture, context, and real world usage patterns.
- Safety and compliance gaps Test the robustness of your AI against harmful outputs policy violations, and inappropriate content generation.
- Security weaknesses Test your AI’s defenses against prompt injection, jailbreaks, data leakage, and unauthorized actions.
- Regression and drift Catch when model updates, system changes, or capability additions introduce new risks.
Comprehensive Risk Testing for AI
Execute broad and targeted red team campaigns to systematically assess application risk across evolving models and prompts.
Broad Model & Application Coverage
Test across 400+ foundation models, custom model deployments, live applications, and agent end points.
Automated and Targeted Campaigns
Run comprehensive automated scans across security, safety, responsible AI risk categories, or launch focused adversarial campaigns.
Context-Specific Adversarial Inputs
Generate attack scenarios tailored to your architecture, prompts, controls, and operational context, not just generic prompt libraries.
Recurring Replay & Regression Testing
Re-run structured adversarial tests after model updates, prompt changes, or new capabilities to evaluate how risk shifts over time.
Continuously Updated Artificial Intelligence
Incorporate evolving attack techniques informed by ongoing adversarial research and real-word red teaming experience.
Scalable Across AI Portfolios
Execute testing across multiple models, applications, and agent architectures from a single platform.
How Teams Use AI Red Teaming
AI Red Teaming supports development, validation, and ongoing operations as AI systems evolve.
- Evaluate During Development – Run adversarial tests as prompts, guardrails, and model configurations change to assess risk early.
- Validate Before Production – Execute comprehensive scans and targeted red team campaigns against live applications and agents prior to release.
- Re-test as Systems Evolve – Schedule recurring adversarial testing after model updates, prompt changes, or new capabilities to detect regressions.
Explore AI Security Resources
AI Agent Security Enterprise Playbook
How to assess and secure AI agents in production.
Gartner on AI Application Security
How to secure Al applications with testing, runtime protection, and discovery.
FAQs
AI red teaming is the process of testing AI systems by simulating real-world attacks and misuse scenarios to identify security, safety, and reliability weaknesses before they can be exploited. Unlike traditional penetration testing, AI red teaming focuses on AI-specific risks such as prompt injection, jailbreaks, sensitive data leakage, harmful outputs, and unsafe tool use.
AI applications introduce new security risks that traditional testing methods often miss. AI red teaming helps organizations identify vulnerabilities before deployment, validate AI safety controls, reduce business and compliance risk, and build confidence that AI systems behave securely under adversarial conditions.
AI red teaming uncovers a wide range of AI-specific vulnerabilities, including prompt injection, jailbreaks, sensitive data leakage, harmful or policy-violating outputs, insecure tool or function calling, agent workflow abuse, and business logic flaws unique to your AI application. It can also identify regressions introduced by model, prompt, or configuration changes.
Traditional penetration testing focuses on identifying vulnerabilities in networks, infrastructure, APIs, and applications. AI red teaming evaluates how AI models, applications, and agents respond to adversarial inputs and misuse attempts, uncovering AI-specific risks such as prompt injection, jailbreaks, unsafe outputs, and data exposure that conventional penetration tests are not designed to detect.
AI red teaming should be performed throughout the AI development lifecycle, including during development, before production deployment, and continuously after releases. Regular testing helps identify new vulnerabilities introduced by model updates, prompt changes, new agent capabilities, or evolving attack techniques, ensuring AI systems remain secure over time.
