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.
