AI red teaming
As conversational AI drives customer interactions, understanding its unique, contextual risks has never been more critical.
Challenges
Conversational AI risks are built differently
Conversational AI introduces unique behavioral threats that put your data and applications at risk.
AI risks are harder to detect
Unlike clear cut security flaws, AI-generated responses can leak sensitive data, hallucinate, and be manipulated through prompt injection or misinformation attacks.
AI evolves faster than manual red teaming can handle
Manual red teaming is too slow and resource-intensive to secure AI systems effectively, yet deploying untested models exposes dangerous vulnerabilities.
Traditional protection leaves you vulnerable
AI conversational risks are context-dependent and constantly evolving, evading detection by traditional security tools and point solutions.
Opportunities
Test like a user to catch unexpected behavior
Discover vulnerabilities before an incident happens by automating simulated attacks specific to your conversational AI.
Test for AI risks specific to you
Proactively identify AI vulnerabilities. Simulate diverse attack vectors—like prompt injection, data poisoning, and social engineering—to detect domain-specific risks, novel exploits, and safeguard your application.
Automate AI red teaming
Ensure your application behaves consistently without unexpected deviations by automating continuous, scalable red teaming tests.
Integrate AI red teaming into your AppSec strategy
With a unified approach, you can proactively uncover unique AI threats, expand security coverage, scale testing, and ensure compliance—without burdening security teams or developers.
The solution
Mend AI red teaming
Mend AI tests against threats like prompt injection, context leakage, and data exfiltration to uncover AI behavioral risks unique to your application.