Weaknesses found, then hardened, Evidence you can take to the board
Evidence of AI resilience you can put in front of the board and regulators
New AI systems tested and cleared for launch in days, not quarters
Findings that close themselves: testing flows directly into hardening
Attack Your AI, On Purpose
Thousands of simulated attacks spanning the latest AI threat techniques, running continuously against your AI systems. Not a quarterly exercise. A standing adversary on your side.
Models, applications, agentic workflows, and MCP servers. If it reasons, retrieves, or acts, it gets tested the way attackers would test it.
Prompt hardening remediates discovered weaknesses at the system-prompt layer. Testing flows directly into hardening, so findings close instead of piling up.
ThreatLabz found critical flaws in 100% of the enterprise AI systems it analyzed
Sooner or later the board will ask: how do we know our AI cannot be turned against us? Hope is not an answer. A pen test from last quarter is not one either, because your AI changed since then, and so did the attacks. AI Red Teaming answers with evidence, continuously attacking your AI systems the way adversaries would, then hardening what it finds. Your answer becomes a report, not a hope. Evidence you can put in front of the board and regulators.
How it works
A Standing Adversary, On Your Side
Thousands of automated attack
simulations spanning the latest AI threat techniques, run continuously
Coverage for models
applications, agentic workflows, and MCP servers
Prompt hardening
that remediates discovered weaknesses at the system-prompt layer
Findings mapped
to recognized AI risk frameworks for audit and compliance reporting
New AI systems
tested and cleared for launch in days, not quarters
Attack techniques
refreshed from ThreatLabz research, so tests track what adversaries actually do
Results feed AI Guard for Apps
so what testing finds, runtime protection holds
Use cases
Prove it’s safe, Before attackers do
Every AI system you ship is a system someone will try to break. AI Red Teaming breaks it first, safely, and hardens what it finds, so launches move fast and stay defensible.
A new AI assistant is ready for production, and the review board wants proof it is safe. Continuous adversarial testing clears it in days, with findings hardened and documented, not debated.
When leadership asks how you know your AI cannot be turned against you, the answer is a current report: what was attacked, what held, what was fixed. Evidence, not assurance.
Agent tooling multiplies faster than any manual review. MCP servers and agentic workflows get the same continuous adversarial testing as your models and apps.
A jailbreak path is discovered on Tuesday, hardened at the system-prompt layer, and retested by Wednesday. Testing and hardening are one loop, not two backlogs.
Part of Zscaler AI Security
Testing is one loop in the platform. What red teaming finds, the rest enforces.
AI Guard for Apps
runtime protection holds the line on what testing uncovered, in production.
AI SPM
posture findings show where to point the next round of testing.
AI Gateway
hardened MCP servers and agent workflows sit behind per-action authorization.
AI Access Graph
blast radius context shows what a discovered weakness could actually reach.
