Netskope: AI Red Teaming: Automated Security Testing for LLMs

Name: Netskope: AI Red Teaming: Automated Security Testing for LLMs
Uploaded: 2026-10-12T13:34:19-04:00
Duration: 3 min 37 s
Description: This is a demo of Netskope One AI Red Teaming Proactively identify and address vulnerabilities in private AI deployments. Automate adversarial simulations to find and fix vulnerabilities, to ensure your AI is resilient and production-ready before it re...

netskope

10/12/2026

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Transcript

One AI Red Teaming. Now LLMs are now core to your app ecosystem, but they bring significant risks from data leakage and compliance gaps to evolving security threats. Netscope One AI Red Teaming automates adversarial simulations to find those vulnerabilities first. We ensure your private models are secure, compliant, and resilient before an attacker ever has a chance to strike. Now let's take a look at Netscope One AI Red Teaming in action. First we want to register our LLMs that we want to perform the simulated attack against. Attack virtually any model via REST API or select a default configuration supporting the OpenAI compatible API, Azure API, or Amazon Bedrock. Next we want to perform test rounds against selected LLMs. This is where you initiate simulated attacks against selected models. Let's take a look at the results of the simulated attack. We are first presented with an overview of the test results including attack success rate, how many attacks succeeded, and how many were caught by the LLMs built-in guardrails, and the total number of use cases for this simulated attack. Now at first glance this looks like a relatively low success rate, but more than 3,300 attacks were successful. You get a breakdown of techniques and categories like PII, prompt injection, and jailbreaking, and inappropriate content. You are also presented with the attack success rate across all categories, techniques, and OASP as well. If we click into one of the failed test cases using the technique Deceptive Delight, we see an explanation of the attack, the details of the prompts and responses, including multi-turn conversations, and ultimately the response that returned from the LLM that triggered the success of this attack. Now AI Red Teaming is best used as a continuous assessment when you perform multiple simulated attacks over time. AI Red Teaming provides a model drifting view that lets you know how the model's built-in guardrails perform over time. The overview page provides a summary of the results of all simulated attacks against all models. In this case, every category and every technique used resulted in successful attacks. Not a good scenario. Now this is a good opportunity to enable the Netscope One AI Gateway and AI guardrails to ensure these security gaps are addressed. That is Netscope One AI Red Teaming, securing your private models against advanced threats and ensuring that your journey to AI is safe and compliant. Thank you for watching.

TL;DR

Netskope One AI Red Teaming automates adversarial simulations against private LLMs to identify vulnerabilities in data leakage, compliance, prompt injection, jailbreaking, and inappropriate content before production deployment.
The platform supports testing any model via REST API or default configurations for OpenAI, Azure, and Amazon Bedrock, providing detailed attack success rates across categories, techniques, and OWASP classifications.
Continuous assessment features track model drift over time, revealing how built-in guardrails perform across multiple simulated attacks and highlighting opportunities to deploy Netskope's AI Gateway for additional protection.

Summary

This demonstration showcases Netskope One AI Red Teaming, a security testing platform designed to identify vulnerabilities in private large language model deployments before they reach production. The product automates adversarial simulations against LLMs to uncover security gaps including data leakage risks, compliance violations, prompt injection attacks, jailbreaking attempts, and inappropriate content generation. The demo walks through the complete workflow from registering target models via REST API or pre-configured integrations with OpenAI, Azure, and Amazon Bedrock, through executing simulated attack campaigns, to analyzing detailed results across multiple security categories and OWASP classifications. The platform provides continuous assessment capabilities with model drift tracking to monitor how built-in guardrails perform over time, positioning itself as a proactive security layer that identifies weaknesses before attackers can exploit them in production environments.

Chapters

0:00 - Introduction to AI Red Teaming
0:49 - Registering LLMs for Testing
1:08 - Executing Simulated Attacks
1:21 - Analyzing Attack Results
2:27 - Continuous Assessment and Model Drift

Key Quotes

0:27 "Netskope One AI Red Teaming automates adversarial simulations to find those vulnerabilities first."
0:35 "We ensure your private models are secure, compliant, and resilient before an attacker ever has a chance to strike."
1:39 "Now at first glance this looks like a relatively low success rate, but more than 3,300 attacks were successful."

FAQ

What types of LLMs can Netskope One AI Red Teaming test?

The platform can test virtually any LLM via REST API, with default configurations supporting OpenAI-compatible APIs, Azure API, and Amazon Bedrock deployments.

How does AI Red Teaming help with ongoing security?

The platform provides continuous assessment capabilities with model drift tracking, allowing organizations to monitor how their LLM's built-in guardrails perform over time across multiple simulated attack campaigns.

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