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Continuous Red Teaming

Continuous red teaming platform for testing and securing LLM agents

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Continuous Red Teaming Description

Continuous Red Teaming by Giskard is a platform designed to test and secure LLM agents through automated vulnerability detection. The platform operates as a black-box testing tool that requires only API endpoint access to the target agent. The system generates dynamic, multi-turn attacks using an AI red teamer that interacts with agents and adapts based on responses rather than using static tests. It creates context-aware attacks by leveraging internal business context such as PDFs, knowledge bases, and websites to generate targeted attacks specific to use cases and operational scope. The platform integrates external threat databases including OWASP and open-source security datasets to provide attack coverage. It detects vulnerabilities including hallucinations, security flaws, stereotypes, discrimination, harmful content, personal information disclosure, and prompt injections. Giskard supports conversational AI agents in text-to-text mode and is aligned with AI security standards including NIST AI RMF, OWASP LLM Top 10, EU AI Act, and ISO 42001. The platform provides both pre-deployment testing with quantitative KPIs and post-deployment continuous monitoring to detect emerging vulnerabilities. The system includes a collaborative red-teaming playground and annotation tools for business stakeholders including domain experts and product managers. Technical consulting support is available to help design guardrails and mitigate identified vulnerabilities. On-premise deployment options are available for mission-critical workloads.

Continuous Red Teaming FAQ

Common questions about Continuous Red Teaming including features, pricing, alternatives, and user reviews.

Continuous Red Teaming is Continuous red teaming platform for testing and securing LLM agents developed by Giskard. It is a AI Security solution designed to help security teams with AI Security, Red Team, Vulnerability Detection.

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