
Giskard is an AI Red Teaming and LLM Security Platform dedicated to identifying and handling GenAI risks. The organization's core mission is to find vulnerabilities in AI agents and Large Language Models (LLMs) before they impact users in production, ensuring the safe deployment of AI. Giskard offers Continuous Red Teaming, LLM Evaluation, and AI Guardrails, providing comprehensive AI assessments and remediation guidance. Serving clients like Google DeepMind and L'Oréal, Giskard contributes to AI safety and innovation, with a strong open-source presence and adherence to European standards.
Discover the people behind Giskard
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Industry, Innovation & Infrastructure
Secure AI Infrastructure for Industry
Giskard identifies vulnerabilities in AI systems and Large Language Models to ensure their secure integration and the development of trustworthy digital infrastructures within companies.
Innovation through Reliable AI
The organization develops AI Guardrails and provides comprehensive assessments, enabling and promoting the safe adoption of innovative AI applications across various industries.
Peace, Justice & Strong Institutions
Strengthening Institutions with Secure AI
Giskard prevents critical security threats like data disclosure and inappropriate content generation by AI, thereby strengthening the integrity and trust in the institutional use of AI.
Building Trust in Digital Systems
The platform enables organizations to deploy AI responsibly by addressing vulnerabilities that could otherwise undermine the credibility and reliability of public and private institutions.
Decent Work & Economic Growth
Economic Growth through AI Security
Giskard minimizes business risks and operational disruptions through secure AI implementations, which strengthens confidence in new technologies and thus promotes stable economic growth.
Secure Jobs in the AI Era
The organization enables companies to reliably deploy AI technologies, thereby increasing productivity and creating a foundation for future, secure jobs in the digital economy.
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