Prevent AI Failures, don't react to them.
The first automated Red Teaming platform for AI agents to prevent both security vulnerabilities and business compliance failures.

Giskard AI

**Traditional guardrails often struggle with enterprise AI agents** due to context-blindness, frequently blocking legitimate user requests as false positives. Furthermore, standard content moderation filters fail to address the technical realities of modern agentic workflows, leaving systems vulnerable to multi-step social engineering and tool-chain exploitation. As organizations scale their generative AI deployments, the reliance on manual governance and generic taxonomies creates significant security and compliance gaps.

In this live session, David Berenstein **will present Giskard Guards**, an independent European platform designed for context-aware, sovereign AI security. We will detail how technical teams can **deploy policy-driven guardrails** directly within their own infrastructure, ensuring that **custom compliance** frameworks are strictly enforced without compromising data sovereignty.

**Agenda:**

*   **AI Guardrails 101:** Understand the fundamentals of enterprise AI defenses, from mitigating data leakage and hallucinations to preventing complex prompt injections.
*   **Model vs. Agent Guardrails:** Explore why standard content moderation filters fail in agentic workflows, and learn how to secure the full execution chain, including multi-step reasoning, tool calls, and parameter validation.
*   **Implementing Policy-as-Code:** Transition from manual, static risk assessments to machine-enforceable OPA/Rego policies that can be versioned in Git and deployed directly on your infrastructure.
*   **Enforcing Regulatory Frameworks:** Leverage pre-built, automated policy packs to ensure compliance with the EU AI Act and the OWASP Top 10 for LLMs, and learn how to add your own policies.
*   **Conclusion and Q\&A.**

**Who Should Attend:** AI Product Managers, Heads of AI, AI/ML engineers, AI security professionals, Data scientists, and anyone building or deploying GenAI applications who wants to ensure their models are safe, reliable, and production-ready.
