OWASP Top 10 for LLMs – Myths & Realities

Are your AI security defenses strong enough to withstand today's generative AI vulnerabilities? OWASP Top 10 vulnerabilities specific to LLMs provide a robust reference framework for mitigating AI and data risks. While traditional security techniques fall short in protecting modern AI applications, even deploying an LLM Firewall to protect the model at the edge is not enough.

The non-deterministic nature of the LLMs and their complex interactions with data sources and other AI systems in a modern app presents significant security, privacy, and compliance challenges. Join us for an engaging talk where we will debunk common myths and reveal the realities around OWASP Top 10 for LLMs.

Further, we will explore a holistic 5 step AI security approach needed to prevent and mitigate risk due to these OWASP LLM application vulnerabilities.

• Understand AI and Data Security Challenges: Explore shadow AI, OWASP Top 10 vulnerabilities for LLMs, data mapping, and sensitive data exposure risks.
• Implement Multi-Layered LLM Firewalls: Protect your prompts, data retrieval, and responses using a multi-layered approach.
• Enforce Data Entitlements: Prevent unauthorized data access in GenAI applications.
• Enforce Inline Data Controls: Safeguard sensitive data during model training, tuning, and RAG (Retrieval Augmented Generation).
• Automate Compliance: Streamline adherence to emerging data and AI regulations.