Artificial intelligence (AI) and large language models (LLMs) are now essential tools in enterprise environments. This blog post summarizes a fireside chat with industry leaders Damián Hasse, Emily Choi-Greene, and Rob Ragan, focusing on pragmatic AI and LLM security mitigations.
Key discussion points include:
- Evolution of AI: From rudimentary chatbots to sophisticated, flexible solutions using both discriminative and generative ML models.
- AI Security Risks: Addressing data poisoning, overfitting, and data leakage with strategies like diverse datasets and differential privacy.
- Enterprise Adoption: Balancing in-house development with third-party vendor partnerships, emphasizing vendor selection, data handling, and threat modeling.
- Differential Privacy: Using data in model training without compromising individual privacy by adding controlled random noise to the data set.
- Confused Deputy Attacks: Preventing AI models from making security decisions for themselves or other AI models.
The post provides actionable insights for CISOs and security teams looking to navigate the complexities of AI security and adoption.
