Securing the LLM Development Lifecycle: A Proactive Approach

Most organizations only start thinking about the security of their large language models (LLMs) once the model is already developed and deployed. However, this reactive approach often leads to costly retrofitting, unexpected vulnerabilities, and missed opportunities to build security in from the ground up. This webinar takes a different approach, guiding security professionals and LLM developers on how to address security concerns at every stage of the LLM development lifecycle. Rather than waiting until the end, we will explore the security implications and best practices for securing LLMs during the critical phases of project inception, data curation, model architecture, training at scale, evaluation, post-improvements, and API integration. By adopting a proactive security mindset, attendees will learn how to: • Align security assessments with the intended use cases and model requirements during the scoping phase • Mitigate risks like data poisoning, backdooring, and adversarial prompting throughout the development process • Ensure training stability and model integrity through techniques like checkpointing, weight decay, and gradient clipping • Evaluate LLM performance while preventing manipulation of benchmark datasets and evaluators • Implement access controls, data leakage prevention, and other security measures during fine-tuning and API integration Attendees will leave this webinar with a comprehensive understanding of how to weave security into every step of the LLM lifecycle, empowering them to build more secure and trustworthy AI systems from the start. This proactive approach is crucial for organizations looking to stay ahead of evolving security threats and deliver LLM-powered solutions with confidence.