Certified GenAI and LLM Cybersecurity Professional for Data Scientists (CGLCP-DS)

Length: 2 Days

Certified GenAI and LLM Cybersecurity Professional for Data Scientists (CGLCP-DS) Certification Course by Tonex

Certified GenAI and LLM Cybersecurity Professional for Data Scientists is a 2-day course where participants grasp the principles of Generative AI and Large Language Models and implement data security and privacy best practices in AI projects.

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As the adoption of generative AI and large language models (LLMs) grows, so does the need for robust cybersecurity measures to protect these technologies.

LLMs, such as GPT-4 and other advanced generative models, are susceptible to various threats, including data poisoning, adversarial attacks, and model extraction. To combat these challenges, specific technologies and strategies are being employed to enhance AI cybersecurity.

One of those important technologies is federated learning, a crucial technology in securing LLMs. It allows models to train across decentralized devices or servers without sharing raw data. This method reduces the risk of data breaches and maintains user privacy, making it harder for attackers to infiltrate and corrupt the training data.

By distributing learning, federated learning minimizes centralized vulnerabilities.

Differential privacy is also a key. To protect sensitive data from exposure, differential privacy adds noise to data before it is used in training. This approach ensures that individual data points cannot be traced back, even if the model is compromised.

Differential privacy provides a layer of security that prevents attackers from extracting sensitive information from the model, safeguarding user data throughout the AI lifecycle.

Then there’s adversarial training, which strengthens models against attacks by exposing them to deliberately crafted adversarial examples during training. By simulating attacks, LLMs learn to recognize and mitigate such threats, making them more resilient to real-world adversarial inputs that could otherwise manipulate outputs or cause malfunction.

And yet another innovative technology, Secure Multi-Party Computation (SMPC), allows multiple parties to jointly compute a function without revealing their individual inputs. This technology is essential in maintaining the confidentiality of data used for training or model refinement, preventing unauthorized access or leaks during collaborative AI development.