Summary
Generative AI technology brings new cybersecurity concerns, including data accuracy, privacy, and trust issues. Securing data used to train models is vital to prevent risks like data poisoning and leakage. To build trust, it's essential to manage model supply chains, vet sources, use APIs securely, and monitor for potential threats like prompt injections and intellectual property theft. Ensuring confidentiality, integrity, and availability (CIA Triad) is paramount for securing AI systems, while governance and regulatory compliance play key roles in safeguarding infrastructure.
Introduction to Generative AI Technology
Generative AI technology introduces new threats and risks related to cybersecurity, privacy, and data accuracy. Lack of trust in generative AI hinders its full potential.
Securing Data for Generative AI
Securing the data used to train and tune the model is essential to prevent data poisoning, exfiltration, and leakage. Measures include data discovery, classification, cryptography, access controls, and monitoring.
Securing the Model
Ensuring the trustworthiness of models by managing the model supply chain, vetting sources, using APIs securely, and preventing prompt injections, privilege escalations, and intellectual property theft.
Securing Generative API Usage
Challenges in securing the usage of generative API include prompt injections, denial of service attacks, model theft, and the importance of monitoring inputs and implementing machine learning detection and response tools.
Securing Infrastructure for Generative AI
The importance of the CIA Triad (confidentiality, integrity, availability) for securing AI systems on traditional IT infrastructure. Governance and regulatory compliance are crucial elements of securing the infrastructure.
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