Generative AI technology has brought about groundbreaking advancements, but with it comes a new set of risks and challenges that require innovative solutions. Phil Venables, CISO of Google Cloud, emphasizes the importance of expanding conventional cyber detection and response to monitor for AI abuses while leveraging AI for defensive advantage.
One of the key risks associated with generative AI is the potential for models to produce inaccurate content or hallucinations. There is also the risk of sensitive data leakage through a model’s output, as well as the manipulation of models through biases introduced during training data selection or fine-tuning.
Venables underscores the need for controls and common frameworks to ensure that every AI deployment does not start from scratch. Mitigating risks associated with data poisoning and ensuring data integrity are crucial elements in securing AI applications.
Filtering inputs to prevent prompt injection and controlling model outputs are essential strategies in mitigating risks from adversarial attacks. Organizations should implement filters and outbound controls to manage how models manipulate data and actuate physical processes.
To safeguard against unintended model behavior, Venables recommends monitoring for software vulnerabilities in the underlying infrastructure. Implementing sandboxing, enforcing the least privilege principle, and focusing on observability and logging actions are vital in maintaining the security and integrity of AI applications.
In conclusion, securing the use of AI involves sanitizing and protecting training data, enforcing strong access controls, filtering inputs and outputs, and implementing a risk and control framework for defense in depth. By adopting these strategies, organizations can effectively mitigate the risks associated with generative AI technology.
