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August 28, 2024 at 10:03AM
The rise of generative AI and large language models bring real security risks, from exposing data to malicious attacks. The rapid adoption of AI introduces new risks, but the opaque nature of AI models makes identifying and managing these risks challenging. Implementing an AI security framework and following key strategies can help mitigate these risks and ensure a security-first posture.
Based on the meeting notes, it’s clear that there is a growing recognition of the security risks associated with generative AI and large language models. These risks range from “hallucinations” to exposing private and proprietary data, and they are part of a broader attack surface associated with AI and machine learning.
The rapid rise of AI has introduced new business risks, such as intrusions, breaches, and the loss of proprietary data and trade secrets. However, due to the opacity of AI systems, many organizations lack visibility into the dispersed and often invisible risks associated with AI. The recent mass adoption of AI systems has changed the stakes, and attackers are now focusing on AI and machine learning technologies.
To address these risks, organizations should consider adopting a comprehensive AI security framework, such as MLSecOps, which provides visibility, traceability, and accountability across AI/ML ecosystems. Additionally, implementing risk management strategies, advanced security scanning tools, creating an AI bill of materials, utilizing open source security tools, and encouraging collaboration and transparency through AI bug bounty programs can help mitigate these risks.
Overall, organizations can implement an advanced AI security framework to make hidden risks visible and enable security teams to track and address them before they have an impact.