Adrian Johnson
TLDR:
- Generative AI poses security risks for organizations.
- IBM introduces a Framework for Securing Generative AI to address these risks.
In a world where AI is becoming increasingly essential, organizations cannot afford to ignore the security risks that come with generative AI. IBM’s Institute for Business Value revealed that 96% of executives believe adopting generative AI increases the likelihood of a security breach within three years. To tackle this issue, IBM introduced its Framework for Securing Generative AI, focusing on five key areas: data security, model development, model usage, infrastructure security, and AI governance.
When it comes to securing AI data, organizations must prioritize encryption, identity and access management, data discovery, and classification. As for securing the AI model, continuous vulnerability scanning, securing API and plug-in integrations, and implementing policies and controls are crucial. Organizations also need to monitor AI model usage for prompt injections, sensitive data, and inappropriate content.
Securing the infrastructure that supports AI is vital in any cybersecurity strategy, which includes network security, access control, data encryption, and intrusion detection/prevention systems. Additionally, establishing AI governance ensures that AI tools and systems remain safe and ethical, monitoring for fairness, bias, and drift over time.
Overall, IBM’s Framework for Securing Generative AI provides a comprehensive approach to addressing the security risks associated with AI adoption, highlighting the importance of a solid security framework in the age of AI-driven technologies.
