Adrian Johnson
TLDR:
Businesses should base AI data security strategies on existing data security investments to handle security challenges posed by AI data. Leveraging existing controls and policies can help to secure data for AI more effectively.
Article Summary:
The article discusses the challenges of securing AI data and how businesses can extend existing data protection strategies to protect information used for AI. Key points include:
- Generative AI poses unique data security challenges, leading cybersecurity leaders to adapt enterprise data protection strategies.
- Instead of creating new security controls for AI data, organizations can build upon existing data security investments.
Data is the currency of AI, and businesses must expose large quantities of potentially sensitive information to AI models for optimal performance. Organizations face risks of data exposure and misuse when working with AI models trained on private information. However, many enterprises have already established controls and policies to protect sensitive data at scale, such as role-based access control systems and data governance rules.
Businesses can take actionable steps to extend data security strategies to support AI, including tidying up unstructured data with effective access controls, using AI to query databases without ingesting data, and aligning data governance rules with AI models. By leveraging existing data security investments in new ways, organizations can tighten controls over AI data effectively and without reinventing the wheel.
Overall, the article emphasizes the importance of building AI data security strategies on the foundation of existing data security controls and policies to address the unique security challenges presented by AI data.
