Data security principles for AI: confidential, integrity, and availability.

TLDR:

Data security is a top challenge for organizations looking to implement AI
Three guiding principles for data security in the AI era: Securing the AI, Securing from AI, and Securing with AI

Article Summary:

Data security is a critical concern for organizations as they navigate the AI era. With the increasing adoption of AI projects, the volume of data being stored across cloud environments is surging. This influx of data opens up new risk vectors and makes organizations prime targets for cybercriminals. To address this, organizations need to develop an effective data security program based on three guiding principles:

1. Securing the AI: Organizations need to secure all components of AI deployments, including data, pipelines, and model output, in the context of their impact on sensitive data exposure, access, and regulatory compliance.

2. Securing from AI: Cyber criminals are leveraging AI to generate and execute attacks at scale, making it crucial for organizations to protect their systems from AI-powered threats. Attackers could compromise generative AI tools and large language models, leading to data leakage and security breaches.

3. Securing with AI: AI can become an integral part of defense strategies, enabling organizations to anticipate, track, and thwart cyberattacks efficiently. AI’s capabilities in pattern recognition can help identify and stop threats earlier, saving security analysts time.

By focusing on these three data security disciplines, organizations can confidently explore and innovate with AI without compromising their security. It is essential for organizations to prioritize data security in the AI era to safely pursue the benefits that AI has to offer.

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Data security principles for AI: confidential, integrity, and availability

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