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Keeping cybersecurity regulations top of mind for generative AI use

Can businesses stay compliant with security regulations while using generative AI? This article explores the security risks associated with generative AI and how businesses can navigate these risks to comply with cybersecurity regulations.

Generative AI poses several cybersecurity risks that may challenge businesses in staying compliant with regulations. One of these risks is the improper use of AI. While generative AI models can assist in programming and even write original code, users can abuse this function by using AI to write malware or create convincing phishing content. These uses increase security threats for businesses as they enable hackers to create malicious content faster and easier.

Another risk is the exposure of sensitive data and intellectual property (IP). Generative AI algorithms learn from every interaction and prompt, meaning they may “remember” any information included in prompts. Additionally, generative AI can use a business’s IP in generated content, as it can only create content recycled from what it has already seen. This puts a business’s IP at risk and makes it challenging to prove if an AI used certain IP.

A unique cybersecurity risk with AI is the possibility of “poisoned” training datasets. Hackers can feed malicious training data to an AI model, creating a backdoor into a system or forcing it to misbehave. These attacks are difficult to detect and can lead to significant breaches.

Despite these risks, it is possible to use generative AI effectively while complying with regulations. Understanding all relevant regulations and mapping out how the AI model is connected to business processes is crucial. Clear guidelines and limitations for using generative AI should be established, outlining what information can and cannot be included in prompts. It is also important to communicate with third-party vendors and partners to ensure they are compliant with cybersecurity measures.

Implementing AI monitoring can help businesses detect any sensitive data or abnormal behavior in generative AI algorithms. Continuous monitoring increases the likelihood of spotting signs of data poisoning and ensuring compliance.

In conclusion, businesses can navigate security compliance with generative AI by understanding regulations, establishing guidelines, communicating with vendors, and implementing AI monitoring. By taking these steps, businesses can leverage the power of generative AI while staying compliant and secure.

Key Points:
1. Generative AI poses cybersecurity risks such as the improper use of AI, exposure of sensitive data and IP, and the possibility of “poisoned” training datasets.
2. Businesses can navigate these risks by understanding regulations, establishing clear guidelines, communicating with vendors, and implementing AI monitoring.
3. Compliance with generative AI requires a thorough understanding of cybersecurity regulations and consideration of non-security standards.
4. Guidelines for using generative AI should prioritize what information can and cannot be included in prompts, and vendors should be assessed for compliance.
5. AI monitoring can help detect sensitive data, abnormal behavior, and signs of data poisoning in generative AI algorithms.

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