5 Practical Steps for CISOs to Secure GenAI

Generative AI (GenAI) is rapidly transforming industries, offering a wealth of possibilities for content creation, code generation, and data analysis. With great power comes great responsibility, especially for CISOs tasked with safeguarding enterprise data and privacy.

Commercially available LLMs like ChatGPT and Google Gemini are widely used by consumers. However, enterprises must consider data security and regulatory compliance as additional requirements before they adopt GenAI; especially those that are entrusted with large amounts of private customer data and/or proprietary intellectual property.

As you may be looking to unlock the benefits of GenAI, here is a practical and actionable checklist to help in that journey.

Benefits of Generative AI (GenAI): A Guide to Secure

1) Build a Secure Foundation: Governance and Risk Assessment

• Identify Your GenAI Use Cases: Not all GenAI applications are created equal. Catalog the specific GenAI tools your organization plans to use and their intended purposes.
• Conduct a Risk Assessment: A thorough risk assessment to identify potential security threats associated with GenAI is essential. Consider data exposure, bias in generated content, and the misuse of outputs.
• Know Your Data: Take stock of all the data feeding your GenAI models. Carefully assess its sensitivity and the privacy implications involved.

2) Data & Model Security Measures: Keeping Your Assets Safe

• Implement Identity and Data Access Controls: Just like Fort Knox, your data needs tight security. Implement granular access controls to limit who can access and what data is fed into the model.
• Prioritize Data Minimization: The less data, the less risk. Use the minimum amount of data necessary to train your GenAI models. Explore privacy-enhancing techniques like tokenization for added protection.
• Encrypt Sensitive Data: Encrypt sensitive data at rest, in transit and in use to shield it from unauthorized access, even in the event of a breach.
• Guardrails to Secure Model Integrity: Validate and monitor data inputs to mitigate the risk of prompt injection and data poisoning.

3) Preparedness is Key: Monitoring and Incident Response

• Monitor Activity: Keep a watchful eye on GenAI usage patterns. Monitor for anomalies and potential misuse to identify and address issues early on.
• Incident Response Plan: The unexpected can happen. Develop a comprehensive incident response plan to identify, respond to, and contain data breaches or security incidents involving GenAI.

4) Legal and Regulatory Considerations: Staying Compliant

• Data Privacy Compliance: Navigate the complexities of data privacy regulations like GDPR and CCPA, as well as newer AI related regulations like the EU AI Act. Ensure compliance with how data is collected, used, and stored for GenAI training.
• Intellectual Property (IP): Clearly define ownership rights. This applies to both the data used to train GenAI models and the content they generate.

5) Empower Your Team: Training and Awareness

• Security Awareness Training for GenAI: Educate employees on GenAI security risks, responsible use practices, and how to identify potential biases in generated content.
• Clear Usage Guidelines: Develop clear and concise guidelines for using GenAI tools. Outline acceptable data inputs, content outputs, and potential limitations of the technology.

Conclusion: A Secure Future with GenAI

Following these steps will serve as a starting point for your GenAI security journey. As technology evolves, so too should your security posture. By continuously evaluating and adapting your approach, you can unlock the full potential of GenAI while safeguarding your organization's data and privacy.