What are the security requirements for generative AI?

Generative AI (GenAI) security requires a multi-pronged approach, focusing on data, prompts, and the model itself. Here are some key security requirements:

Data Security:

Data Inventory and Classification: Maintain a comprehensive record of all data used to train GenAI models. Classify data based on sensitivity (confidential, PII etc.) to prioritize security measures.
Data Governance: Implement access controls to restrict who can access and use sensitive data for training. Techniques like dynamic masking or differential privacy can further protect sensitive data.
Compliance: Ensure data used for training complies with relevant regulations regarding data consent, residency, and retention.
Prompt Security:

Prompt Scanning: Scan user prompts before feeding them to the model. Identify and flag malicious prompts that attempt to:
Inject code to manipulate the model's behavior.
Phish for sensitive information.
Leak confidential data through the generated response.
Model Security:

Zero Trust Architecture: Apply a "zero trust" approach, assuming any user or prompt could be malicious. Implement robust authentication and authorization procedures.
Continuous Monitoring: Monitor the model's outputs for signs of bias, drift, or unexpected behavior that could indicate security vulnerabilities.
Regular Updates: Keep the GenAI model and its underlying libraries updated to address any discovered security flaws.
Additional Considerations:

Vendor Security: When using cloud-based GenAI services, research the vendor's security practices and ensure they align with your company's security posture.
Staff Training: Educate staff on responsible GenAI use, including proper data handling and identifying suspicious prompts.
By implementing these security requirements, companies can leverage the power of GenAI while minimizing the risk of data breaches and misuse.