8 Generative AI Security Best Practices for Users and Data

• Understand the unique risks to your data

Before implementing generative AI, it's crucial to identify specific use cases for it within your organization. From automating customer service responses to generating marketing content, understanding the application will determine the security measures needed.  

For instance, if you're using AI for customer service, you'll need to focus on data privacy and integrity. Conversely, if it's for content creation, safeguarding intellectual property becomes paramount.

• Vet GenAI tools with an eye on security

Not all generative AI tools are created equal. It's essential to vet each tool for its security features before integration.  

Look for tools that offer robust encryption, user authentication and audit logs. Ensure they comply with industry standards such as ISO/IEC 27001 for information security management. By thoroughly examining these tools before adopting one, you can mitigate the risk of data breaches and unauthorized access.

• Discover and classify data to apply appropriate controls

Identifying sensitive data such as PII and financial records is understandably the first step in protecting it. You can take a proactive approach by rapidly applying data discovery and classification with Data Security Posture Management (DSPM), powered by innovative AI Mesh technology.

Once classified, apply appropriate controls like anonymization and encryption. These measures ensure that even if your data is compromised, it will remain unreadable and unusable to malicious actors.

• Employ a policy of least privilege for Identity and Access Management (IAM)

The principle of least privilege is a fundamental security practice. It ensures that users have the minimum level of access necessary to perform their tasks. Implement Role-Based Access Controls (RBAC) and Multi-Factor Authentication (MFA) to enhance security. Regularly review access permissions to ensure they remain appropriate as roles and responsibilities change within your organization.

You can ensure secure employee access to cloud and web apps across your organization, providing continuous control over data, with Forcepoint ONE SSE.

• Train employees and create internal policies for GenAI

Human error remains one of the most significant security risks. Training your employees on the security risks associated with generative AI is imperative.  

Develop comprehensive internal usage policies that outline acceptable use, data handling procedures and incident reporting protocols. Regular training sessions and awareness campaigns can help reinforce these policies and reduce the likelihood of security breaches.

• Pay attention to input sanitization and prompt handling

Now as ever, input sanitization is crucial to prevent injection attacks, in which malicious inputs can compromise your AI system. Implement stringent input validation and sanitization techniques to ensure only clean data enters your system. To block user input of off-limits data, you can enforce policies tailored to generative AI and prevent data loss with Forcepoint Data Loss Prevention.

Likewise, handle AI-generated prompts with care, ensuring that they don't inadvertently expose sensitive information or lead to unintended actions.

• Monitor compliance requirements and audit vendors

Regulatory compliance is non-negotiable in today's data-driven world. Keep ahead of relevant regulations such as GDPR, CCPA and HIPAA. Regularly audit your vendors to ensure they comply with these regulations and meet your security standards.  

Maintaining compliance not only protects your organization from legal repercussions but also enhances your reputation.

• Establish an AI incident response plan

Unfortunately, security incidents can occur even if you have strong defensive measures in place. An AI incident response plan is crucial for minimizing damage and restoring normal operations. Your plan should include clear steps for identifying, containing and mitigating AI-related incidents.