December 4, 2024 at 11:13PM
AI adoption is increasing among organizations for productivity and new business opportunities, but security often lags behind. The article outlines AI security risks, including prompt injection and model theft, suggesting best practices to mitigate these risks, such as configuring sensitive information filters and disabling public access to AI resources.
### Meeting Summary: AI Configuration Best Practices to Address AI Security Risks
**Date:** December 02, 2024
**Presented by:** Joy Ngaruro
**Read Time:** 5 minutes
#### Key Takeaways:
1. **Rise of AI Adoption:**
– Many organizations are increasingly adopting Artificial Intelligence (AI) for productivity improvements and new business opportunities.
– A McKinsey survey indicated that 65% of organizations are actively using generative AI (GenAI), nearly double the past year.
2. **Security Risks Associated with AI:**
– As companies rush to adopt GenAI, security best practices are often overlooked, increasing vulnerability to attacks.
– Recent incidents highlight security challenges, including:
– The Qubitstrike attack exploiting exposed AI model notebooks.
– The ChatGPT exploit revealing sensitive information.
3. **Specific Security Threats:**
– AI usage brings about various risks outlined by the OWASP Top Ten for LLMs and Generative AI apps:
– **Prompt Injection**: Risk of disclosing sensitive information.
– **Insecure Output Handling**: May lead to cross-site scripting.
– **Training Data Poisoning**: Risks performance degradation.
– **Model Denial of Service**: Affects service availability.
– **Sensitive Information Disclosure**: Potentially exposing confidential data.
– **Excessive Agency**: Damaging actions from ambiguous outputs.
– **Overreliance on LLMs**: Leads to misinformation.
– **Model Theft**: Theft of valuable AI models or data.
4. **Consequences of Security Failures:**
– Organizations may suffer loss of customer trust, legal issues, reputational damage, and financial losses due to inadequate security controls.
5. **Recommended AI Security Best Practices:**
– **Amazon Bedrock:**
– Configure sensitive information filters to protect PII.
– Disable direct internet access for Notebook instances for enhanced security.
– **Microsoft Azure:**
– Disable public network access to OpenAI service instances.
– Use system-assigned managed identities for security enhancement.
– **Google Cloud:**
– Disable root access for Vertex AI notebook instances to prevent misuse.
– Utilize Customer-Managed Encryption Keys for dataset encryption.
6. **Trend Micro Solutions:**
– Trend Micro AI Security Posture Management (ASPM) provides tools to detect misconfigurations and recommend remediation steps.
#### Action Items:
– Review and implement recommended practices for AI configuration security.
– Consider using Trend Micro’s AI Security Posture Management services for ongoing compliance and security monitoring.
#### Additional Resources:
– For further information, visit the Trend Micro resources: [Trend Micro AI Security](https://ift.tt/aoEnui7).
This summary provides insights into the rapid adoption of AI technologies, the associated security risks, and essential best practices for ensuring secure deployment.