Building Secure AI Applications: A 5-Step Approach

Introduction

• Many organizations struggle to deploy AI applications due to data security and privacy concerns.
• OAS (Open AI Safety) provides guidance on the key issues to address for secure AI development.
• This presentation outlines a 5-step framework to help organizations build AI applications faster, safer, and with lower stress.

Key AI Usage Areas

• Internal applications: Shadow AI being developed without governance
• Developer experimentation: Lack of security guardrails
• SaaS applications: Exposing sensitive corporate data to external services

Prioritizing OAS Vulnerabilities

• The most important vulnerabilities to address are:
  1. Sensitive information disclosure
  2. Excessive agency

Holistic Approach to AI Security

• Security is not just about prompt security, but a comprehensive approach across data, models, and interactions.
• Unstructured data governance is a critical foundational element.

5-Step Approach to Building Secure AI Applications

1. Automated Discovery: Identify all AI models and agents being used in the organization.
2. Model Risk Assessment: Assess the risks and vulnerabilities of the models being used.
3. Data Understanding: Develop a knowledge graph to understand the data being used by the AI pipelines.
4. Security Controls:
   • Data-level controls: Sanitization, entitlements
   • Model-level controls: Conversational firewalls for prompts, retrieval, and responses
5. Automated Compliance: Tie security controls to compliance frameworks, making compliance a byproduct.

Conclusion

• This 5-step approach provides a comprehensive framework to build secure AI applications, addressing key OAS vulnerabilities.
• The approach emphasizes visibility, risk assessment, and the implementation of holistic security controls.
• Automating the process and aligning it with compliance requirements are crucial for successful and secure AI deployment.