Redefining security for the cloud AI era (AIM111)

Adoption Trends and Security Considerations for Cloud AI Applications

Adoption Trends

  • Over 60% of customers have started using generative AI (Gen AI) applications or large language model-based applications within their environment.
  • Over 50% don't have appropriate security policies or haven't started implementing security policies for these Gen AI applications.
  • The adoption curve is expected to increase exponentially over the next 3 years, with projections of over $300 billion in software spend on AI services and software.

Security Considerations

  1. AI Model Safety:

    • Prioritize safety, transparency, fairness, and robustness of AI models.
    • Ensure models are free from unintended biases and can withstand adversarial attacks.
    • Maintain ethical and legal standards required by emerging regulatory frameworks.
  2. Data Governance:

    • Ensure governance and access controls for the fine-tuned, internal, and proprietary data used to train the models.
  3. Infrastructure Security:

    • Protect the infrastructure, whether running in the cloud or on-premises, from adversaries.
  4. Privacy Concerns:

    • Address data anonymization, GDPR compliance, minimizing data exposure, and implementing access controls for developers.
  5. Informal Adoption Process:

    • Developers or users may start using Gen AI applications without formal adoption processes, creating risks outside of established security controls and governance.
  6. Real-World Vulnerability Example:

    • An authentication bypass vulnerability in a large language model application allowed attackers to access sensitive information.

Shared Responsibility Model

  • The shared responsibility model for AI applications includes the AI platform, the AI application, and the AI data in use.
  • Depending on the deployment model (build your own, AI platform as a service, or AI SaaS), the customer's security responsibilities vary.

Implementing Security

  • The AWS framework outlines the top 10 risks to consider for large language model applications, including input/output security, data security, and supply chain vulnerabilities.
  • Securing the AI application during development, managing the application's posture, and monitoring threats are crucial steps.

Securing the AI Development Pipeline

  • Scan container images for vulnerabilities, including AI/ML framework-specific vulnerabilities.
  • Perform dynamic container analysis to detect behavioral anomalies.
  • Scan serverless functions and virtual machine images as part of the supply chain.

Securing the AI Runtime

  • Develop AI governance and culture to educate users on acceptable use and risks.
  • Implement access controls and data protection measures.
  • Gain visibility into cloud service configurations and runtime protection for AI models.
  • Utilize unified AI posture monitoring to track vulnerabilities, compliance, and attack paths.

Best Practices for AI Security Strategy

  1. Supply Chain Governance
  2. Secure Development Lifecycle
  3. Transparency and Responsibility
  4. Adversarial R&D and Proactive Defense
  5. Train the Security Operations Team

Conclusion

  • Crosstalk integrates with AWS services to provide comprehensive security for AI applications, including deployment, runtime protection, and monitoring.
  • Additional resources are available, including the Global Threat Report and Crosstalk's own AI-powered security solutions.

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