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Generative AI Security Scoping Matrix

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Generative AI Security Scoping Matrix

Generative AI Security Scoping Matrix Cheat Sheet

  • The Generative AI Security Scoping Matrix is a framework to classify generative AI (GenAI) use cases by the level of ownership and control over the models and data.
  • It helps organizations assess and prioritize security requirements based on their generative AI deployment approach.
  • The matrix defines 5 scopes from least to most ownership and control:
    • Governance & Compliance
    • Legal & Privacy
    • Risk Management
    • Controls
    • Resilience

Generative AI Security Scoping Matrix Scope

Scopes of Generative AI Use Cases

Buying Generative AI (Low Ownership)

  • Scope 1: Consumer app

    • Uses free or paid public third-party services like ChatGPT and Midjourney.

    • No ownership or visibility into the underlying training data, models, or infrastructure. Can’t modify or fine-tune the model.

    • Direct use of APIs or UI of public services governed entirely by the service provider’s terms.

    • Security Considerations:

      • Educate employees and users on avoiding input of sensitive or proprietary information.

      • Restrict use for specific business processes if data confidentiality cannot be guaranteed.

      • Monitor usage for compliance with corporate policies.

    • Example: Employees use ChatGPT for marketing ideas.

  • Scope 2: Enterprise app

    • Uses third-party enterprise SaaS with embedded GenAI features.

    • Business relationships with vendors offer higher protection and configuration options than in Scope 1.

    • Users leverage enterprise-grade AI services embedded in software applications with some degree of corporate control.

    • Security Considerations:

      • Review and negotiate vendor agreements emphasizing data privacy, usage restrictions, and security controls.

      • Ensure contractual commitments do not allow the use of your data for additional training or model improvements without consent.

      • Implement access controls and audit logging where supported.

    • Example: Using an enterprise scheduling app with AI-generated agendas.

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Building Generative AI (More Ownership)

  • Scope 3: Pre-trained models

    • Builds applications on third-party foundation models via API.

    • Integrates models into business apps without modification.

    • Own the application and input/output data, not the base model or training data.
    • Security Considerations:

      • Apply strong identity and access controls on API usage.

      • Sanitize and filter prompt inputs to reduce injection risk.

      • Use techniques like Retrieval-Augmented Generation (RAG) to protect sensitive data by not embedding it directly in prompts.

      • Monitor usage patterns and anomalies.

    • Example: Support chatbot using Anthropic Claude through Amazon Bedrock.

  • Scope 4: Fine-tuned models

    • Fine-tunes third-party pre-trained models with proprietary data.

    • Owns fine-tuned models, training datasets, and custom configurations.
    • Creates enhanced models specialized for business needs.

    • Security Considerations:

      • Protect training data with classification, encryption, and role-based access controls.

      • Maintain audit trails of model versions and datasets used.

      • Perform rigorous testing for data leakage, output bias, and hallucinations.

      • Restrict inference to authorized users and environments.

    • Example: Marketing content generator fine-tuned on company data.

  • Scope 5: Self-trained models

    • Full lifecycle ownership, building and training generative AI models from scratch using owned or licensed data.

    • Full ownership of data, training, and model.

    • Models tailored entirely to organizational or industry-specific use cases.
    • Security Considerations:

      • Implement enterprise-grade governance covering data sourcing, model training infrastructure, and deployment.

      • Employ zero-trust architectures for cloud compute and data storage.

      • Continuously assess and address model risks such as adversarial attacks, data poisoning, and extraction threats.

      • Establish AI ethics reviews and compliance audits.

      • Plan for disaster recovery and resilience through checkpoints and multi-region availability.

    • Example: Industry-specific video generation AI trained from ground-up.

Core Security Disciplines Across Scopes

  1. Governance & Compliance

    • Establish a cross-functional AI governance committee including legal, compliance, security, HR, and product teams.

    • Define clear AI usage policies covering acceptable use, data handling, regulatory compliance, and ethical guidelines.

    • Implement transparency and explainability protocols for model decisions, including automated metadata capture, audit trails, and model behavior and data provenance documentation.

    • Maintain thorough AI-generated content records including source data, generation timestamps, and modification history to meet compliance audit requirements.

    • Employ continuous monitoring and auditing to detect policy violations, ethical risks, and compliance gaps.

    • Align with standards such as the National Institute of Standards and Technology (NIST) AI Risk Management Framework and industry-specific regulations.

    • Promote ongoing training and awareness programs for all stakeholders on responsible AI use and evolving compliance mandates.

    2. Legal & Privacy

    • Conduct detailed privacy impact assessments (PIAs) before deploying generative AI solutions.

    • Implement data minimization strategies and rigorous data masking, tokenization, and de-identification of personal or sensitive data before model training or inference.

    • Establish robust contracts and data processing agreements with third-party AI providers to clarify responsibilities for data protection and IP rights.

    • Address the right to be forgotten by defining processes for data removal or retraining models to exclude specific data.

    • Monitor and enforce compliance with global privacy laws (GDPR, HIPAA, CCPA) considering cross-border data flows in AI use.

    • Include intellectual property considerations relating to data inputs and AI-generated outputs.

    3. Risk Management

    • Perform comprehensive threat modeling and risk assessments focused on GenAI-specific risks such as prompt injections, model hallucination, data poisoning, adversarial attacks, data leakage, and bias amplification.

    • Develop and apply risk-based controls and guardrails aligned with risk tolerance and use case sensitivity.

    • Include human oversight mechanisms at critical decision points or high-impact AI outputs to mitigate errors or unintended consequences.

    • Use anomaly detection and monitoring to identify unusual or malicious AI usage patterns in real time.

    • Regularly test AI models for biases, fairness, accuracy, and robustness using AI fairness and security assessment tools.

    • Incorporate incident response playbooks specific to AI, addressing breaches, misuse, or unsafe outputs.

    4. Controls

    • Implement least privilege access control with strong IAM policies for AI model and data resources.

    • Use end-to-end encryption for data at rest and in transit, leveraging customer-managed keys where possible.

    • Integrate input validation and sanitization to prevent injection attacks or harmful prompt manipulation.

    • Apply output filtering and moderation to mitigate harmful, biased, or disallowed content generation.

    • Employ API management tools to enforce rate limiting, usage quotas, and authentication for model endpoints.

    • Use Retrieval-Augmented Generation (RAG) and watermarking techniques to protect sensitive data and identify AI-generated content.

    • Configure comprehensive audit logging for model access, invocations, changes, and data handling actions.

    5. Resilience

    • Design the AI architecture for high availability and fault tolerance using cloud-native tools like failover, load balancers, and region redundancy.

    • Implement checkpointing and version control for AI models and training datasets to enable rollback and recovery.

    • Maintain disaster recovery plans that cover AI infrastructure, data loss scenarios, and operational continuity.

    • Conduct load and stress testing to understand system behavior under high inference or training demand.

    • Ensure operational excellence by monitoring system health, latency, accuracy, and throughput.

    • Adopt continuous improvement cycles, including regular retraining, patching, and security hardening, aligned with AWS Well-Architected Framework’s guidelines.

Generative AI Security Scoping Matrix References:

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Written by: Nikee Tomas

Nikee is a dedicated Web Developer at Tutorials Dojo. She has a strong passion for cloud computing and contributes to the tech community as an AWS Community Builder. She is continuously striving to enhance her knowledge and expertise in the field.

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