🔐 Incident Response for Generative AI Workloads: A Structured Approach by AWS
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Amazon Web Services (AWS) outlines a structured approach for incident response in Generative AI workloads, emphasizing both response methodology and core application components essential for secure operations.
📊 Key Components of the AWS Methodology:
Understanding where and how incidents may occur requires clarity on both the architecture and behavior of generative AI systems. AWS outlines 7 critical areas to focus on during incident response:
1️⃣ Access: Monitor and analyze access patterns to detect anomalies or unauthorized access to the GenAI application.
2️⃣ Infrastructure Changes: Investigate modifications to infrastructure components like servers, databases, and serverless environments.
3️⃣ AI Changes: Examine if users accessed or altered GenAI models, application guardrails, or configurations.
4️⃣ Data Store Changes: Detect unauthorized access or changes in training datasets, knowledge bases, and data repositories.
5️⃣ Invocation: Analyze GenAI prompts and file inputs for anomalies, including prompt injections or malicious activities.
6️⃣ Private Data: Ensure no unauthorized access or tampering of sensitive or confidential data.
7️⃣ Agency: Evaluate application permissions to prevent excessive or unintended actions on behalf of users.
🛠️ Generative AI Application Components:
🤖 Foundation Models: Large-scale AI models trained on diverse datasets, serving as a base for AI applications. (Focus: Access & AI Changes)
🛡️ Custom Models: Fine-tuned models tailored to an organization’s specific data. (Focus: AI Changes)
⚠️ Guardrails: Mechanisms to ensure AI operates within defined boundaries. (Focus: AI Changes & Access)
🤝 Agents: Enable workflows for multistep tasks across systems and data sources. (Focus: Agency & Invocation)
📚 Knowledge Bases: Domain-specific repositories accessible by AI applications. (Focus: Data Store Changes)
📊 Training Data: Data used for model training, fine-tuning, and RAG techniques. (Focus: Data Store Changes & Private Data)
🔌 Plugins: Software components extending functionalities and integrations with external services. (Focus: Invocation & Access)
📚 Source: AWS Blog — Methodology for Incident Response on Generative AI Workloads by Anna McAbee, Jennifer Paz, @Anthony Evans, and Steve de Vera. https://aws.amazon.com/blogs/security/methodology-for-incident-response-on-generative-ai-workloads/
💡 Inspiration: A special shoutout to Thomas Roccia for sparking the idea behind this post.