Security and Governance Framework for GenAI | Protect GenAI
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Generative AI, with its immense potential, requires robust security frameworks to mitigate risks. Here's a quick breakdown of key elements: |
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Generative AI Security Framework: This framework outlines best practices for securing generative AI systems throughout their lifecycle, from data collection to deployment. It addresses concerns like model manipulation, bias, and adversarial attacks. A good example is Google's Secure AI Framework SAIF which focuses on building secure-by-default generative AI. |
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Governance Framework: This framework establishes policies and procedures for the responsible development and use of generative AI. It ensures compliance with regulations and ethical considerations. Think of it as the rulebook for generative AI projects. |
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Guardrails: These are specific controls within the governance framework that limit or prevent risky behaviors. Imagine guardrails on a bridge - they provide boundaries to keep generative AI use on track. They might include things like data access restrictions or bias detection algorithms. |
Security Framework - For Gen AI
Establish AI Governance across enterprise. Have action plan to secure data, infrastructure and Model. See more details by clicking on slides |
Governace for GenAI LLM and Chatbots | governance Framework for GenAI
We recognize the immense potential of LLMs to revolutionize various aspects of our lives. However, we also acknowledge the critical need to ensure their development and deployment are guided by ethical principles and safeguard human values. Above are guiding principles and framework for AI. It is further extended for GenAI. Click to see detail slides for personalziation, automation and creative scenario to specific governance items |
Guardrails for GenAI LLM and Chatbots
Guardrails are essentially guidelines and controls that steer the LLM's outputs in the desired direction.
Here are some ways to keep your LLM on track: Input Validation: Set criteria for what kind of information the LLM can process, preventing nonsensical or malicious inputs. Output Filtering: Review and potentially edit the LLM's outputs before they are used, catching any biases or factual errors. Real-time Monitoring: Continuously track how the LLM is being used and intervene if it generates harmful content. Human oversight: Ensure humans are always involved in the LLM interaction |
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