While having your own custom model built and tuned in-house with your private data in a private cloud or on-prem is the core foundation of private AI, you need to be able to manage and maintain all the interactions between your LLMs and your employees or departments within your organization to further secure your documents and your GenAI LLMs.
How do you ensure that prompt injections and jailbreak attempts to trick your tuned LLMs are under surveillance via centralized monitoring?
How do you ensure that you are monitoring the quality of the answers by the GenAI applications, as measured by your user feedback?
It is preciely in circumstances like these that the SecureLLM function of DKubeX comes in handy.
It monitors and logs every interaction with your LLM during training and deployment. It captures alerts on prompt injections and jailbreak attempts. It manages your OpenAI keys in a vault, and monitors the quality of the answers.
SecureLLM is a crucial feature of DKubeX designed to enhance the security of using LLM models as a service, such as those provided by OpenAI or Anthropic. It focuses on centralizing the management and distribution of API keys to authorized employees, offering an additional layer of security beyond physical and cyber infrastructure safeguards.
SecureLLM actively monitors and logs every interaction with your LLM model during both training and deployment phases. It has the capability to capture alerts on prompt injections and jailbreak attempts, helping to identify and mitigate potential security risks effectively.
SecureLLM takes charge of managing your API keys for services like OpenAI by storing them in a secure vault. This ensures that access to these critical keys is restricted to authorized personnel, reducing the risk of unauthorized usage.
Cloud-based computing has enabled organizations to make use of high-performance resources without requiring large IT groups. And it has enabled a supply of production-ready applications to companies who might not otherwise be able to access them. But, what if your organization can’t make use of the public cloud?
How to deploy a preferred version of a model into production or to first push it to a model catalog for another gatekeeper to test and select a preferred model before pushing into production. The model catalog capability is unique to DKube and minimizes accidental escape to production of a model that may not be ready yet.
Want to learn how to monitor your models in production? The DKube platform integrates model monitoring into the overall system with DKube Monitor. It includes everything necessary for engineers and executives to identify how well your models are achieving their business goals - and facilitates a smooth workflow to improve them when necessary.