Venafi taps generative AI to streamline machine identity management
As security professionals struggle with an overwhelming amount of machine identities owing to the shift to virtualization, Venafi is turning to generative AI to help them solve machine identity management problems faster and easier, via a natural language interface and automated code generation.
The company has added a large language model (LLM) layer to the control plane of its namesake SaaS offering, calling it “Venafi Athena,” and enabling the system to be used from a chat interface, according to Kevin Bocek, the company’s vice president for ecosystem and community.
Venafi Athena, available now at no extra cost to users, is also available as open source code on Hugging Face for community projects, according to Bocek.
“Venafi’s Athena enables identity professionals to optimize their procedures using a natural language interface,” said Jack Poller, senior analyst at Enterprise Strategy Group. “This is very important as operational efficiency is becoming the keyword for the year as IT teams are told they must do more with less in this demanding economic climate.”
Additionally, Athena’s LLM was trained and tested on Venafi proprietary data as well as data from multiple external LLMs, including models available as-a-service and open source, and fine-tuned using Venafi’s machine identity knowledge. The depth of training that Athena has had, which includes training on infrastructure as code (IaC) tools such as Anisble and Terraform, should help the software avoid hallucinations — incorrect information given by generative AI systems — regarding virtual machine identity and management, according to Pollard.
Athena automates code generation, operations
Venafi said that Athena leverages generative AI to make it easy for platform and developer teams to automate machine identity operations by generating and suggesting complete code recipes.
