To address this, ReliaQuest built GreyMatter, an Open XDR-as-a-Service platform that brings together telemetry from any security and business solution, whether on-premises or in one or multiple clouds, to unify detection, investigation, response, and resilience.
In 2021, ReliaQuest turned to AWS to help it enhance its artificial intelligence (AI) capabilities and build new features faster.
Using Amazon SageMaker, Amazon Elastic Container Registry (ECR), and AWS Step Functions, ReliaQuest reduced the time needed to deploy and test critical new AI capabilities for its GreyMatter platform from eighteen months to two weeks. This increased the speed of its AI innovation by 35x.
“This innovative architecture has dramatically decreased the time to value of ReliaQuest’s data science initiatives.
Now, we can truly focus on what’s most important – developing powerful solutions to further improve the security of our customer’s environments in an ever-changing threat landscape.”
Lauren Jenkins, Snr Product Manager, Data Science, ReliaQuest
Using AI to enhance the performance of human analysts
GreyMatter takes a fundamentally new approach to cybersecurity, pairing advanced software with a team of highly-trained security analysts to deliver drastically improved security effectiveness and efficiency.
Although ReliaQuest’s security analysts are some of the best-trained security talent in the industry, a single analyst may receive hundreds of new security incidents on any given day. These analysts must review each incident to determine the threat level and the optimal response method.
To streamline this process, and reduce time to resolution, ReliaQuest set out to develop an AI-driven recommendation system that automatically matches new security incidents to similar previous occurrences. This enhanced the speed with which human analysts can identify the incident type as well as the best next action.
Using Amazon SageMaker to put AI to work faster
ReliaQuest had developed an initial machine learning (ML) model, but it was missing the supporting infrastructure to utilize it.
To solve this, ReliaQuest’s Data Scientist, Mattie Langford, and ML Ops Engineer, Riley Rohloff, turned to Amazon SageMaker. SageMaker is an end-to-end ML platform that helps developers and data scientists quickly and easily build, train, and deploy ML models.
Amazon SageMaker accelerates the deployment of ML workloads by simplifying the ML build process. It provides a broad set of ML capabilities on top of fully-managed infrastructure. This removes the undifferentiated heavy lifting that too-often hinders ML development.
ReliaQuest chose SageMaker because of its built-in hosting feature, a key capability that enabled ReliaQuest to quickly deploy its initial pre-trained model onto fully-managed infrastructure.
ReliaQuest also used Amazon ECR to store its pre-trained model images, using Amazon ECRs fully-managed container registry that makes it easy to store, manage, share, and deploy container images and artifacts, such as pre-trained ML models, anywhere.
ReliaQuest chose Amazon ECR because of its native integration with Amazon SageMaker. This enabled it to serve custom model images for both training and predictions, the latter via a custom Flask application it had built.
Using Amazon SageMaker and Amazon ECR, a single ReliaQuest team developed, tested, and deployed its pre-trained model behind a managed endpoint quickly and efficiently, without needing to hand-off to or depend on other teams for support.
Using AWS Step Functions to automatically retrain and improve model performance
In addition, ReliaQuest was able to build an entire orchestration layer for their ML workflow using AWS Step Functions, a low-code visual workflow service that can orchestrate AWS services, automate business processes, and enable serverless applications.
ReliaQuest chose AWS Step Functions because of its deep functionality and integration with other AWS services. This enabled ReliaQuest to build a fully automated learning loop for its model, including:
a trigger that looked for updated data in an S3 bucket
a full retraining process that created a new training job with the updated data
a performance assessment of that training job
pre-defined accuracy thresholds to determine whether to update the deployed model through a new endpoint configuration.
Using AWS to increase innovation and reimagine cybersecurity protection
By combining Amazon SageMaker, Amazon ECR, and AWS Step Functions, ReliaQuest was able to improve the speed with which it deployed and tested valuable new AI capabilities from eighteen months to two weeks, an acceleration of 35x in its new feature deployment.
Not only do these new capabilities continue to enhance GreyMatter’s continuous threat detection, threat hunting, and remediation capabilities for its customers, but also they deliver ReliaQuest a step-change improvement in its ability to test and deploy new capabilities into the future.
In the complex landscape of cybersecurity threats, ReliaQuest’s use of AI to enhance its human analysts will continue to improve their effectiveness. Furthermore, its accelerated innovation capabilities will enable it to continue helping its customers stay ahead of the rapidly evolving threats that they face.
About the Author
Daniel Burke is the European lead for AI and ML in the Private Equity group at AWS. In this role, Daniel works directly with Private Equity funds and their portfolio companies to design and implement AI and ML solutions that accelerate innovation and generate additional enterprise value.