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AWS Software / SaaS Denver

CyberGRX

Application Refactoring · IT Operations Automation · Real-time Analytics · Risk Management · Security and Compliance

The legacy ML modeling solution for CyberGRX took 8 days to complete predictive modeling for all the companies in its database — 150,000 at the time. Four engineers were required to monitor and manage it, which added costs to an already compute-intensive solution.

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CyberGRX reduced the runtime of its solution from 8 days to 1 hour for its quarterly modeling, improved staff productivity, and reduced its hardware costs by 90 percent.
CyberGRX’s legacy solution prior to using AWS Step Functions took 8 days for predictive ML modeling of data from 150,000 businesses.
Improved staff productivity 1 hour runtime compared to 8 days
Modeling the predictive analysis using its legacy solution took up to 8 days and required four engineers to oversee the process.

Reported impact

Business outcome

As CyberGRX continues to iterate on and improve its solution using AWS, it provides a better experience for its customers. The company can provide data more quickly for customer use and plans to fully automate the process in the future with reduced delay. CyberGRX has taken risk assessment turnaround time for customers from 1 year or more to within 1 month, and it wants to use AWS to further reduce that to less than 1 week. “Using AWS, we have gone from a solution that was slow and expensive to one that’s lean, nimble, and incredibly fast,” says Burton. “Using AWS has been a great learning journey, a great experience, and a great accelerator to our business.” Using AWS has been a great learning journey, a great experience, and a great accelerator to our business.

  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

CyberGRX, a third-party risk management provider, wanted to improve its machine learning (ML) modeling, which processes data from hundreds of thousands of companies on a quarterly basis. Modeling the predictive analysis using its legacy solution took up to 8 days and required four engineers to oversee the process. In 2022, AWS launched AWS Step Functions Distributed Map , a high-concurrency mode for the map state capable of running 10,000 parallel workflow processes. The company can now scale its use of AWS Lambda — a serverless, event-driven compute service used to run code for virtually any type of application or backend service without provisioning or managing servers — in the background for 10,000 concurrent processes. CyberGRX saves 90 percent on compute costs by using AWS Step Functions Distributed Map. Using AWS Step Functions Distributed Map, the company can run ML modeling for 260,000 businesses in 1 hour. CyberGRX’s legacy solution

Data flow

workflow control is handled by AWS Step Functions Distributed Map, AWS Step Functions; processing runs on AWS Lambda; results and state are stored in Amazon S3 for the security analytics workload.

Solution architecture

4 components · 3 layers
  1. Compute
    • AWS Lambda Runs core compute or model execution for the security analytics workload.
  2. Storage
    • Amazon S3 Stores application, analytics, or workflow state for the security analytics solution.
  3. Orchestration
    • AWS Step Functions Distributed Map Coordinates the security analytics workflow and automation steps across services.
    • AWS Step Functions Coordinates the security analytics workflow and automation steps across services.

Architecture clues

  • In 2022, AWS launched AWS Step Functions Distributed Map , a high-concurrency mode for the map state capable of running 10,000 parallel workflow processes.
  • In the future, the company will be able to take advantage of the ability to split its data and run two distributed maps simultaneously, using the 20,000 parallel compute capacity of AWS Lambda.
  • The company can now scale its use of AWS Lambda — a serverless, event-driven compute service used to run code for virtually any type of application or backend service without provisioning or managing servers — in the background for 10,000 concurrent processes.

Evidence from the source

CyberGRX can make scalability decisions at runtime to balance its horizontal scalability, a further optimization of this solution.
CyberGRX immediately began using this new feature of AWS Step Functions to improve upon the first iteration of its solution. “AWS Step Functions Distributed Map was the exact solution that we needed to make our solution fly,” says Burton. “It has the ability to understand the size of data that you’re putting into it, and then you can make runtime decisions based on that.” With the new solution, the company can place its data in Amazon Simple Storage Service (Amazon S3) — object storage built to retrieve virtually any amount of data from anywhere — and AWS Step Functions Distributed Map parses the data and distributes it correctly.
Four engineers were required to monitor and manage it, which added costs to an already compute-intensive solution.
In 2022, AWS launched AWS Step Functions Distributed Map , a high-concurrency mode for the map state capable of running 10,000 parallel workflow processes.
The company can now scale its use of AWS Lambda — a serverless, event-driven compute service used to run code for virtually any type of application or backend service without provisioning or managing servers — in the background for 10,000 concurrent processes.
The legacy ML modeling solution for CyberGRX took 8 days to complete predictive modeling for all the companies in its database — 150,000 at the time.
Transparency Evidence & methodology 95/100 reference value · 90% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    CyberGRX customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/cybergrx-step-functions-case-study/
    Source type
    Vendor-published customer story
    Published
    Sep 18, 2023
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. Built In Colorado

    CyberGRX, Inc. Colorado Office: Careers, Perks + Culture | Built In Colorado

    Independent secondary
    https://www.builtincolorado.com/company/cybergrx-inc

    The profile lists "180 Total Employees," which maps to SMB under the 0-249 rule.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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