Delivering AI-native security awareness training through the KnowBe4 Platform to more than 70,000 users means collaboration.
Collaboration takes many forms. Our people working together. Our departments working together. And as KnowBe4 has grown, our infrastructure, DevOps, and site reliability engineering teams have collaborated with Amazon Web Services (AWS) to streamline architectural improvements that support changing demand and simplify data sharing across products.
The Problem
As a recent AWS case study explores, the sheer size of the global employee base using KnowBe4 Security Awareness Training could lead to processing delays and backlogs. Usage peaks came at the start of the week, the month, and the business day, across the United States, Europe, and Southeast Asia.
Our engineers needed a centralized infrastructure channel to manage communication across all components and locations, enabling greater reliability and more robust integration capabilities for our customers.
What a Strong Foundation Makes Possible
The results of the AWS collaboration brings these concrete benefits to our engineering teams and ultimately our customers:
1. Two features reached production three months ahead of schedule.
2. Dozens of AI projects are now in development, each run by teams of two to four engineers supported by AI coding agents.
3. Engineering productivity on net-new development is up to 10x what it was 12-24 months ago.
Long story short: Streamlined infrastructure means lean and recurring innovation directed straight at our customers and their users.
This foundation also supports AIDA Orchestration, our autonomous AI agents that generate and deliver personalized security awareness training and personalized phishing simulations across your entire workforce, without the need for human administration.
"Building on AWS helps us ship new risk indicators faster," says Matt Duren, SVP of Engineering for AI and Data. "We're at 316 today, and the number keeps growing every year."
Keeping AI development within AWS helps our engineers get started quickly and use common development patterns across model providers. The KnowBe4 team also reinforces continuous learning through an internal certification program with incentives for engineers to earn associate-level certification within their first 12 months.
AWS AI Competency: Proof Takes a Team
Results like these are what the AWS AI Competency, recently awarded to KnowBe4, exists to recognize. The designation honors AWS Partners that demonstrate technical proficiency and proven customer success building on foundation models to automate domain-specific work.
Earning it is a collaboration of its own. The process requires a valid AWS Foundational Technical Review, evidence of AI technical controls, including a documented Responsible AI framework, and four production customer examples from the past 18 months, at least two of them public. KnowBe4's engineering, security assurance, alliance marketing, and business development teams pulled that evidence together alongside AWS partner and solutions architect counterparts.
A credential like this is a record of people on both sides asking hard questions and sharing what they know.
The Real Lesson
AWS secures the infrastructure. KnowBe4 helps secure the humans and AI agents operating within it. That is the shared-responsibility model in practice, and it only works when both sides show up.
No single vendor has all the answers, especially as AI changes how attackers and defenders work. The organizations moving fastest pair deep expertise with trusted partners.
Read the full AWS case study to see how it came together.
Find Us at AWS re:Invent
Come learn more about our collaboration with AWS and all the cool things we’re building at AWS re:Invent Nov. 30 – Dec. 4 in Las Vegas, Nevada. Attend to learn:
- Tested approaches and vetted best practices ready to put to work the week you return
- Architecture patterns and cross-industry approaches from outside your usual orbit
- A clear view of what's changing across cloud and AI and what it means for your next move
- Hands-on skills already applied in a working session, and concrete evidence to bring back to stakeholders
