Risk Management
The company’s goal is to make automation human centric by teaching machines to better understand people and using this layer of human context to help companies develop next-generation products and services, create safer environments for people, and to elevate the customer experience.
Reported impact
Using AWS, Humanising Autonomy has launched its new software-as-a-service platform to improve its customers’ experience, make engineering processes smoother, and help it grow.
Humanising Autonomy was already using Amazon Web Services (AWS) for training, researching, and refining its machine learning (ML) models. Humanising Autonomy decided to expand its use of AWS and now uses multiple services for its customer-facing website, which lets users access software and other services. The company processes a vast amount of data uploaded from CCTV cameras, in-vehicle dashcams, and other input devices. Whether it’s metadata or raw data generated from these devices, Humanising Autonomy uses AWS encryption services—such as AWS Key Management Service and AWS CloudHSM—to take measures to protect, secure, and anonymize data that it processes on behalf of its customers.
This capability is made possible by Amazon Simple Storage Service (Amazon S3), which is capable of retrieving vast amounts of data from multiple locations to allow Humanising Autonomy’s customers to connect to its software.
For example, in Nextbase dashcams, Humanising Autonomy’s software flags collision warnings and driver blind spots.
Humanising Autonomy, founded in 2017 by Maya Pindeus, Raunaq Bose, and Leslie Nooteboom, develops ethical behavior AI that is changing how machines understand people.
Humanising Autonomy’s AI software analyzes, classifies, and interprets human behavior captured from video cameras, using both live, real-time footage, and historic, pre-recorded footage.
Humanising Autonomy’s technology considers the cognitive processes people use every day to understand the social and environmental context of what’s happening around them—and translates this into a language that machines can understand.
The company’s goal is to make automation human centric by teaching machines to better understand people and using this layer of human context to help companies develop next-generation products and services, create safer environments for people, and to elevate the customer experience.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
AWS Sample Sep 16, 2024 Gen When creating Norton Genie, Gen faced several development obstacles. Its team wanted to develop a prototype within weeks instead of months, an incredibly quick turnaround. A study conducted by the Social Market Foundation found that one in five people were victims of scams between… AWS Lambda · AWS Step Functions · Amazon DynamoDB AWS Sample Sep 18, 2023 CyberGRX 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… CyberGRX reduced the runtime of its solution from 8 days to 1 hour for its quarterly modeling, improved staff… AWS Lambda · AWS Step Functions · AWS Step Functions Distributed Map 
OpenAI Sample Oct 14, 2026 SentinelOne SentinelOne needed to reverse engineer sophisticated malware far faster than specialist researchers could do manually so it could investigate more samples, spot relationships across threats, and extend scarce cyber… 8 hours to reverse-engineer malware, down from 4–6 weeks OpenAI Daybreak · OpenAI models 