Amazon’s AWS Santa Clara Summit ‘19 has been chockful of exciting product announcements, including AWS Deep Learning Containers, a service that provides Docker images that will simplify deployment of TensorFlow or ApacheMXNet workloads for training deep learning algorithms (at least according to Amazon).
The idea is to provide ready-to-eat instances that will allow end users to focus on customizations and workflows instead of configuring largely standard environments. The goal, according to AWS’s general manager of deep learning and AI, is for users to “do less of the undifferentiated heavy lifting and installing these very, very complicated frameworks and then maintaining them.”
The cloud’s key value propositions include on-demand self-service and rapid elasticity. The ability to spin up a destructible environment suitable for AI development in a few minutes is valuable, and providing those simple images is one of the ways Amazon will “make machine learning boring.”
This story is about operating-system–level virtualization (Docker containers) and deep learning, both of which will change IT. Containers are powerful tools for reducing overhead and building microservices; applications driven by deep learning are the future, and as they become simpler to build, users will begin to expect them. The democratization of cloud artificial intelligence should not be ignored. If your developers have not explored machine/deep learning (for whatever reason), now might be a good time to come aboard.
The recent Schrems II invalidation of the EU-US Privacy Shield has added a layer of difficulty for organizations that operate across borders, as they now require additional contractual clauses and measures in place to ensure data can transfer freely. Privacy program management vendor Proteus-Cyber offers a streamlined solution with the release of its Transfer Impact Assessment tool.
PHEMI is a data privacy solution focused on keeping data-processing activities secure by redacting information based on the role of the accessor. Thus, allowing such data to be used for multiple use cases without compromising privacy.
OneTrust challenges the antiquated idea of data privacy and artificial intelligence (AI) as stark opponents, with the introduction of OneTrust Athena, the vendor’s AI and robotic automation-powered platform.
Startup security vendor SECURITI.ai wins RSAC “Most Innovative Startup” at the RSA Conference 2020 Innovation Sandbox Contest.
Osano recently released its SaaS privacy solution aimed at simplifying compliance and vendor assessments. The product feels familiar, but Osano’s ethical commitment sets it apart from the crowd.
DataStealth is a difficult product to classify. It resembles DLP and privacy software but doesn’t fit neatly in either category. DataStealth focuses on data obfuscation, using a novel approach aimed at limiting sensitive-data acquisition.
TrustArc has announced the acquisition of Canadian counterpart, Nymity – a more boutique-style vendor known for its very high standard of privacy research, expertise which manifests in its product offering.
Privacy by Design (PbD) is a General Data Protection Regulation (GDPR) requirement, but effective implementation requires deep insight into the operation and interconnection of various data collection processes. Thus, PbD can be difficult to document and demonstrate. However, Proteus may help.
The US Federal Trade Commission announced both a $5-billion settlement with Facebook and a $575-million penalty against Equifax in the same week. Both were for data breaches – the Equifax case affected 147 million people, and the Facebook incident 87 million. So why is Facebook being hit with the heavier penalty?