Proofpoint’s people-centric cybersecurity approach
Founded in 2002 and based in Sunnyvale, California, the company’s solutions protect an organisation’s people through a suite of cloud solutions that can safeguard data and make users less vulnerable to cyber attacks in areas such as email, the cloud, web and social media.
The company counts more than half of the Fortune 1000 among its customers, and recently announced a content capturing compliance solution integrated with Microsoft Teams, to enable remote workers to continue to adhere to the rules.
Darren Lee, executive vice president and general manager of Compliance and Digital Risk for Proofpoint, said: : “Millions of users rely on digital collaboration platforms like Microsoft Teams to successfully work remotely. Proofpoint Content Capture makes it easy for organisations to ensure compliance as employees adopt these tools. And as the communications market evolves, we will continue to innovate to support customers’ changing needs.”
The company was recently named a leader in Gartner’s 2020 Magic Quadrant for Enterprise Information Archiving. “We believe our positioning as a Leader in the Gartner Magic Quadrant for the ninth consecutive year underscores our ongoing commitment to people-centric innovation that helps ensure compliance, mitigates risks, and streamlines processes while reducing costs,” said Lee.
You can read about the company’s work with 7.ai in the latest edition of our magazine. The two partner on compliance solutions, protecting customers across every channel from email, web and cloud to social media and mobile messaging. Proofpoint are able to help us with our top layer of security, to see where active threats are coming from before those attempts start trickling down into our architecture,” explains 7.ai CISO Dr Rebecca Wynn. “It means we don’t have to spend time training our personnel because we have their specialists on board providing real-time dashboards for threat analysis of our firewalls.”
What is neuromorphic AI?
AI is dead. Long live AI?
AI is evolving. The first generation of machine learning used ordinary logic and rules to draw conclusions in a very specific manner. A good example would be IBM’s Deep Blue computer, which was trained to play chess to championship standard. That hasn’t disappeared, but it has been augmented by more perceptive deep learning networks that can analyze a broader set of parameters and provide intelligent insights.
And neuromorphic AI is next?
Correct. Neuromorphic computing is a way of designing hardware – microprocessors, really – to work more like human brains. The idea is that this new iteration of AI hardware will allow machine learning of the future to deal better with ambiguity and contradiction, things that are currently difficult to process for computers.
How does neuromorphic AI work?
The problem with current chip architecture is that it is not very efficient. Because of the linearity of the process, the chips have to built with a massive amount of horsepower just in case it’s needed. Building a human brain that way would be unfeasible, so engineers have had to rethink the nature of chip design in their quest to get computers to perform more of the tasks human brains are good at. Enter SNNs.
What’s an SNN?
A spiking neural network (SNN) is, in the words of chipmaker Intel, “a novel model for arranging those elements to emulate natural neural networks that exist in biological brains.” Each ‘neuron’ fires independently, triggering other neurons only when they are required. Intel again: “By encoding information within the signals themselves and their timing, SNNs simulate natural learning processes by dynamically remapping the synapses between artificial neurons in response to stimuli.”