Andy has been appointed as the Regional Director for Cylance with an initial focus on ANZ as the launch pad into AsiaPac. Until recently he was the SVP Business Development at the Telstra Software group where he was accountable for strategy and go to market for Telstra investment is Media and IoT. Previously at Telstra he led Product Team for Managed Services and Industry Solutions and ran the Telstra Security business. With over 20 years’ experience in the industry; he is a globally recognised expert in security and systems management and is a respected member of several high profile government and industry advisory boards including The Critical Infrastructure Advisory Board to the Attorney General.
Andy has held a range of high level management positions in the security industry, including as Regional Director for RSA in ANZ. Prior to RSA, he was based in the US and was VP and CTO of the Enterprise Business Units of SafeNet, a security solutions company specialising in encryption technologies.
He has also served as General Manager for Senetas, a group of companies specialising in the delivery of Information Technology services to the finance and government sectors, as well as being the first Tivoli Architect at IBM to be certified in the APJ region.
Real World Application of Machine Learning - Prevent and Protect vs Detect and Respond
With today's security solutions, most government and businesses are trying to conquer the evolution of malware with the old "tried-and-true," detect and respond approach. The detect and respond strategy is outdated and based upon executing malware and observing the post-execution actions of network traffic to ensure the destination isn't already on a blacklist, or by scanning the activity on the endpoint and verifying the presence of signatures. The major flaw with this approach is the fact that the technique is reactive in nature and does not prevent execution, or detect new malware never seen in the wild. Unlike the detect and respond approach, protection is proactive and stops the malware it before it can execute. Prevention can be achieved through the use of artificial intelligence and machine learning and identify the malware and neutralize it before it can execute.