Organizations are developing and deploying systems at a rate that exceeds their ability to properly secure those systems.
As a technology executive and researcher, I have witnessed similar patterns emerge throughout the technology industry; companies are rapidly adopting artificial intelligence (AI) and machine learning (ML), while using outdated security strategies to protect these emerging technologies.
These actions create both significant technical risks, and fundamentally erode the trust between an organization’s users, customers, partners and the company itself.
Neuralgebra was created to address this issue.
This is not a news aggregator, nor is it a generic technology blog. This is a workspace for me to document and analyze the realities surrounding AI Security, along with the operational challenges associated with scaling AI securely.
Ultimately, my aim is to provide a ‘missing manual’ for executives and leaders who wish to effectively govern complex machine learning systems while maintaining the velocity required by today’s rapid pace of technological innovation.
I’m an executive with experience in both academia and the government. My role as a researcher and practitioner allow me to bring a different perspective than most researchers and practitioners.
As a Practitioner: I am a Senior Technology Executive for the Public Sector.I’ve lived through what it means to implement “Digital Transformations” – I know the demands of delivery, the complexities of legacy IT environments, and the challenges of overcoming organizational inertia.
As a Researcher: I am a Doctoral candidate at the University of Pittsburgh, where I conduct empirical research to determine how we can integrate cybersecurity control elements directly into the design of artificial intelligence (AI) and machine learning (ML) systems.