Malek Ben Salem

Dr. Malek BEN SALEM

is a technology executive and the Americas Security R&D lead for Accenture. She works on building innovative AI and security technologies helping organization reduce their risk and to proactively adhere to digital ethics principles in order to earn clients’ trust—going beyond a compliance-driven security approach.

Her research covers AI and IoT security, Trustworthy AI, data protection, security analytics, and cloud and mobile security. Dr. Ben Salem holds a PhD and a MSc in Computer Science from Columbia University, New York, a Dipl.-Ing. in Electrical Engineering from the Technical University of Hanover, GERMANY, and a certificate in Technology Strategy from the MIT Sloan School of Business.

Accenture

Email: bensalem@gmail.com

DVP term expires December 2023

Presentations

AI Opportunities and Risks

Developments in machine learning, deep learning, and artificial intelligence are introducing a new age of “Smart cities” and “intelligent enterprises”. From self-driving cars to home automation, AI has the potential to be the most disruptive class of technologies during the next decade.

In this talk, I will introduce the audience to the technologies behind Artificial Intelligence and the suite of capabilities that it allows. I will discuss various applications enabled by these capabilities. In the second half of the talk, I will cover the risks associated with AI. In order to deploy AI broadly and limit any negative implications, we need to build trust betweens human and AI, by developing AI that is trustworthy, i.e. AI that is ethical, resilient, and explainable.

The New Cyberattack Surface: Artificial Intelligence - Know your threat

Organizations are increasingly using AI/ML in autonomous systems. But AI without security and safety safeguards can have nefarious consequences and can erode customers’ trust in an organization, potentially impacting its future business performance. My recent research related to secure, safe, robust, and trustworthy AI aims to equip industry practitioners with tactical and strategic tools to prevent, detect, and respond to adversarial AI attacks against their machine-learning powered systems.