Satnam Singh

Satnam Singh

Chief Data Scientist

Acalvio Technologies

Satnam is a highly experienced AI professional with over two decades of expertise in product development, from conception to execution. His collaborative approach and leadership have driven successful AI strategies across various organizations, notably as Chief Data Scientist at Acalvio. He is recognized for his ability to translate complex business concepts into practical AI solutions and has earned accolades, such as being named one of India's top 10 data scientists. He has more than 25 patents and 35 Technical papers to his credit. Satnam is also active on the global stage as a public speaker and author, and he has a passion for endurance sports like Ultra Running and Rock Climbing.

In today’s AI-driven world, traditional cybersecurity isn’t enough. Generative AI systems can be exploited in new and unexpected ways—and that’s where AI Red Teaming comes in. Think of it as offensive security for your models, probing them before real attackers do.

In this hands-on session, we’ll unpack how red teaming works for GenAI: from simulating real-world attacks and prompt injection to uncovering hidden, risky capabilities. You’ll learn practical methodologies adversarial simulation, targeted testing, and capability evaluation, as well as how to operationalize them at scale.

We’ll also explore frameworks like the MITRE ATLAS Matrix, compliance alignment with NIST AI RMF and the EU AI Act, and must-know tools like Garak, PyRIT, and ART.

By the end, you’ll walk away with a practical playbook to proactively harden your AI systems, detect emerging threats, and build secure, responsible GenAI applications before adversaries get there first.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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