SpeakersAgendaWhy attendPartnersVenue
Locations ā–¼

Partnerships

Register

Partnerships

Request your invite

Call to action
Your text goes here. Insert your content, thoughts, or information in this space.
Button

Back to speakers

Rahul
Sasi
CEO and Co-Founder
CloudSEK
Cybersecurity expert Rahul Sasi is redefining the cybersecurity landscape by leveraging AI (Artificial Intelligence). He is the CEO and Co-Founder of contextual AI company CloudSEK that builds products to predict cyber threats even before they occur. Rahul founded CloudSEK in 2015, when he realized that there wasnā€™t a comprehensive solution to monitor external cyber threats. He set out to build intelligent machines that can emulate human cognition, to provide contextual threat intelligence, even before an incident occurs - thus empowering organizations to anticipate and preempt large-scale cyber attacks. Rahul started his cybersecurity journey with Garage4Hackers, an online community of cybersecurity researchers, back in 2006. He spent his college days researching, programming, and contributing to the cybersecurity community. Later, he dropped out of college to pursue his passion for cybersecurity by joining iSIGHT Partners. iSIGHT Partners has since been acquired by Google. Subsequently, Rahul joined Citrix, where he was the first person without an engineering degree to be hired by Citrix India.
Button
04 June 2026 12:30 - 12:50
Securing the AI Infrastructure Layer: New Attack Surfaces and What They Break
Enterprises have deployed AI faster than theyā€™ve learned to secure it. Agents, RAG pipelines, inference APIs, and tool-calling servers are now production infrastructure yet most teams have no inventory of these assets and no monitoring over them. EASM, CSPM, and DAST were built for a different layer and donā€™t observe this one. This talk walks through the attack surfaces specific to AI infrastructure and the failure modes already being exploited: MCP servers with weak authentication that turn one compromise into broad access; inference endpoints leaking weights, system prompts, and training data; vector stores poisoned to corrupt retrieval; agentic workflows where hijacking one step becomes lateral movement; prompt injection pivoting into data exfiltration through connected tools; and model supply-chain risk from untrusted weights and fine-tunes. For each, Iā€™ll cover why it evades traditional detection, what a realistic attack chain looks like, and what defenders can instrument today. The argument is simple: this is an enumerable attack surfaceā€”it just isnā€™t being inventoried or watched yet.