Security, privacy, resilience, governance, and AI-risk practitioners.
12 sessions from the real MITechCon schedule
LLM features can become new attack surfaces. As AI systems move into enterprise applications, prompt injection, extraction, and jailbreaks turn from demos into real design risks. Ethan DeMott uses "Fortifying AI: Understanding Prompt Attacks and How to.
Defenders make better decisions after seeing the attacker's workflow. Most defenders have never seen an attack from the other side of the keyboard. Ken Smith uses "Certified Red Team Fundamentals" to connect the warning signs to a walkthrough of a realistic.
Municipal infrastructure needs security that respects always-on operations. The risk is easiest to reduce before it becomes an incident, so the workflow matters as much as the warning sign. In "Securing Cyber Physical Systems in Municipal Infrastructure.
AI-era security demands more than point tools. In an era where cybercrime rivals the largest global economies, securing digital landscapes has never been more critical. In "Microsoft’s Approach to Comprehensive Security in the Age of AI", David Giard moves.
Security concepts click faster when you see an attack unfold. A live attack chain makes the gap between theory and defense much easier to spot. In "Real World Cyber Attacks & How to Stop Them", Ken Smith turns each step of the attack into a practical defense.
Autonomous vehicles expand the attack surface across AI, connectivity, and control systems. Cybersecurity threats are not static; it’s a dynamic environment and constantly changing. In "Driving into the Future: Navigating Emerging Cybersecurity Threats in.
Prompting is only one layer of useful AI. Once an LLM feature reaches real users, the engineering questions move from prompts to behavior, context, risk, and reliability. "LLMs Under the Hood - Applied Engineering Workshop" lets Barry Stahl connect tokens.
Conversational BI fails when answers are not trusted. Self-service analytics only works when the data, metrics, and context behind each answer are governed. In "Designing Conversational BI: How Azure Databricks AI/BI Genie Unlocks Self-Service Insights", Mou.
Static rules create false positives and miss changing crime patterns. The hard part is keeping trust intact as volume, ownership, definitions, and expectations change. In "Applying Machine Learning and Network Analytics for Modern Financial Crime Detection".
AI security changes when systems answer in probabilities instead of certainties. When I say "probabilistic" outcomes, your mind likely jumps to coins or dice. "Probably Secure: What We Misunderstand About AI And Determinism Is Making Us Less Secure" keeps.
Enterprise investigations slow down when evidence is scattered across systems. The challenge is bringing retrieval, agent workflow, and human oversight together without burying investigators in more noise. In "Case Study: Designing AI-Augmented Enterprise.
Apache Iceberg gets powerful when you understand the table lifecycle. The value comes from seeing how snapshots, rollback, compaction, and governance fit into the full table lifecycle. "Apache Iceberg Deep Dive Workshop" turns that scenario into a workshop.
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Speakers connected to the sessions in this guide
Ken SmithDirector of Solutions and Strategic Partnerships2 sessions in this guide
Barry StahlSolution Architect and Developer1 session in this guide
David GiardMicrosoft Partner Solution Architect1 session in this guide
Dwayne McDanielDeveloper Advocate at GitGuardian and huge fan of open source1 session in this guide
Ethan DeMottAI Engineer at OneStream Software1 session in this guide
Hemanth TadepalliCybersecurity & Compliance SME at May Mobility1 session in this guide
John Selvaraj ArulappanLead AI Engineer @ADP1 session in this guide
Lester MartinTrino Developer Advocate - Starburst1 session in this guide
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