https://speakerdeck.com/aloktibrewala 2026-04-12 00:18:29 -0400 Presented at Chandigarh University in collaboration with IEEE and IEEE DataPort, April 2026. Humanitarian crises affect 245 million people worldwide, and AI has the potential to accelerate response, but only if the data layer is solid. This talk explores how IEEE DataPort can serve as the foundation for building secure, data-driven humanitarian AI solutions. Topics covered: - Why humanitarian AI matters now — scale of need (UNICEF, UN OCHA, WMO data) - Why AI fails when the data layer is weak — 32% of crisis data is outdated or unavailable - IEEE DataPort as a platform for citable, reproducible, governed research datasets - Reference architecture for humanitarian AI pipelines aligned with NIST AI Risk Management Framework - Real-world use cases: disaster damage mapping and flood detection using IEEE DataPort datasets - FAIR principles (Findable, Accessible, Interoperable, Reusable) for publishing humanitarian data - Privacy as a non-negotiable — UNHCR data protection frameworks and NIST Privacy Framework Presented at Chandigarh University in collaboration with IEEE and IEEE DataPort, April 2026. Humanitarian crises affect 245 million people worldwide, and AI has the potential to accelerate response, but only if the data layer is solid. This talk explores how IEEE DataPort can serve as the foundation for building secure, data-driven humanitarian AI solutions. Topics covered: - Why humanitarian AI matters now — scale of need (UNICEF, UN OCHA, WMO data) - Why AI fails when the data layer is weak — 32% of crisis data is outdated or unavailable - IEEE DataPort as a platform for citable, reproducible, governed research datasets - Reference architecture for humanitarian AI pipelines aligned with NIST AI Risk Management Framework - Real-world use cases: disaster damage mapping and flood detection using IEEE DataPort datasets - FAIR principles (Findable, Accessible, Interoperable, Reusable) for publishing humanitarian data - Privacy as a non-negotiable — UNHCR data protection frameworks and NIST Privacy Framework Sun, 12 Apr 2026 00:00:00 -0400 https://speakerdeck.com/aloktibrewala/leveraging-ieee-dataport-for-humanitarian-ai https://speakerdeck.com/aloktibrewala/leveraging-ieee-dataport-for-humanitarian-ai Presented at OWASP Boston Application Security Conference (BASC) 2026. AI coding agents don't just autocomplete, they plan, execute, install dependencies, and make security-sensitive implementation choices autonomously. This talk covers how to threat model your development workflow when an AI agent is a system actor in your pipeline. Topics covered: - Why AI-generated code is insecure 45% of the time and why bigger models don't help - Four new trust boundaries (B1–B4) introduced by AI in the dev pipeline - OWASP Agentic Top 10 mapped to real-world attack scenarios - Slopsquatting, memory poisoning, and cascading multi-agent attacks - A practical threat model template and 5-layer validation pipeline - Org-level actions for AI code security Presented at OWASP Boston Application Security Conference (BASC) 2026. AI coding agents don't just autocomplete, they plan, execute, install dependencies, and make security-sensitive implementation choices autonomously. This talk covers how to threat model your development workflow when an AI agent is a system actor in your pipeline. Topics covered: - Why AI-generated code is insecure 45% of the time and why bigger models don't help - Four new trust boundaries (B1–B4) introduced by AI in the dev pipeline - OWASP Agentic Top 10 mapped to real-world attack scenarios - Slopsquatting, memory poisoning, and cascading multi-agent attacks - A practical threat model template and 5-layer validation pipeline - Org-level actions for AI code security Sun, 12 Apr 2026 00:00:00 -0400 https://speakerdeck.com/aloktibrewala/threat-modeling-development-workflows-with-autonomous-code-generation https://speakerdeck.com/aloktibrewala/threat-modeling-development-workflows-with-autonomous-code-generation