Diptamay Sanyal
Principal Engineer | Data, AI & Cybersecurity Platforms | IEEE Senior | Forbes Technology Council | ex-CrowdStrike, Hubspot, Dataminr, Axon
Bayonne, New Jersey, United States
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Diptamay Sanyal is a principal engineer and data and AI platform architect with more than 20 years of experience building large-scale, real-time distributed systems across cybersecurity, public safety, and enterprise SaaS. He has designed multi-petabyte, AI-native platforms that process trillions of telemetry events daily, including production systems at CrowdStrike, HubSpot, and Dataminr. He focuses on AI-native security architectures, streaming systems, and policy-constrained automation. He is a Senior Member of the IEEE and a member of the Forbes Technology Council.
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Universal Agent Frameworks at Enterprise Scale
Lessons from building a reusable AI agent framework designed to scale across 250k enterprise customers globally in a multi-tenant enterprise platform.
Reducing Alert Noise in SOCs With Context-Driven Triage
Stop Drowning in Security Noise - A practical triage model that reduces duplicate alerts and false positives using context, reachability, and exploit-path thinking.
Real-Time Data Platforms at Trillion-Event Scale
Some of the largest real-time platforms in production today operate at scales that were considered theoretical a decade ago. Numbers like Trillion events are not unique — Security, AI agent platforms, observability backends, and large SaaS analytics systems are all pushing into the same territory. The interesting question is what actually breaks at that scale, and which architectural choices keep the system honest.
This talk distills patterns and failure modes from years of building streaming and AI data platform infrastructure, framed against publicly disclosed industry references rather than any single employer's internals. Topics include:
- Stateful streaming joins across different data streams
- Handling replay storms and out-of-order events without latency collapse
- The honest tradeoffs between latency, cost, and correctness — and when each one wins
- Observability and degradation modes that keep the platform usable when something is failing
Attendees will leave with concrete guidance on designing real-time systems that fail loudly, recover predictably, and surface the metrics that actually matter at scale.
Note: Views are my own and do not represent any employer. Examples reference publicly disclosed sources.
WeAreDevelopers World Congress 2026 - North America Sessionize Event
Diptamay Sanyal
Principal Engineer | Data, AI & Cybersecurity Platforms | IEEE Senior | Forbes Technology Council | ex-CrowdStrike, Hubspot, Dataminr, Axon
Bayonne, New Jersey, United States
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