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AI & DataAutomationCloudConsulting & EngineeringData CenterDigitalImplementation ServicesIT Spend OptimizationLab HostingMobilityNetworkingSecurityStrategic ResourcingSupply Chain & Integration
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EnergyFinancial ServicesGlobal Service ProviderHealthcareLife SciencesManufacturingPublic SectorRetailUtilities
Learn from us
Hands on
AI Proving GroundCyber RangeLabs & Learning
Insights
ArticlesBlogCase StudiesPodcastsResearchWWT Presents
Come together
CommunitiesEvents
Who we are
Our organization
About UsOur LeadershipLocationsSustainabilityNewsroom
Join the team
All CareersCareers in AmericaAsia Pacific CareersEMEA CareersInternship Program
Our partners
Strategic partners
CiscoDell TechnologiesHewlett Packard EnterpriseNetAppF5IntelNVIDIAMicrosoftPalo Alto NetworksAWSGoogle CloudVMware
The ATC

Jason Ludwig

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World Wide Technology

Principal Solutions Architect

3 Followers

Contributions

5 Total

2 Articles

2 Events

1 Lab

Contributions

5 Total

2 Articles

2 Events

1 Lab

Areas of Expertise

  • Cisco AI Defense
Bio
Contributions

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About Jason

Jason is a Principal Architect with over 25 years of experience in software engineering, architecture, and AI development. He specializes in AI security and machine learning systems, with deep expertise spanning LLM safety and red-teaming, embedded and industrial AI, real-time predictive analytics, and AI-driven cybersecurity platforms. His active research programs focus on agentic coding attack surfaces and defenses, LLM backdoor detection, model fingerprinting and tampering detection, and high-efficiency inference optimization for quantized KV caches. Jason's work has been featured in Wired and at TED Talks. He has led engineering teams at top global organizations including World Wide Technology, Bayer, Anheuser-Busch InBev, Mastercard, and Monsanto. He is an active presenter at Black Hat, Def Con, and Cisco Live, where he covers AI safety, security, and adversarial attack/defense techniques.

Recent Achievements

Retrieval Augmented Generation (RAG) Security Learning Path
Retrieval Augmented Generation (RAG) Security Learning Path
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