Hastimal Jangid
Co-Founder, RankRabbit.ai | Coozmoo - Cloud and AI Engineering
Houston, Texas, United States
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Hastimal Jangid is the product and technology leader with 15+ years of experience designing and delivering large-scale architectures for analytics, real-time data, and digital platforms. He leads the architecture and automation strategy behind the company’s AI-powered digital marketing ecosystem for small and medium-sized businesses. He engineered RankRabbit.ai, Coozmoo’s proprietary AI-driven growth platform, which scaled to $2M ARR within its first year. The platform leverages agentic AI, intelligent automation, multi-LLM orchestration, and real-time data intelligence to help SMBs improve visibility across both traditional search engines and emerging AI-powered discovery platforms such as ChatGPT, Perplexity, and Gemini. He also defined the enterprise cloud vision and automation frameworks supporting Coozmoo’s suite of AI-driven visibility and reputation management products.
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Topics
Top 15 Security Mistakes Startups Make on AWS (and How to Fix Them)
Startups move fast—and that's exactly why security is often overlooked until it's too late. From accidentally exposing Amazon S3 buckets to granting overly permissive IAM roles, small configuration mistakes can lead to costly breaches, compliance issues, and downtime.
In this session, we'll explore 15 of the most common AWS security mistakes startups make while building and scaling their applications on the AWS tech stack. You'll learn why these issues happen, understand the real-world risks they introduce, and discover practical, AWS-native solutions to prevent them.
Whether you're a founder, developer, DevOps engineer, Solutions Architect, or cloud enthusiast, this talk will provide actionable best practices that you can apply immediately to build a more secure AWS environment.
What you'll learn:
1. The 15 most common AWS security mistakes startups make
2. How attackers exploit common cloud misconfigurations
3. AWS security best practices for IAM, networking, storage, secrets, and encryption
4. How to use services like AWS IAM, AWS Organizations, AWS CloudTrail, Amazon GuardDuty, AWS Config, AWS Security Hub, AWS WAF, AWS KMS, and AWS Secrets Manager to improve your security posture
5. Practical techniques to secure applications without slowing down development
6. A simple security checklist every startup should implement before launching to production
By the end of this session, you'll leave with practical guidance, a security-first mindset, and a roadmap to help protect your AWS workloads as your startup grows.
Modern AI-powered Healthcare Data Lake on AWS
Healthcare organizations generate vast amounts of data from electronic health records (EHRs), medical imaging, laboratory systems, wearable devices, and patient applications. Turning this data into actionable insights requires a modern, secure, and scalable data platform.
In this session, we'll explore how to build an AI-powered healthcare data lake on AWS that centralizes structured and unstructured data while enabling advanced analytics and generative AI use cases. You'll learn how AWS services can be combined to securely ingest, catalog, govern, analyze, and visualize healthcare data, while maintaining compliance and preparing it for AI-driven applications.
Whether you're a cloud architect, data engineer, developer, healthcare technologist, or AI enthusiast, this session will provide practical architecture patterns and best practices for designing modern healthcare data platforms on AWS.
What you'll learn:
1. How to design a scalable healthcare data lake architecture on AWS
2. Best practices for ingesting, storing, and governing healthcare data using Amazon S3, AWS Glue, AWS Lake Formation, and Amazon Athena
3. Approaches to building secure, compliant data platforms with encryption, IAM, auditing, and fine-grained access controls
4. How to integrate generative AI and analytics using Amazon Bedrock, Amazon QuickSight, and other AWS AI services
5. Real-world healthcare use cases such as patient analytics, clinical dashboards, document summarization, and population health insights
6. Cost optimization, monitoring, and operational best practices for production-ready healthcare data platforms
By the end of this session, you'll understand how to build a secure, scalable, and AI-ready healthcare data lake on AWS that transforms raw healthcare data into meaningful insights and intelligent applications.
From RAG to Production: How SMBs Can Grow Securely with GenAI on AWS
Generative AI is transforming how small and medium-sized businesses (SMBs) operate, from intelligent customer support and document search to knowledge management and business automation. While building a proof of concept is relatively straightforward, deploying a secure, scalable, and production-ready AI application presents a different set of challenges.
In this session, we'll explore how to take a Retrieval-Augmented Generation (RAG) application from prototype to production using AWS. We'll walk through the key architectural components, discuss security and governance best practices, and examine how AWS services can be used to build reliable, cost-effective GenAI solutions that scale with business needs.
Whether you're a developer, solutions architect, startup founder, or cloud engineer, you'll gain practical insights into designing and deploying production-ready GenAI applications on AWS.
What you'll learn:
1. What Retrieval-Augmented Generation (RAG) is and why it's a preferred approach for enterprise and SMB AI applications
2. How to design a secure, scalable RAG architecture on AWS
3. Best practices for document ingestion, embeddings, vector search, and prompt orchestration
4. How to use services such as Amazon Bedrock, Amazon S3, AWS Lambda, Amazon API Gateway, Amazon Cognito, and Amazon OpenSearch Service to build end-to-end GenAI applications
5. Security considerations including IAM, encryption, access controls, guardrails, and protecting sensitive business data
6. Strategies for monitoring, optimizing costs, and operating GenAI workloads in production
7. Common pitfalls when moving from proof of concept to production and how to avoid them
By the end of this session, you'll understand the architectural patterns, security principles, and operational best practices needed to build production-ready GenAI applications on AWS that deliver business value while maintaining security, reliability, and scalability.
Cloud Careers: Combining AWS, Security, and AI Skills - Founder's Journey
The cloud industry is evolving faster than ever, and the most valuable professionals are no longer experts in just one domain. Organizations are looking for people who can combine cloud architecture, cybersecurity, and AI to build secure, scalable, and intelligent solutions.
In this session, I'll share my personal journey—from learning AWS and cloud technologies to building expertise in security, exploring generative AI, and eventually starting my own company. Along the way, I'll discuss the lessons learned, the challenges faced, and the skills that have had the greatest impact on my career.
Whether you're a student, aspiring cloud engineer, developer, security professional, or someone looking to transition into cloud and AI, this session will provide practical advice on building the right skills, gaining hands-on experience, and creating a long-term career roadmap.
What you'll learn:
1. Why AWS, cybersecurity, and AI are a powerful combination for today's technology careers
2. A practical learning roadmap for building cloud, security, and AI skills
3. How to gain real-world experience through projects, certifications, and community involvement
4. The importance of security-first thinking when designing cloud solutions
5. Lessons learned from transitioning from engineer to founder
6. Common career mistakes to avoid and strategies for continuous growth
7. How to build a strong professional brand through open-source contributions, technical content, public speaking, and networking
By the end of this session, you'll have a clear roadmap for developing in-demand technical skills, growing your career in cloud and AI, and understanding how continuous learning and community involvement can open doors to new opportunities, including entrepreneurship.
The Hidden Layer in AI Healthcare: Who Controls the LLM Recommendation?
Patients are increasingly turning to LLMs and AI systems for medical guidance before traditional healthcare channels, but these systems do not simply generate answers; they actively shape recommendations by ranking, filtering, and prioritizing medical information, providers, and treatments. This introduces a hidden recommendation layer inside AI systems where outcomes are influenced by training data, retrieval pipelines, system prompts, and safety alignment layers. In healthcare, this creates a critical security and governance challenge, as these recommendation pathways are not transparent or auditable. The talk explores how LLM recommendation stacks work, where control points exist, and why emerging risks such as prompt injection, content poisoning, and recommendation manipulation directly impact patient safety. It also highlights the governance gap in AI-driven healthcare decisions, raising the core question of who ultimately controls what AI recommends and how that control is exercised.
Hidden Facts: Training Data, RAG, Prompts, and Safety Layers Behind AI-Driven Healthcare Advice
Patients are increasingly turning to AI systems and large language models (LLMs) for medical guidance before engaging with traditional healthcare channels. However, these systems do not merely answer questions—they shape recommendations by selecting, ranking, filtering, and prioritizing information about symptoms, treatments, medications, and healthcare providers.
Behind every AI-generated response lies a set of hidden control points: training data, retrieval-augmented generation (RAG) pipelines, system prompts, and safety and alignment layers. These components collectively determine what information is surfaced, what is omitted, and how recommendations are framed. Despite their growing influence on healthcare decisions, these mechanisms remain largely opaque and difficult to audit.
This session explores how these hidden recommendation layers operate, where control and influence exist within AI systems, and why emerging risks such as prompt injection, content poisoning, and recommendation manipulation have direct implications for patient safety and trust. It also examines the widening governance gap created by AI-driven healthcare advice, where transparency, accountability, and auditability have not kept pace with adoption.
The discussion ultimately addresses a critical question for healthcare organizations, regulators, and technology providers: Who controls what AI recommends, and how can these hidden control points be made transparent, auditable, and accountable in healthcare?
Healthcare governance gap: lack of auditability and transparency in AI-driven advice
As patients increasingly turn to large language models (LLMs) and AI assistants for medical guidance before consulting traditional healthcare channels, a new governance challenge is emerging. These systems do far more than generate answers—they actively influence decisions by ranking, filtering, and prioritizing medical information, treatment options, and healthcare providers.
Beneath every AI-generated response lies a complex recommendation stack shaped by training data, retrieval mechanisms, system prompts, safety guardrails, and alignment layers. This hidden recommendation layer determines what information is surfaced, suppressed, or emphasized, yet it remains largely opaque and difficult to audit.
The session examines how AI recommendation systems in healthcare operate, where control points exist, and why vulnerabilities such as prompt injection, content poisoning, and recommendation manipulation represent more than technical risks—they directly impact patient safety, trust, and clinical outcomes. It also explores the growing governance gap created by AI-driven healthcare advice, where accountability and transparency mechanisms have not kept pace with adoption.
Ultimately, the discussion raises a fundamental question for healthcare leaders, regulators, and technologists: Who controls what AI recommends in healthcare, and how can those decisions be made transparent, auditable, and accountable?
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