The Fifth Elephant 2026 Annual Conference

Description
Enterprise AI is forcing organizations to rethink one of the most fundamental assumptions in data security.
For decades, security strategies have focused on protecting data where it is stored. Encryption, tokenization, access controls, and governance were designed for applications that queried databases and presented information to users. Generative AI changes that model completely.
Today’s AI systems reason over customer conversations, documents, support tickets, medical records, contracts, emails, and enterprise knowledge bases as the very data that contains the highest concentration of sensitive personal information. Organizations are now caught between two competing priorities: providing AI with enough context to deliver meaningful outcomes while complying with regulations like India’s Digital Personal Data Protection (DPDP) Act.
Most enterprises respond in one of two ways. They either expose sensitive data to AI models, increasing privacy and compliance risks, or aggressively redact and mask data, significantly reducing AI quality.
This talk explains why both approaches are incomplete.
Using real enterprise AI architectures including Retrieval-Augmented Generation (RAG), AI copilots, and agentic workflows, the session introduces Runtime Data Control, an architectural pattern that enables AI systems to reason over privacy-safe, semantically preserved representations of sensitive data while revealing identities only when required through policy-driven runtime access controls.
Rather than treating privacy as a compliance checkpoint that slows AI adoption, attendees will see how modern data architecture can enable organizations to securely leverage AI while maintaining governance, preserving context, and aligning with regulations such as the DPDP Act.
The session concludes with a broader perspective: enterprise AI requires us to move beyond protecting where data is stored and start governing how data is used.

Takeaways
Attendees will leave with:
A practical framework for building enterprise AI without exposing sensitive customer data.
Why traditional masking and redaction reduce AI effectiveness and what works better.
Architectural patterns for securing RAG systems, AI copilots, and agentic AI workflows.
How Runtime Data Control enables AI innovation while strengthening privacy and governance.
Practical guidance for aligning enterprise AI architectures with DPDP requirements without compromising AI performance.

Who Should Attend?
CTOs and Chief Architects
AI and ML Engineering Leaders
Platform and Data Engineering Teams
Security and Privacy Architects
Enterprise Architects
CISOs and Data Protection Leaders
Engineering teams building RAG, Copilots, or Agentic AI applications

Bio
Kalpesh Jajoo is a Solutions Architect, APAC at Skyflow, with more than 20 years of experience helping enterprises build secure, scalable, and compliant technology platforms.
He specializes in Data Privacy, Data Protection, Cloud Infrastructure, Digital Native Applications, and Enterprise AI architectures. At Skyflow, Kalpesh works closely with customers across the APAC region to solve complex challenges around protecting sensitive data while enabling AI adoption and regulatory compliance.
Prior to Skyflow, Kalpesh held engineering and architecture leadership roles at Bayer, Vodafone, and Cisco, delivering large-scale infrastructure and digital transformation initiatives. He holds a Master’s degree in Computer Networks from the UK and maintains certifications across Cisco, Microsoft, VMware, and Red Hat.
LinkedIn: https://www.linkedin.com/in/kalpeshjajoo/