Kubernetes and Cloud Native Security India

As AI adoption grows, organizations are navigating new data protection, security, and governance challenges. This session explores how evolving regulations and AI security practices are shaping a more structured approach to managing AI risk.

### What We’ll Cover

• Data protection and governance challenges in the AI era
• Key regulatory developments, including India’s DPDP Act and the EU AI Act
• Emerging AI security trends organizations need to watch
• How AI-SPM, AI-DR, and AI-GRC support AI security, risk, and governance
• Aligning AI security practices with evolving regulatory requirements

Date & Time: Thursday, October 22

### Join the Session

Understand the changing compliance landscape and explore practical approaches to governing AI securely as regulations and AI adoption continue to evolve.

As banks work toward DPDP and RBI requirements, visibility into data, software, and infrastructure is key. This session explores practical ways to bring asset visibility, SBOM/XBOM coverage, data protection, and compliance together across modern and on-prem banking environments.

### What We’ll Cover

• DPDP compliance overview and key deadlines
• Asset visibility and compliance gap analysis
• SBOM, CBOM and QBOM (XBOM) coverage
• RBI compliance frameworks and requirements
• Encryption, masking, tokenization and consent management
• Self-hosted deployment for on-prem environments

Date: October 29, 2026

Join the session to understand how security and compliance teams can bring asset visibility, software and data security, and regulatory requirements together in modern banking environments.

## Securing AI Across Hybrid & Multi-Cloud

AI workloads are increasingly spread across on-premises, private cloud, and multi-cloud environments. This creates new security gaps around visibility, access, data movement, and AI-to-system interactions.
Join us for a practical discussion on securing distributed AI environments beyond traditional perimeter controls.

Agenda

• Hybrid AI Blind Spots
  Visibility across AI workloads, identities, data, and models.
• Why Perimeters Fail for AI
  Understanding new access and data-flow risks across distributed environments.
• Zero Trust for AI
  Protecting workloads, identities, and AI-to-tool interactions with LSS, eBPF, and KubeArmor.
• AI Supply Chain Visibility
  Using SBOM and xBOM to track components and dependencies.
• AI-Specific Risk Surfaces
  Securing model access, prompts, data flows, and agent-to-system communication.

Walk away with a practical reference model for evaluating and securing AI environments across hybrid and multi-cloud infrastructure.