A deep technical exploration of how to implement Zero Trust across AWS workloads using IAM, STS, IRSA, VPC Lattice, PrivateLink, GuardDuty, CloudTrail, Security Hub, and AWS Verified Access. This episode breaks down identity-centric security, micro-segmentation, continuous verification, workload isolation, and automated response patterns for modern cloud-native architectures on AWS.

Identity has become the primary attack surface in modern cloud environments. This episode explores how attackers abuse IAM, STS, IRSA, and trust relationships to move laterally across cloud workloads—and how to detect, contain, and respond using AWS-native services like Amazon GuardDuty, AWS CloudTrail, Amazon EventBridge, and AWS Step Functions. A deep technical dive into identity-driven defense, automated containment, and Zero Trust architecture in AWS.

In this episode, we take a deep, technical dive into how modern AWS environments can move beyond detection into fully engineered, automated containment.

Using a real-world runtime scenario, the episode walks through how Amazon GuardDuty findings are ingested via Amazon EventBridge, enriched with Kubernetes and IAM context, and orchestrated through AWS Step Functions for deterministic response. It covers evidence preservation using Amazon S3 with AWS Key Management Service, controlled quarantine strategies across EKS and EC2, and identity containment using IAM boundaries and IRSA.

This is a hands-on blueprint for building cloud-native runtime security workflows—where detection, response, and auditability operate as a single, resilient system.

Ideal for CISOs, cloud architects, and security engineers looking to reduce blast radius, improve response time, and operationalize security at scale in AWS.

This episode breaks down how to combine identity controls (IAM, IRSA), network telemetry (VPC Flow Logs, DNS), and deep runtime visibility (process behavior, containers, Lambda execution) to detect and contain threats in real time. It also explores practical AWS-native approaches using services like GuardDuty, CloudTrail, Security Hub, EventBridge, and Step Functions to move from reactive monitoring to automated, intelligence-driven response.

A must-listen for architects and CISOs looking to reduce blast radius, improve detection fidelity, and build truly resilient, cloud-native security operations.

A deep dive into how AWS Security Agent is transforming cloud security from reactive monitoring to autonomous, intelligence-driven operations. This episode explores identity attack paths, real-time threat containment, and how services like IAM, GuardDuty, CloudTrail, and Security Hub come together to enable context-aware, self-healing security at scale.

In Preparing AWS Workloads for the Post‑Quantum World – Part 2, Pradeep Rao moves from strategy to execution, outlining a practical AWS‑native operating model for post‑quantum readiness. The episode focuses on workload prioritization, encryption‑in‑transit, trust chains, testing and phased rollout, and how to adopt post‑quantum cryptography using managed AWS services such as KMS, ACM, and Private CA—without triggering large‑scale disruption. Designed for CISOs, security leaders, and cloud architects, this episode translates post‑quantum risk into actionable steps.

In this episode, Pradeep Rao speaks about the emerging post‑quantum risk to cloud‑based cryptography and what it means for AWS‑hosted workloads. This Podcast focuses on the real‑world impact of “harvest now, decrypt later” attacks, the exposure of long‑lived encrypted data, and how AWS is embedding post‑quantum cryptography into foundational services such as AWS KMS, AWS Certificate Manager, CloudFront, and Secrets Manager. Part 1 establishes the security and risk context, clarifies shared responsibility, and outlines how post‑quantum readiness should be approached as a cloud security architecture decision—not a future crisis response.

Host is Pradeep Rao => https://www.linkedin.com/in/rao-pradeep/ 

AI/ML systems introduce an entirely new attack surface — from poisoned training data and model theft to insecure inference endpoints and privilege escalation inside ML pipelines.

In this episode of the AWS Expert Series Podcast, we take a CISO and Principal Engineer–level deep dive into securing AI/ML workloads on AWS. We explore identity boundaries in Amazon SageMaker, protecting data in S3-based feature stores, securing model artifacts, isolating training jobs, hardening inference endpoints, and implementing zero trust across the ML lifecycle.

If you’re building or governing AI on AWS, this episode goes beyond basics and into the real architectural and security decisions that matter.

Host is Pradeep Rao => https://www.linkedin.com/in/rao-pradeep/ 

Zero Trust on AWS goes far beyond IAM policies and MFA. This episode dives into identity propagation, workload compromise, and real-world attack paths—revealing how to design AWS environments that limit blast radius after credentials are exposed.

Host is Pradeep Rao [ https://www.linkedin.com/in/rao-pradeep/ ]