ACERCA DE ESTE EPISODIO
Privacy does not fail only because organizations do not have policies.Many times, privacy fails because it comes too late.After the product is designed.After the code is written.After the vendor is onboarded.After the data flow is already live.After the AI use case has already started using personal data.In this podcast/session, Prabh discusses the practical meaning of Operationalising Privacy across Product, AI and Security.This session focuses on how privacy can move from paperwork to real execution inside organizations.We discuss why privacy by design fails when privacy is reviewed only after design is complete, and why privacy must be embedded into product development, engineering workflows, AI programs, security reviews, data-flow mapping, risk scoring, and day-to-day business decisions.https://www.linkedin.com/in/devika-subbaiah-infosec/In this session, we cover:- Why privacy should not appear only at the end of product design- Why privacy by design must be embedded before code is written- What Privacy Operations really means- Difference between privacy policy and privacy operations- Why DPO oversight and Privacy Ops execution are not the same role- How product, security, legal, business and privacy teams should work together- Why data flow diagrams should be living maps, not one-time documents- How vendor changes, retention changes and subprocessors affect privacy risk- Why privacy risk should not be viewed only through a legal lens- Why privacy risk and security risk must both be assessed- How dual-axis risk scoring can help evaluate organizational risk and individual harm- How an Activity-First Data Model can reduce repeated privacy documentation- How RoPA, DPIA, TIA, LIA and consent records can be generated from a common activity record- Why privacy must scale across products, functions and AI programs- Why every privacy framework field ultimately represents a real person and a real riskThe key message is simple:Privacy that only works on the day it was checked is not privacy.Organizations need privacy systems that are operational, scalable, evidence-driven, and embedded into daily decision-making.Watch the full session and comment below:What is the biggest privacy challenge in your organization — product design, AI usage, vendor risk, data mapping, privacy operations, or DPO execution?#PrivacyOps #DataProtection #PrivacyByDesign #AIGovernance #CyberSecurity #GRC #DPDP #GDPR #ProductSecurity #PrivacyEngineering #CoffeeWithPrabh
