
Research Interview
Security has spent years trying to influence engineering through policies, champions, and persuasion, and mostly losing. Platform engineering changes that equation.
In this episode, Sam talks with Giovanna Faso, VP of Engineering at Dashlane, about why security is one of the fastest-growing forces inside the platform engineering movement, and why “begging teams to care” no longer works at scale.
Drawing on years of leading platform and security-adjacent teams, Gio explains how platform engineering turns security from a social problem into a structural one: baking guardrails into templates, standardizing incident response, and replacing tribal knowledge with shared systems that actually stick.
They unpack the real gaps between DevOps and platform engineering, why tooling was never the hard part, and how centralized platforms finally make DevSecOps operable in large, complex organizations. The conversation also explores the human side of security; the burnout, regulatory pressure, and legacy risk security teams carry, and why better relationships matter as much as better architecture.
In the second half, they zoom out to AI: why security teams are rightly cautious, why experimentation feels chaotic right now, and how platform engineering can be the stabilizing layer between “too strict” governance and reckless AI adoption.
In this episode:
Why security champion programs usually fail
How platform engineering removes security from persuasion politics
DevOps vs platform engineering: principles vs operating models
Incident response, standardization, and the end of tribal knowledge
Why security teams are perceived as “too strict”, and what they’re actually carrying
How to align developers, product managers, and security around shared constraints
AI governance, guardrails, and why “it’s all happening too fast”
Platform engineering as the human interface to security complexity
FEATURED GUESTS
State of Platform Engineering Report: Volume 4
state-of-platform-engineering-report-volume-4
Reference architecture for an AI/ML Internal Developer Platform on GCP
reference-architecture-for-a-data-ai-internal-developer-platform-on-gcp
Reference architecture of an Internal Developer Platform on Azure
reference-architecture-of-an-internal-developer-platform-on-azure
Reference architecture of an Internal Developer Platform on GCP
reference-architecture-of-an-internal-developer-platform-on-gcp
Cloud Development Environments (CDEs) for Platform Engineers
cloud-development-environments-for-platform-engineers
Kubernetes cluster lifecycle management for platform engineers
kubernetes-cluster-lifecycle-management-for-platform-engineers
State of AI in Platform Engineering 2025
state-of-ai-in-platform-engineering-2025
Observability for platform engineers
observability-for-platform-engineers
Reference architecture of an Internal Developer Platform on AWS
ref-arch-aws