privacy-by-design
- AI Strategy (315)
- Generative AI (106)
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- Leadership (76)
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How Financial Product Teams Can Earn Consumer Trust
A practical framework for designing, measuring, and governing trust across high-stakes financial journeys without reducing it to a brand score.
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How to Evaluate Corporate Data for AI Training Deals
A practical framework for testing enterprise data rights, quality, privacy, model fit, and economic value before committing to an AI training deal.
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A Practical Privacy Control Model for AI Agent Trace Analytics
A practical control model for collecting useful AI agent traces while limiting exposure across capture, redaction, access, exports, and retention.
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How to Build Governance Infrastructure for Autonomous AI Agents
A practical control-plane blueprint for identifying agents, limiting delegated authority, monitoring behavior, and preserving recourse.
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AI Agent Data Privacy: A Product Leader’s Launch Playbook
A launch-ready framework for mapping AI agent data flows, minimizing collection, redacting traces, setting retention, and testing every control.
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Persistent Memory for AI Assistants: A Product Playbook
A practical guide to deciding what an AI assistant should remember, operating its memory lifecycle, and measuring whether users can trust it.
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Persistent Memory for AI Agents: A Product Leader’s Guide
A practical framework for deciding what an AI agent should remember, how it should retrieve and forget memories, and how to test the value safely.
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How to Design Production AI Guardrails for Sensitive Workflows
A practical control-plane design for routing sensitive data, constraining agent actions, enforcing approvals, testing failures, and preserving auditability.
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How to Build Trustworthy AI Diagnostics for Women’s Health
A practical framework for defining clinical scope, communicating uncertainty, preventing automation bias and governing sensitive health data.
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AI Product Leadership: Faster Learning, Safer Systems
A practical framework for improving product discovery and delivery while matching AI evaluation, privacy, and governance to real-world risk.
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Migrate Analytics Platforms Without Chaos: 7 Proven Lessons to Plan, Move, and Land Cleanly
Migrating analytics platforms doesn’t have to derail roadmaps or erode trust. I share seven battle-tested lessons—shaped by work with Human37 and Amplitude—that help teams align on…
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Package Hack Wake-Up Call: My Playbook for Securing Cowork, Coding Agents, and Secrets
A recent wave of malicious package hacks pushed me to level up my security posture without slowing down my AI workflows. In this piece, I break…
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A Product Leader’s Playbook for Humane, Sustainable Growth
A practical operating model for balancing product growth with user agency, trust, community impact, and durable business value.
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Governed AI Analytics in Financial Services: A Playbook
A practical operating model for deploying AI analytics with clear data boundaries, human review, audit evidence, and measurable business value.
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How to Scale Session Replay Without Sacrificing Privacy
A practical operating model for scaling session replay with strict capture controls, performance budgets, targeted sampling, and measurable governance.
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Amplitude MCP: Evidence-Grounded AI Workflows for Product Teams
A practical playbook for grounding product decisions, bug investigations, and AI-generated changes in Amplitude behavioral evidence, with clear decision gates.
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AI Product Data Security: A Practical Playbook for PMs
A practical playbook for mapping AI data flows, assigning risk, selecting vendors, and embedding security controls without blocking delivery.
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How to Build Agentic AI for Product Analytics and Support
A practical model for connecting behavioral data, safe agent actions, and outcome measurement so product support resolves issues without losing control.
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Product Experimentation for AI Systems: A Practical Playbook
A practical playbook for testing prompts, retrieval, and policy changes with decision-ready metrics, safe rollouts, and reliable instrumentation.
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How to Ship Responsible AI Products in Regulated Healthcare
A practical delivery model that turns healthcare AI data boundaries, evaluation, staged rollout, and production monitoring into release decisions.
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Browse topics
- AI Strategy (315)
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