Privacy & Compliance / AI Governance / Greater Seattle
Chris Kendrick
Global privacy, compliance, and governance leadership
Privacy and compliance leadership for the agentic AI era.
Across 19 years in law, product, and global program operations, including more than six years building and transforming privacy and risk-review organizations at Meta, I've led global regulatory compliance, privacy-by-design, and audit-ready governance for products used by billions. I also work hands-on with generative AI and multi-agent systems. That combination shapes my approach: build governance that makes advanced systems accountable while preserving clear human judgment, responsibility, and agency.
i.
Privacy and compliance at scale
Built and scaled global privacy operations through hypergrowth
What
Helped build and scale a global privacy-review organization while owning privacy incident and breach response and global regulatory-compliance work.
Scale
Grew the organization from roughly 35 to 75 people in a single year; conducted roughly 70 interviews and developed new managers while covering major leadership gaps.
How
Partnered with Legal and Security on GDPR, UK GDPR, CCPA, breach analysis, regulatory reporting timelines, data-subject notification, and cross-border data-transfer mechanisms while strengthening hiring, calibration, coaching, and organizational design.
Result
Promoted from Senior Manager to Director within the first year for outsized impact; the team was described in the promotion rationale as being in a completely different state than when the year began.
Turned around privacy review for a generative-AI research organization
What
Inherited a privacy-review function for a high-priority AI research organization that had escalated to senior leadership.
Scale
Reduced decision turnaround time by more than half within one quarter while enabling more than 100 generative-AI product launches.
How
Redesigned staffing, deployed additional resources, rebuilt researcher relationships, introduced a decision-tree framework, and partnered with Legal to clarify dataset guidelines and reduce unnecessary manual review.
Result
Moved the program from executive-escalation status to steady state; the decision framework became a template for related programs.
Designed an audit-ready enterprise risk-review system
What
Co-founded a cross-organizational pillar coordinating risk-review decisions across multiple product groups serving billions of users.
Scale
Organized review forums spanning five risk pillars and synthesized roadmap inputs from five workstreams into one operating plan.
How
Defined decision rights, authored the canonical system design and runbooks, facilitated cross-functional governance, and coordinated audit readiness.
Result
Cleared a major operational milestone with the inaugural internal audit closing with only three minor findings.
Rebuilt privacy partnership for hardware and AR products
What
Took over the privacy-review pillar for a hardware and augmented-reality product organization with a fractured cross-functional relationship.
Scale
Led a multi-year partnership across hardware, software, infrastructure, and growth with roughly 120 extended reports at peak.
How
Built joint roadmaps, created a weekly product forum, integrated partner staff into team operations, aligned leadership, and handled escalations transparently.
Result
Reversed partner sentiment; the relationship was later cited as a model for other partnerships, and the team led the broader organization in engagement scores.
Unified privacy review across a major business line
What
Sponsored a pilot that consolidated two privacy-review workflows across a major business line.
Scale
Launched as a focused seven-week pilot and expanded to full coverage shortly afterward.
How
Provided strategic sponsorship, partner alignment, change leadership, and working-team oversight while tracking review speed against an explicit target.
Result
Exceeded the business line's review-time goal on a majority of cases and cleared the team's stretch target.
ii.
AI, agents, and human judgment
Agentic AI changes more than the toolset. When systems can plan, decide, and act across a workflow, governance must define where machines may exercise autonomy, where people must retain judgment, and who remains accountable for the outcome.
Turned AI adoption into an operating-model change
Authored an AI Innovation Framework, led organization-wide AI learning, and drove AI-augmented and AI-native ways of working across the program-management team. Adoption reached the high 80s, and the framework became foundational thinking for a broader operating-model rework.
Governed generative AI in practice
Supporting more than 100 generative-AI launches made the tradeoffs concrete: improve decision speed without weakening privacy analysis, clarify dataset guidance without replacing judgment, and use reusable decision frameworks without turning governance into a mechanical checklist.
Design for agency, not just automation
My approach treats human agency as an operating requirement. People need clear decision rights, understandable escalation paths, meaningful intervention points, and responsibility for outcomes, even as agentic systems take on more planning and execution.
iii.
Products and systems I build
I take products from an identified human need and paper specification through research, architecture, implementation, deployment, and iteration. I work with coordinated AI agents across multiple products simultaneously—using them for research, design, engineering, testing, and operations while retaining responsibility for priorities, tradeoffs, risk, quality, and outcomes.
Product case study
Adaptive Piano Learning App
An adaptive learning product designed, built, and iterated from paper specification through deployment.
ReactTypeScriptPythonMulti-agent AIMIDIiOS
Adaptive planningExplainable lesson decisionsInteractive learningRole-aware chord practiceMeasured feedbackPerformance becomes the next input
1 / 3
Product leadership
From paper specification to shipped product
Defined the learning model, product principles, roadmap, system architecture, and release sequence.
Agentic design
Adaptive without displacing agency
The learner establishes goals and constraints; the system adapts the path and feedback.
How I build
Multiple specialized AI agents
Coordinated parallel research, design, engineering, testing, review, and deployment.
Product insight. Useful autonomy depends on clear goals, observable progress, trustworthy feedback, and meaningful user control. The system should expand the learner's agency, not make the learner dependent on the system.
Product case study
Integrated 3D-Printing and Inventory Hub
A connected software-and-hardware product that turns a fragmented home-printing workflow into one operational system.
ReactTypeScriptPythonMQTTESP32-S3Computer Vision
End-to-end productFrom configuration to printable objectDomain-specific automationTool-fit geometry without CAD overheadSoftware + hardwareLive operational state in one workflow
1 / 3
Product leadership
From mapped journey to integrated system
Defined the roadmap, component boundaries, data flows, integration requirements, and safe failure behavior.
End-to-end workflow
From model selection to organized output
Connects material planning, spool inventory, loading guidance, live print state, and fitted storage generation.
How I build
Parallel agents, one product direction
Directed specialized agents across product research, interaction design, software, firmware, computer vision, testing, and deployment.
Technology
Full-stack and hardware-aware
React and Python services connect through MQTT to NFC-enabled spool identity, live printer state, and computer-vision workflows.
Product insight. Agentic systems that cross software, hardware, and physical-world boundaries need explicit permissions, observable decisions, safe failure behavior, and reliable points for human intervention. Product leadership means designing those controls into the workflow, not adding them after the system works.
Additional system
A console-like PC experience
Designed a PC-based living-room system to behave like a dedicated console: intentional one-action wake, automatic television control, reliable suspend, correct input restoration, and recovery without keyboards or manual troubleshooting.
The system integrates operating-system sleep services, network television control, Wake-on-LAN, an HDMI-CEC listener, and USB wake hardware. When the PC began resuming immediately after suspend, I used system telemetry, protocol analysis, and controlled hardware tests to isolate an unexpected CEC wake path and implement a persistent fix without disrupting intentional controller and remote wake behavior.
How I build with agents. AI agents supported protocol research, source analysis, hypothesis comparison, and test planning. I defined the product experience, set the architecture and constraints, directed the investigation, and established the physical evidence required to consider the system reliable.
Director of Strategic Programs + Privacy Program Leader / Meta
Built and transformed global privacy and risk-review organizations, owned regulatory-compliance and breach-response work, established governance and decision systems, and led distributed teams through growth, crisis, audit, and AI-driven operating-model change.
Defined and executed connected-home and smart-home strategy with senior leadership. Negotiated and signed ecosystem agreements including Apple HomeKit. Implemented a CCPA compliance roadmap and educated leadership on privacy and data practices across the smart-home ecosystem.
Sep 2012 - May 2017
Managing Partner + Privacy Consulting / Kendrick & Madrid LLP
Advised businesses on data-privacy regulation, drafted and implemented privacy notices and policies, and guided clients in standing up privacy programs. Maintained an active criminal and appellate practice, including a published Colorado Court of Appeals opinion.
May 2009 - Aug 2012
Deputy State Public Defender / Colorado State Public Defender
Trial attorney in the Denver Trial Office, building the litigation, judgment, and high-stakes communication foundation that still shows up in executive privacy and risk work.
JD, University of Denver - Sturm College of Law / MBA, University of Colorado Denver / Licensed to practice law in Colorado.
