Chris Shatswell · Principal
AI and cybersecurity that can survive production.
Regulated environmentsI help leadership teams move high-stakes technology from concept to operation with security, privacy, infrastructure, and delivery discipline designed in from the start.
Selected work
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MD
A public-facing AI platform brought to market in regulated healthcare.
Led the platform from inception through market launch, shaping its multi-model architecture, condition-specific model design, privacy framework, and forward roadmap while coordinating delivery across a large, distributed technology organization.
Where I help
From executive decision to working system.
Engagements are built around a defined decision, launch, or operating problem—not a generic transformation program. The work connects product intent to architecture, risk, delivery, and the realities of regulated operations.
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AI product and platform strategy
Translate a promising use case into a defensible product direction, operating model, architecture, and delivery plan.
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AI governance, privacy, and security
Build practical controls around data use, model behavior, vendors, human oversight, and deployment risk.
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Cybersecurity and compliance execution
Align security programs and technical delivery with the demands of SOC 2, ISO 27001, HITRUST, NIST, and regulated customers.
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Technology operating model and recovery
Clarify ownership, reset delivery, and connect engineering, infrastructure, security, vendors, and leadership around measurable execution.
Point of view
AI investment through an operator's lens.
These are practical tests I use when evaluating AI work. They come from operating through earlier technology shifts and from current experiments with model behavior, implementation effort, and cost.
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Governance belongs in the investment model
Regulation changes scope and cost. A useful business case accounts for compliance, licensing, infrastructure, and ongoing support before comparing the investment with projected labor savings. Several use cases may need to share that overhead before the economics work.
Read the LinkedIn post -
Production economics decide the ROI
Prototype costs rarely describe production. Data cleanup, scaled compute and API usage, and legacy integration can consume the expected return. The implementation timeline also has to make sense against the likely lifespan of the technology.
Read the LinkedIn post -
Context discipline affects cost and answer quality
Connecting every repository to every request can increase token usage and let irrelevant material compete with the task. Retrieve only the records and fields that can change the answer, then define what the model should return.
View the LinkedIn post and video
AI platform delivery, privacy, security, infrastructure, and global technology operations.
vCIO leadership, cybersecurity, managed services, and technology delivery for community banks.
Multi-site transformations across finance, real estate, hospitality, and entertainment.
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