Establish controls for model usage, data handling, and third-party AI services across the enterprise. Reduce regulatory, reputational, and operational risk as AI scales.
Talk to usEstablish controls for model usage, data handling, and third-party AI services across the enterprise. Reduce regulatory, reputational, and operational risk as AI scales.
Focus areas
AI risk assessment frameworks
Model and data governance policies
Third-party AI vendor review
Monitoring, logging, and audit trails
Incident response for AI systems
Define acceptable use, data classification, and approval requirements for AI initiatives. Align policies with existing security and privacy programs.
Implement guardrails for prompts, outputs, and model access in development and production. Log activity for audit and investigation.
Evaluate external models and SaaS AI tools for security, residency, and contractual fit. Maintain an inventory of approved services and versions.
Clarify outcomes, constraints, and the systems already in place so scope stays grounded in reality.
Shape architecture, delivery phases, and success measures before build starts.
Deliver in clear increments, validate with stakeholders, and keep quality visible throughout.
Put value into production, stabilize operations, and keep refining based on real usage.
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Talk to usWe start with business outcomes and operating constraints, then design delivery around architecture, integration, and adoption so change lasts beyond launch.
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