layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Global Head of Data & AI Governance & Oversight, SVP at State Street

Enterprise Data, AI & Agentic AI Governance
Define, maintain, and evolve enterprise-wide policies, standards, and control frameworks for:
Data governance and data management
AI, GenAI, and Agentic AI
Responsible AI and AI risk classification
Third ‑ party and vendor AI usage
Ensure governance applies across the full lifecycle of data and AI assets, from design through retirement.
Strategic Governance Target State (LOB Partnership)
Partner with each line of business to define and maintain the target state for Data and AI governance aligned to enterprise standards and regulatory expectations.
Translate enterprise governance principles into domain ‑ specific, actionable models .
Provide governance leadership into Data & AI roadmaps without owning delivery or architecture decisions.
Authoritative Data Sources, Ownership & Stewardship
Establish and operate the enterprise framework for authoritative data sources by data domain and key data element.
Partner with data owners and data stewards to: Designate approved and trusted data sources
Resolve conflicts between competing sources
Ensure lineage, data quality, and fitness for purpose
Ensure consistent use of authoritative data sources across analytics, reporting, and AI use cases.
Enterprise Data Competency
Institutionalize the enterprise data governance operating model, including: Data ownership and accountability
Data steward roles and responsibilities
Management of key data elements and critical data assets
Embed data accountability into business processes across all lines of business.
AI Inventory, Classification & Streamlined Approvals
Own the enterprise inventory of AI initiatives across AI, GenAI, ML, and Agentic AI.
Ensure inventories, classifications, and definitions align with NIST AI Risk Management Framework and applicable regulatory expectations.
Design and operate streamlined, tiered approval processes for all AI types, ensuring:
Consistent intake and classification
Clear routing to required partners (e.g., Model Risk Management, Legal, Privacy, Security)
Predictable and efficient approval timelines
Monitor adherence to approval processes and continuously improve them to reduce friction and late-stage escalation.
Model Risk Management Partnership
Partner closely with the Model Risk Management (MRM) function.
Ensure AI and ML use cases are appropriately classified and routed to MRM where required.
Align governance standards and approval workflows with MRM requirements without duplicating or owning MRM accountabilities.
Data Quality & Data Incident Management
Define enterprise standards for data quality measurement, monitoring, and control.
Own enterprise processes for data issues and incidents, including root cause analysis, remediation tracking, and escalation.
Partner with business and platform teams to embed preventive and detective quality controls.
Regulatory Leadership & Proactive Engagement
Act as the single enterprise point of contact for regulators, audit, and external inquiries related to Data and AI.
Proactively assess emerging regulatory expectations and drive early alignment across the firm.
Coordinate and participate in all active regulatory efforts relating to data, technology, and AI, working with appropriate bank owners.
Lead regulatory examinations, findings, and remediation programs (e.g., BCBS 239, MRAs).
Ensure regulatory learnings are converted into durable governance and process improvements.
Enterprise Partnership & Operating Model Stewardship
Partner with: Data Architecture to align governance intent with target architectures
Data & AI Platforms to embed controls, approvals, and authoritative sourcing
Enablement teams to operationalize compliant pathways
Business leaders to adopt governance consistently
Maintain independence from delivery while actively shaping enterprise design decisions.
Continuously evolve governance practices to match regulatory and technology change.

Global Head of Data & AI Governance & Oversight, SVP

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