SmartSuite for the AI Governance Lead
The AI Governance Lead monitors enterprise AI posture, identifies systemic issues and ensures AI use aligns with laws, regulations and internal standards. They run the governance program: inventory coverage, exceptions, framework mapping and maturity, linking models to obligations, controls and evidence.
What you own
The AI Governance Lead monitors enterprise AI posture, identifies systemic issues and ensures AI use aligns with laws, regulations and internal standards.
- Operate the AI governance program and its policies
- Maintain inventory coverage and risk tiering of AI use cases
- Map models to obligations, controls and standards
- Track exceptions, systemic issues and governance maturity
- Coordinate reviews with model owners, validators, privacy and security
- Report AI governance posture to the Chief Data and AI Officer
At a glance
The GRC processes this role owns, touches and depends on.
Owns
accountable for the process
Touches
contributes or approves
Depends on
consumes its output
How the GRC processes connect
Three lines of defense set the frame; the chain shows where each process hands off to the next. Colors follow the role: navy is owned, blue is touched, gray is depended on.
Reporting lines
Who this role reports to, who reports to them, and the peers they work with most.
Reports to
Direct reports
Works closely with
How SmartSuite supports this role
Lead
AI governance. Provides a connected governance ecosystem linking models, risks, controls, obligations and evidence, with framework mapping and issue management.
Compliance management. Maps models to obligations, controls and standards and tracks evidence per requirement.
Issues and actions. Tracks exceptions and repeat issues to identify enterprise risk themes.
Reporting. Reports coverage, exceptions and governance maturity.
Products this role uses most
Industry reference
The NIST AI RMF's Govern function and ISO/IEC 42001:2023 define the program: policies, an inventory of AI systems, roles, impact assessments and monitoring, with the Generative AI Profile (2024) covering foundation-model use. The EU AI Act (2024/1689) makes inventory, risk classification and documentation legal duties for providers and deployers of high-risk systems, now applying from December 2027 for stand-alone systems under the 2026 Digital Omnibus on AI.
Sector rules add detail. US federal agencies must inventory AI use cases under OMB guidance; insurers follow the NAIC's 2023 model bulletin and Colorado's SB 21-169 insurance rules; healthcare organizations meet HHS transparency rules for decision support and FDA device guidance; financial firms fold AI into SR 11-7 model governance.
Related roles
Explore Further
Guides and definitions from the GRC Knowledge Center and the What-is glossary, chosen for this role.
Frequently asked questions
What does an AI Governance Lead own in the governance program?
The AI Governance Lead runs the AI governance program end to end: its policies, inventory coverage and risk tiering of AI use cases, the mapping of models to obligations, controls and standards, and the tracking of exceptions, systemic issues and governance maturity. In SmartSuite that program lives in AI Governance, a connected ecosystem linking models, risks, controls, obligations and evidence, so the lead can see which use cases are inventoried, which carry open exceptions and how mature each part of the program is.
How does SmartSuite help an AI Governance Lead keep the AI inventory complete?
SmartSuite keeps every model and AI use case in AI Governance, where each record links to the risks, controls, obligations and evidence that apply to it. Inventory coverage becomes measurable: the lead can see which use cases are registered, which have a risk tier and which still lack an owner or a review. Framework mapping sits alongside the inventory, so standards are applied to the registered models rather than tracked in a separate spreadsheet, and CRI AI RMF is available for teams aligning to that profile.
How does SmartSuite map models to obligations, controls and standards?
SmartSuite maps each model to the obligations, controls and standards that apply to it and tracks evidence per requirement, so the lead can show for any model which laws, internal standards and controls cover it and what proof exists. Policy Management holds the AI policies those controls implement, and because the mapping is recorded against the inventory, the lead can see at once which models a new obligation touches rather than reassessing them one by one.
How are exceptions and systemic issues tracked across the AI estate?
Exceptions and repeat issues are tracked in Issues Management, which records each exception against the model and control it relates to and follows it to closure. Because repeat issues are visible across the whole inventory, the lead can identify enterprise risk themes, for example the same control failing across several use cases, rather than treating each exception in isolation. Those themes feed the maturity view and the coordinated reviews with model owners, validators, privacy and security.
How does the AI Governance Lead report to the Chief Data and AI Officer?
The AI Governance Lead reports coverage, exceptions and governance maturity to the Chief Data and AI Officer from the same records the program runs on. SmartSuite reports inventory coverage, open exceptions and maturity by area, so the upward report is a view of live data rather than a compiled deck. The same view serves the lead's work with the AI Risk Officer, Model Risk Manager and Privacy Program Manager, who see the models and issues they share.
What does an organization need in place to start AI governance in SmartSuite?
You need a starting list of AI use cases and models, an owner for each and the obligations and standards you intend to map against; the inventory can begin incomplete and grow as coverage improves. New customers can start with SmartSuite Onboarding, a guided four-week program around one or two priority workflows, such as the AI use-case inventory. The AI governance work shares its issue, policy and evidence records with the compliance, privacy and risk teams, so model owners and validators work in the same system rather than sending documents to the lead.
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