Associate Consultant - MFT KGS CH

Associate, AI Inventory & Foundational Enablement

Qualifications:

1. Bachelor's degree or equivalent work experience with 1-3 years of experience in information security, technology risk, IT governance, data management, compliance, business analysis, or related technology functions.

2. Strong technical aptitude and ability to understand AI-enabled systems, software applications, cloud services, vendor platforms, data flows, integrations, and technology ownership models at a foundational level.

3. Strong organizational skills with demonstrated ability to collect, structure, validate, and maintain detailed information across multiple stakeholders, systems, and workstreams.

4. Ability to work with incomplete or ambiguous information, identify gaps, follow up with stakeholders, and drive tasks to completion with strong attention to detail and data quality.

5. Strong communication, time management, problem solving, and coordination skills; flexible and adaptable team player; resourceful in delivering high-quality work in an environment driven by customer service, collaboration, and operational consistency.

6. Proficiency with Microsoft Office applications, including Excel, PowerPoint, Word, Outlook, and Teams; experience with SharePoint, Power BI, Archer, ServiceNow, or similar workflow, reporting, GRC, or inventory management platforms is preferred.

Key Responsibilities:

1. Build and maintain a centralized inventory of AI systems, applications, tools, use cases, models, agents, integrations, and supporting technology components across the enterprise.

2. Gather, validate, and organize foundational AI system information needed to support security, governance, compliance, risk management, and operational oversight.

3. Coordinate with business, technology, security, privacy, legal, risk, and governance stakeholders to collect required AI system details, clarify ownership, document system purpose, and maintain accurate records.

4. Track key inventory attributes, including business owner, technical owner, use case description, vendor or platform, deployment environment, data classification, model usage, integration points, and lifecycle status.

5. Identify data gaps, incomplete records, aging items, and ownership issues; conduct follow-up with system owners and stakeholders to improve inventory completeness and data quality.

6. Prepare reports, dashboards, status updates, documentation, process guides, templates, and metrics that support AI inventory completeness, foundational AI visibility, and responsible AI enablement.