They help organizations operationalize enterprise AI governance through practical training, framework-based implementation, risk assessments, technical controls, and ongoing monitoring for AI and GenAI initiatives.

No. Every organization using AI benefits from clear ownership, data controls, evaluation, monitoring, and human oversight. Regulated or high-impact use cases typically require more rigorous controls.

Data governance manages the quality, access, lineage, and accountability of data. AI governance adds controls for AI use cases, models, prompts, outputs, decisions, human oversight, and lifecycle risk. They should work together.

Yes. GEM can assess current pilots, identify gaps in data, permissions, evaluation, monitoring, documentation, and ownership, then create a prioritized remediation and scale-up plan.

The right controls vary by use case, but often include permission-aware retrieval, PII detection and masking before prompts are sent to the model, prompt-injection defenses, input/output guardrails aligned to guidance such as the OWASP Top 10 for LLM Applications, evaluation, logging, monitoring, cost controls, incident handling, and approval gates for consequential actions.

Begin with an AI governance assessment that maps your priority use cases, current controls, risk exposure, and target operating model. Then implement the highest-value gaps in phases.

GEM designs governance programs aligned to widely recognized references such as the NIST AI Risk Management Framework and the ISO/IEC 42001 AI management system standard, adapted to your industry, risk profile, and existing governance structures.

GEM defines success metrics during the assessment phase, such as reduced incident rate, faster pilot-to-production time, and audit readiness, and measures results against a pre-agreed roadmap.

Monitoring, review cadence, and incident handling are established during the Operate & Improve phase, with metrics tracked continuously as AI use expands.