Adopt AI with confidence — not with unmanaged risk.

01 AI Readiness Assessment

• Evaluate data quality, infrastructure, and organizational readiness for AI adoption
• Assess current, unsanctioned ("shadow AI") tool usage across the business
• Deliver a readiness score with prioritized next steps

02 AI Strategy & Use Case Roadmap

• Identify and prioritize high-value, low-risk AI use cases
• Build a phased adoption roadmap aligned to business goals
• Define success metrics and ownership for each initiative

03 AI Governance Framework Development

• Establish a governance structure aligned to NIST AI RMF and ISO/IEC 42001
• Define roles, oversight, and approval processes for AI initiatives
• Support audit and compliance evidence for emerging AI regulation

04 AI Risk & Model Risk Assessment

• Assess risk from bias, hallucination, and inappropriate model reliance
• Review model and vendor documentation for transparency and limitations
• Prioritize risk mitigation based on business impact

05 AI Policy Development & Documentation Support

• Develop acceptable AI use, data handling, and disclosure policies
• Create guidance for employee use of public and enterprise AI tools
• Support vendor AI risk language for contracts and procurement

06 AI Tool & Vendor Evaluation

• Evaluate platforms such as Microsoft Copilot, ChatGPT Enterprise, and Gemini
• Assess data handling, retention, and security terms of AI vendors
• Support rollout planning and license/cost optimization

07 Employee AI Training & Enablement

• Train staff on safe, effective, and policy-compliant AI use
• Provide role-based guidance for high-risk functions (finance, HR, legal)
• Build internal champions to sustain adoption

08 Responsible & Ethical AI Use Review

• Review AI use cases for fairness, transparency, and accountability
• Identify reputational and legal risk in customer-facing AI use
• Recommend human-in-the-loop controls where appropriate

09 AI Security Review

• Assess exposure to prompt injection, data leakage, and model abuse
• Review integration points between AI tools and sensitive systems/data
• Recommend technical and procedural safeguards

10 AI Vendor & Third-Party Risk Review

• Assess AI vendors and embedded AI features in existing SaaS tools
• Review data residency, training-data usage, and retention practices
• Create an AI vendor risk register alongside your broader vendor risk program