Common buying triggers
- Employees are using GenAI without a complete inventory.
- Customer or board questions about AI risk are increasing.
- AI vendors process sensitive or customer data.
- LLM applications need security testing and accountable approval.
AI governance
AI Governance & AI Security Consulting
Govern AI use at the speed the business adopts it—without blocking useful experimentation or ignoring material risk.
What should change after the engagement?
- AI use-case and vendor inventory
- Risk-tiering and approval model
- AI acceptable-use and data rules
- LLM security testing expectations
- AI vendor due diligence
- Governance evidence aligned to business and regulatory needs
Illustrative advisory outputs
Tangible records the work should leave behind
The examples below are illustrative advisory outputs and do not represent a specific client.
AI inventory
Use cases, owners, models/vendors, data classes, integrations, criticality, and approval status.
AI risk register
Security, privacy, model, vendor, operational, legal, and customer risks with accountable decisions.
AI policy baseline
Approved use, prohibited data, human oversight, testing, vendor, logging, and escalation expectations.
AI assurance pack
Decision records, evaluations, vendor evidence, testing results, and governance reviews.
Working model
Truth → ownership → rhythm
1. Discover AI use
Identify sanctioned and shadow AI, data flows, vendors, and business criticality.
2. Tier and control
Apply risk tiers, approval gates, vendor expectations, and LLM security controls.
3. Evidence and review
Create monitoring, periodic review, exception, and executive reporting cadence.
Who is this for?
- Organizations adopting GenAI rapidly
- SaaS companies embedding LLM features
- Teams preparing for EU AI Act or ISO 42001 discussions
- Companies with sensitive data in AI workflows
What this is not
- Model development outsourcing
- A legal classification opinion without security governance
- A generic AI policy copied without implementation
Questions buyers ask before starting
What should an AI governance program include?
At minimum: inventory, ownership, risk classification, approved-use rules, data controls, vendor due diligence, security testing for AI-enabled applications, human oversight, monitoring, evidence, and exception handling.
Is ISO 42001 required?
Not universally. It can provide a useful management-system structure for organizations that need formal AI governance, but the decision should follow customer, regulatory, and operating needs.