Discovery & Inventory

Why Discovery Matters

You cannot govern what you cannot see. Discovery and inventory form the critical foundation of any AI governance program. In today's rapidly evolving AI landscape, organizations often have AI systems scattered across departments, business units, and shadow IT environments. Without systematic discovery, you're flying blind — unable to assess risks, enforce policies, or ensure compliance.

The proliferation of AI tools has accelerated dramatically. Employees are using ChatGPT, Copilot, Midjourney, and hundreds of other AI-powered applications — often without IT knowledge or approval. Development teams are embedding AI capabilities into products. Data science teams are training custom models. Third-party vendors are incorporating AI into their solutions. Each of these represents a potential governance gap.

Effective discovery goes beyond simply listing AI tools. It requires understanding how AI is being used, who is using it, what data it processes, what decisions it influences, and what risks it may introduce. This comprehensive view enables informed decision-making about which AI systems require heightened scrutiny and controls.

Key Discovery Activities

1. AI System Inventory

Create a centralized registry of all AI systems, tools, and applications in use across your organization. This inventory should capture both sanctioned enterprise AI solutions and shadow AI being used by individual teams or employees. For each system, document its purpose, technical architecture, data sources, stakeholders, and business impact.

Your inventory should differentiate between AI systems you build internally, commercial AI tools you procure, and AI capabilities embedded in third-party products. Each category presents different governance challenges. Internal models require oversight of development practices. Commercial tools need vendor assessment. Embedded AI requires contractual protections and visibility into how vendors use AI on your behalf.

2. Shadow AI Discovery

Shadow AI — unauthorized or unknown AI usage — represents one of the most significant governance challenges today. Employees can sign up for powerful AI tools in minutes, often bypassing procurement, security review, and data protection processes. Implement systematic approaches to identify shadow AI, including network traffic analysis, SaaS management platform monitoring, expense report review, and employee surveys.

Rather than viewing shadow AI purely as a compliance violation, treat it as valuable intelligence about business needs. When employees seek out AI tools independently, they're often solving real problems that your approved technology stack doesn't address. Use discovery as an opportunity to understand these needs and provide governed alternatives.

3. Use Case Documentation

For each AI system identified, document the specific use cases and applications. What business problems does it solve? What processes does it automate? What decisions does it inform or make? Understanding the use case is essential for risk assessment — an AI tool used for creative brainstorming presents very different risks than one making hiring decisions or approving financial transactions.

4. Risk Classification

Not all AI systems present equal risk. Develop a classification scheme to categorize AI systems by risk level based on factors like the sensitivity of data processed, the significance of decisions made, the degree of automation, potential for bias or discrimination, regulatory applicability, and potential business impact if the system fails.

Common classification schemes include high-risk (affecting legal rights, safety, or critical business operations), medium-risk (significant business impact but with human oversight), and low-risk (minimal impact or high human control). This classification drives subsequent governance activities — high-risk systems warrant comprehensive risk assessment, strict controls, and ongoing monitoring, while low-risk systems may require only basic policy compliance.

Implementation Approach

Start your discovery process with a combination of top-down and bottom-up approaches. Top-down involves working with IT, procurement, and business unit leaders to identify known AI systems. Bottom-up means surveying employees, analyzing tool usage data, and monitoring for AI-related spending. Both perspectives are necessary for comprehensive coverage.

Make discovery an ongoing process, not a one-time project. New AI tools emerge constantly, and business needs evolve. Establish processes for continuous discovery, including mandatory registration of new AI systems, regular discovery sweeps, and automated monitoring where possible.