How to Build an AI Usage Register for SaaS Applications

An AI register is only useful if it explains what is actually happening in the business, not what the policy hoped would happen.

Many organizations are trying to govern AI with disconnected assets: a policy document, a spreadsheet from procurement, a few vendor questionnaires, a list of approved tools, and a growing pile of employee questions. An AI usage register brings those fragments into one operational record. It is the evidence layer that shows which AI-enabled applications exist, who uses them, what data they touch, what controls are available, and which decisions have been made.

Define the purpose before designing the fields

A good register should support security review, vendor risk, access review, privacy review, audit evidence, and business decision-making. That means it cannot be just a list of vendors. It should connect tools to users, owners, data categories, approval status, and control evidence. If a customer asks whether a specific AI vendor is used, the answer should come from the register. If an auditor asks how AI usage is reviewed, the register should show the workflow.

The ISO/IEC 42001 AI management systems standard provides a useful management-system lens: AI needs governance practices that are repeatable, documented, and connected to organizational responsibilities. A register is one of the simplest ways to make those responsibilities visible.

Minimum fields for a useful AI usage register

• Application name, primary domain, and vendor name.

• AI capability type: chatbot, summarizer, classifier, generator, copilot, agent, recommendation engine, or automation.

• Business owner, technical owner, and approving function.

• Known users, departments, and authentication method.

• Data categories processed, including customer data, employee data, financial data, source code, PHI, student data, or regulated records.

• Training and product-improvement position: no training, opt-in, opt-out, enterprise excluded, yes, or unknown.

• External model provider or subprocessors, where disclosed.

• Admin controls, audit logs, retention controls, export controls, and offboarding path.

• Approval status: approved, approved with conditions, under review, prohibited, or unknown.

• Last review date, next review date, evidence links, and open remediation tasks.

SaaS governance and compliance becomes much easier when these fields are consistently maintained. Instead of rebuilding evidence for every audit, security teams can reuse a living register that reflects the environment.

Step 1: Start with discovery, not intake

The register should not depend on employees volunteering every tool they use. Self-reporting is helpful, but it is incomplete by design. People forget. Teams rename tools. Free trials appear and disappear. Embedded AI features arrive inside tools that procurement already approved. Discovery should identify applications, accounts, OAuth grants, and usage signals before the register is populated.

This is where SaaS Discovery belongs at the beginning of the workflow. The register should reflect actual usage, not only approved purchasing records.

Step 2: Separate AI feature risk from vendor risk

One vendor may support multiple use cases with different risk profiles. A document platform might have an AI summary feature, an AI search feature, a writing assistant, and an automation feature. Treating the vendor as one risk record can hide important details. The register should capture the AI capability that is enabled, the data it can reach, and whether the feature can take action.

The OWASP LLM guidance is useful here because it distinguishes security risks such as sensitive information disclosure, excessive agency, and insecure output handling. Those risks depend on the feature and integration pattern, not just the vendor name.

Step 3: Assign owners and expiration dates

Every approved AI use should have an owner and a review date. “Approved forever” is not a governance decision; it is a slow leak. Vendor terms change, product features change, departments change, and data use expands. Time-boxed approvals force the organization to revisit high-risk AI usage before it becomes permanent by accident.

Step 4: Build a triage model

Not every AI tool needs the same review depth. Use a simple tiering model. Low-risk tools process public or non-sensitive content and cannot take action. Medium-risk tools process internal business data or have unclear training terms. High-risk tools touch regulated data, customer records, source code, financial information, privileged workflows, or agentic actions. The register should make the tier visible so remediation work can be prioritized.

The NIST AI RMF can help teams structure this around mapping, measuring, managing, and governing AI risk. The register is not the whole program, but it is the shared evidence layer that keeps the program grounded.

Step 5: Connect the register to access reviews

An AI register should not live only in compliance. It should inform access reviews, offboarding, vendor review, and incident response. When someone leaves, the team should be able to see which AI-enabled tools and OAuth grants were tied to that identity. When a vendor changes terms, the team should be able to identify affected owners and users. When a department requests a new AI tool, the team should compare it against approved alternatives.

SaaS Security Posture Management helps turn the register from a static document into an operating process, connecting discovery, posture, controls, and evidence.

A simple review cadence

• Weekly: review newly discovered AI-enabled applications and unknown training positions.

• Monthly: update owners, approval status, and remediation progress.

• Quarterly: review high-risk AI tools, admin controls, audit logs, and vendor terms.

• Annually: refresh policy alignment, executive reporting, and evidence requirements.

What not to include

Avoid turning the register into a junk drawer. Do not include long policy essays, generic vendor marketing claims, or duplicate rows for every minor feature unless the risk profile is meaningfully different. The register should be detailed enough to support decisions but concise enough that teams will maintain it. If a field does not change a decision, it may belong in supporting evidence rather than the primary register.

It is also important to avoid false precision. If the training position is unknown, write unknown. If the owner is suspected but not confirmed, mark it as likely owner and assign follow-up. A register full of confident guesses is worse than a register that clearly separates confirmed facts from open questions.

Who should own the register

Security should usually own the operating process, but not every field. Vendor risk should own vendor evidence. Privacy should own privacy-sensitive data categories. IT should own access and configuration information. Business owners should own the use case and acceptable-use boundaries. Compliance should own the evidence requirements. The register becomes durable when each group updates the fields it can actually verify.

Set a recurring review meeting for high-risk changes, but do not require a committee for every low-risk update. The best register is operational, not ceremonial. New discoveries should move into a queue automatically, high-risk items should be escalated, and approved tools should carry review dates so they do not become permanent exceptions by neglect.

How to keep the register from going stale

The biggest threat to an AI register is neglect. A spreadsheet built during a policy push can look impressive for a month and useless by the next quarter. To avoid that, connect the register to events: new app discovery, new OAuth grants, vendor renewals, employee offboarding, contract changes, data-processing updates, and department requests. The register should update when the environment changes, not only when an audit is scheduled.

Create an owner for the register itself and owners for specific fields. Then create a monthly hygiene process that reviews unknowns, expired approvals, high-risk vendors, and tools with missing business owners. The work does not need to be heavy, but it must be regular. Unknowns should move toward decisions every month.

A well-maintained register becomes a shared source of truth. Security uses it for risk prioritization. Compliance uses it for evidence. Procurement uses it for renewals. Business teams use it to find approved tools. Leadership uses it to understand where AI adoption is accelerating.

Need a faster way to build the first register? Use Waldo Security SaaS Discovery to identify AI-enabled SaaS usage, then connect it to SaaS compliance workflows so the register becomes living evidence instead of another spreadsheet.