AI Asset Inventory and Model Registry
5 minutes
5 Questions
An AI Asset Inventory and Model Registry are foundational controls in advanced AI security management and governance, providing the visibility needed to manage risk across the AI lifecycle. The principle is simple: you cannot secure, govern, or audit what you do not know exists. The AI Asset Inventory is a comprehensive, continuously maintained catalog of all AI-related assets in the organization. This extends beyond models to include training and validation datasets, feature stores, prompts and prompt templates, embeddings and vector databases, third-party APIs (such as foundation model services), agents, plugins, pipelines, and the infrastructure that hosts them. Each asset should carry metadata such as owner, business purpose, data classification, risk tier, regulatory applicability (e.g., EU AI Act category), dependencies, and lifecycle status. The inventory also surfaces 'shadow AI'—unsanctioned tools adopted by business units—which is a major governance blind spot. The Model Registry is a more specialized system of record for models themselves. It stores versioned model artifacts alongside lineage information: which data and code produced the model, hyperparameters, evaluation metrics, fairness and robustness test results, approval signatures, and deployment history. It enforces stage gates (development, staging, production, retired) so that only validated, approved models reach production, and enables rapid rollback when issues arise. Integrity controls such as cryptographic hashing and signing protect against tampering and supply-chain attacks. From a program management perspective, these tools operationalize frameworks like NIST AI RMF, ISO/IEC 42001, and the OWASP LLM Top 10. They support risk-based prioritization of security assessments, incident response (identifying affected models quickly), compliance reporting, and change management. Integration with CMDBs, MLOps platforms, data governance catalogs, and security tooling ensures the inventory stays accurate through automated discovery rather than manual spreadsheets. Ultimately, a mature inventory and registry transform AI governance from reactive oversight into measurable, auditable, and defensible program execution.
AI Asset Inventory and Model Registry: A Complete Guide for the ISACA AAISM Exam
1. Why AI Asset Inventory and Model Registry Matter
You cannot govern, secure, or hold anyone accountable for what you do not know exists. This single principle is the foundation of the AI Asset Inventory and Model Registry domain within ISACA AAISM (Advanced in AI Security Management), under the broader area of AI Governance and Program Management.
Organizations today adopt AI through many channels: data science teams build custom models, business units subscribe to SaaS tools with embedded AI, developers call third-party APIs such as large language models, and employees quietly use generative AI assistants. Each of these introduces risk: data leakage, bias, regulatory exposure, intellectual property loss, supply chain vulnerabilities, and model drift. Without a centralized and maintained inventory, the AI security manager has no baseline against which to assess risk, apply controls, respond to incidents, or demonstrate compliance.
Key reasons this topic is important:
• Visibility - It establishes the scope of the AI security program. Every subsequent control (risk assessment, access management, monitoring, testing) depends on knowing which AI systems exist.
• Accountability - Each asset is tied to a named owner, enabling clear responsibility for risk decisions and remediation.
• Regulatory compliance - The EU AI Act, ISO/IEC 42001, the NIST AI Risk Management Framework, and many sector regulators expect organizations to identify and classify AI systems, especially high-risk ones. Several of these explicitly require documentation and registration of models.
• Incident response - When a vulnerability is discovered in a model, dataset, or vendor service, the inventory answers the question: where is it used, and who is affected?
• Shadow AI control - Unregistered AI usage is one of the fastest-growing sources of enterprise risk. Inventory processes are the primary defense.
• Lifecycle management - Models change over time. A registry tracks versions, approvals, performance, and retirement so that stale or unapproved models do not remain in production.
2. What It Is: Definitions and Distinctions
AI Asset Inventory
An AI asset inventory is a comprehensive, authoritative record of all AI-related assets across the enterprise. It is broader than a model registry. It typically includes:
• AI systems and applications (internally developed and third-party)
• Machine learning and foundation models (proprietary, open-source, vendor-hosted)
• Training, validation, and test datasets
• Prompts, prompt templates, agents, and orchestration workflows
• AI APIs and external services (including embedded AI features in SaaS products)
• Supporting infrastructure such as feature stores, vector databases, pipelines, and compute environments
• Associated documentation: model cards, data sheets, risk assessments, approvals
The inventory answers: What AI do we have, where is it, who owns it, what does it do, and how risky is it?
Model Registry
A model registry is a specialized system, often a technical platform within the MLOps toolchain, that stores and manages machine learning models and their metadata throughout the lifecycle. It functions as the system of record for models specifically. A registry typically tracks:
• Model name, unique identifier, and version history
• Lineage: training data sources, code, hyperparameters, and the experiment that produced the model
• Performance metrics and evaluation results
• Lifecycle stage (experimental, staging, production, deprecated, archived)
• Approval status and sign-offs
• Deployment locations and dependencies
• Artifacts such as model files, containers, and signatures or hashes for integrity
How they relate
Think of the inventory as the governance view and the registry as the engineering view. The registry feeds the inventory with technical detail about models; the inventory adds business context such as ownership, risk tier, regulatory classification, and vendor relationships, and extends coverage to assets that may never appear in a registry, such as a marketing team using a third-party generative AI tool. In exam questions, if the scenario emphasizes enterprise-wide visibility, business ownership, or third-party and shadow AI, the answer usually points to the inventory. If the scenario emphasizes versioning, lineage, reproducibility, promotion to production, or rollback, the answer usually points to the model registry.
3. Core Attributes to Capture
A well-designed inventory record for an AI asset generally includes:
• Identification - unique ID, name, description, type (predictive model, generative, agentic, embedded feature, etc.)
• Ownership - business owner, technical owner, data steward, risk owner
• Purpose and use case - intended use, user population, decisions it influences
• Risk classification - risk tier (e.g., low, medium, high, prohibited), regulatory category (such as EU AI Act high-risk), impact on individuals
• Data - data sources, sensitivity classification (PII, PHI, confidential), data residency, consent basis
• Provenance and supply chain - origin (built, bought, open-source), vendor, licensing, model source and integrity verification
• Technical details - model type, version, deployment environment, integrations, APIs consumed
• Lifecycle status - development, pilot, production, retired; dates of approval, last review, and next review
• Controls and assessments - completed risk assessment, security testing, bias testing, human oversight mechanism, monitoring in place
• Dependencies - upstream and downstream systems, other models it relies on, third-party components
4. How It Works: The Operating Process
Step 1 - Define scope and criteria
The AI governance body, often an AI steering committee or governance council, defines what counts as an AI asset, what must be registered, and the minimum metadata required. Policies should state that no AI system may be deployed to production or used with organizational data unless registered. Definitions must be broad enough to include embedded AI in vendor products and employee use of external tools.
Step 2 - Discovery
Assets are identified through multiple methods, because no single source is complete:
• Self-attestation and surveys of business units
• Procurement and vendor management records (contracts with AI clauses, SaaS subscriptions)
• Integration with the existing IT asset inventory, CMDB, and software asset management tools
• Scanning code repositories and CI/CD pipelines for ML libraries and model files
• Network, proxy, CASB, and DLP telemetry to detect traffic to AI services (the main technique for finding shadow AI)
• Cloud account and API key reviews
• Automated hooks from MLOps platforms and model registries
Step 3 - Registration and classification
Each discovered asset is recorded with its metadata and assigned a risk tier. Classification drives everything that follows: a high-risk model requires a full AI impact assessment, bias testing, security testing, human oversight design, and executive approval; a low-risk tool may require only basic registration and acceptable use confirmation. Classification criteria commonly consider impact on individuals, use of sensitive data, autonomy of decisions, regulatory applicability, and exposure to external users.
Step 4 - Integration with governance gates
The registry and inventory become control points in the AI lifecycle. Promotion from staging to production requires that the model is registered, has passed required evaluations, and is approved by designated authorities. This is where the model registry provides enforcement: deployment pipelines can be configured to reject models not in an approved state. Integrity controls such as cryptographic hashes or signatures stored in the registry protect against model tampering and substitution in the supply chain.
Step 5 - Maintenance and reconciliation
Inventories decay quickly. Processes must include periodic recertification by owners, automated reconciliation against discovery sources, change management triggers (any model retrain, version change, new data source, or new use case requires an inventory update), and metrics such as percentage of discovered assets that are registered, percentage of records recertified on schedule, and number of unregistered AI tools detected.
Step 6 - Retirement
When a model or system is decommissioned, the record is updated rather than deleted to preserve auditability. Retirement should address secure disposal or archival of model artifacts and data, revocation of access and API keys, and notification of dependent systems.
5. Roles and Responsibilities
• AI governance committee / Chief AI Officer - sets policy, approves classification scheme, owns the program
• AI security manager - ensures inventory completeness, uses it to scope security assessments and monitoring, drives shadow AI detection
• Business owner - accountable for the asset, its use case, and recertification
• Technical owner / ML engineers - maintain registry entries, versions, and lineage
• Data steward / privacy office - validates data classification and lawful basis
• Procurement / vendor management - ensures third-party AI is registered at onboarding
• Internal audit - independently verifies accuracy and completeness
6. Framework and Regulatory Alignment
• NIST AI RMF - The Govern and Map functions call for establishing accountability, documenting AI systems, and understanding context and dependencies; an inventory operationalizes these.
• ISO/IEC 42001 - The AI management system standard expects organizations to identify AI systems within scope, assign roles, and maintain documented information, including AI system impact assessments.
• EU AI Act - Classifies AI into risk categories and requires providers and deployers of high-risk systems to maintain technical documentation and, in certain cases, register in an EU database. Knowing which systems fall into which category is impossible without an inventory.
• ISO/IEC 27001 and traditional IT governance - Asset management is a longstanding control objective; AI inventory extends it to a new asset class and should integrate with, not duplicate, existing asset management.
7. Common Challenges
• Shadow AI and embedded AI features that bypass procurement
• Rapid version churn making records stale
• Lack of a clear definition of what counts as AI
• Multiple registries across teams and clouds without a federated view
• Vendors unwilling to disclose model details, requiring contractual transparency requirements
• Treating the inventory as a one-time project instead of a continuous process
8. Exam Tips: Answering Questions on AI Asset Inventory and Model Registry
Tip 1 - The inventory is almost always the first step. When a question asks what an AI security manager should do FIRST when establishing a program, assessing AI risk, responding to a new regulation, or addressing shadow AI, the answer that involves identifying or inventorying AI assets typically outranks options about implementing controls, training, or buying tools. You cannot assess or protect what has not been identified.
Tip 2 - Distinguish inventory from registry. Expect questions that test whether you know the difference. Governance, ownership, risk classification, third-party and enterprise-wide scope point to the inventory. Versioning, lineage, reproducibility, rollback, promotion gates, and artifact integrity point to the model registry.
Tip 3 - Think in ISACA terms: governance over technology. ISACA exams favor answers emphasizing accountability, defined ownership, policy, and alignment with business objectives over purely technical fixes. If one option says assign a business owner and risk classification to every AI asset and another says deploy a scanning tool, the governance answer is usually best unless the question is specifically about detection methods.
Tip 4 - Shadow AI questions. The best detective method is typically network or proxy monitoring, CASB, and DLP telemetry combined with procurement review; the best preventive method is policy plus an approved tool catalog and easy registration process. Blocking everything outright is rarely the best answer because it drives usage further underground and conflicts with business enablement.
Tip 5 - Completeness and currency are the quality measures. If asked how to evaluate the effectiveness of an inventory, look for answers about reconciliation against independent discovery sources, periodic owner recertification, and coverage metrics rather than the number of fields captured.
Tip 6 - Integrate, do not duplicate. Answers that integrate AI inventory with the existing CMDB, IT asset management, and vendor management processes are preferred over standalone spreadsheets or parallel systems.
Tip 7 - Risk classification drives control intensity. Questions about how much assessment a given system needs are answered by its risk tier in the inventory. High-impact, sensitive-data, autonomous-decision systems get the most rigorous treatment.
Tip 8 - Lifecycle and change triggers. Retraining, new data sources, new use cases, and vendor model updates all require inventory and registry updates and possibly re-assessment. A model approved for one purpose is not automatically approved for another.
Tip 9 - Supply chain and integrity. If a scenario involves downloaded open-source or vendor models, look for answers about recording provenance, verifying integrity through hashes or signatures, and documenting licensing in the registry.
Tip 10 - Eliminate extremes. Options that say inventory only high-risk models, rely solely on self-reporting, or delete records of retired models are usually wrong. Comprehensive scope, multiple discovery sources, and retained audit history are the defensible positions.
9. Worked Example
Scenario: An organization discovers that several departments are using generative AI services that were never reviewed by security. The AI security manager has been asked to address the situation. What should be done FIRST?
A. Block all access to external generative AI services at the firewall
B. Conduct discovery to identify and register all AI tools and use cases currently in operation
C. Deliver mandatory AI awareness training to all staff
D. Perform penetration testing of the generative AI services
Analysis: Option A is an extreme control that disrupts business and does not provide understanding. Options C and D are valid later activities but presuppose knowledge of what exists and where risk lies. Option B establishes visibility, ownership, and risk classification, enabling informed decisions about the other actions. Correct answer: B.
10. Key Takeaways
• The AI asset inventory is the foundation of AI governance and security; it defines program scope.
• The model registry is the technical system of record for model versions, lineage, and lifecycle state, and can enforce deployment gates.
• Every asset needs an owner, a purpose, a risk classification, and a lifecycle status.
• Discovery must use multiple sources, especially to find shadow and embedded AI.
• Inventories require continuous maintenance, reconciliation, and recertification.
• On the exam, identify first, govern over technology, integrate with existing processes, and let risk classification drive control intensity.
You cannot govern, secure, or hold anyone accountable for what you do not know exists. This single principle is the foundation of the AI Asset Inventory and Model Registry domain within ISACA AAISM (Advanced in AI Security Management), under the broader area of AI Governance and Program Management.
Organizations today adopt AI through many channels: data science teams build custom models, business units subscribe to SaaS tools with embedded AI, developers call third-party APIs such as large language models, and employees quietly use generative AI assistants. Each of these introduces risk: data leakage, bias, regulatory exposure, intellectual property loss, supply chain vulnerabilities, and model drift. Without a centralized and maintained inventory, the AI security manager has no baseline against which to assess risk, apply controls, respond to incidents, or demonstrate compliance.
Key reasons this topic is important:
• Visibility - It establishes the scope of the AI security program. Every subsequent control (risk assessment, access management, monitoring, testing) depends on knowing which AI systems exist.
• Accountability - Each asset is tied to a named owner, enabling clear responsibility for risk decisions and remediation.
• Regulatory compliance - The EU AI Act, ISO/IEC 42001, the NIST AI Risk Management Framework, and many sector regulators expect organizations to identify and classify AI systems, especially high-risk ones. Several of these explicitly require documentation and registration of models.
• Incident response - When a vulnerability is discovered in a model, dataset, or vendor service, the inventory answers the question: where is it used, and who is affected?
• Shadow AI control - Unregistered AI usage is one of the fastest-growing sources of enterprise risk. Inventory processes are the primary defense.
• Lifecycle management - Models change over time. A registry tracks versions, approvals, performance, and retirement so that stale or unapproved models do not remain in production.
2. What It Is: Definitions and Distinctions
AI Asset Inventory
An AI asset inventory is a comprehensive, authoritative record of all AI-related assets across the enterprise. It is broader than a model registry. It typically includes:
• AI systems and applications (internally developed and third-party)
• Machine learning and foundation models (proprietary, open-source, vendor-hosted)
• Training, validation, and test datasets
• Prompts, prompt templates, agents, and orchestration workflows
• AI APIs and external services (including embedded AI features in SaaS products)
• Supporting infrastructure such as feature stores, vector databases, pipelines, and compute environments
• Associated documentation: model cards, data sheets, risk assessments, approvals
The inventory answers: What AI do we have, where is it, who owns it, what does it do, and how risky is it?
Model Registry
A model registry is a specialized system, often a technical platform within the MLOps toolchain, that stores and manages machine learning models and their metadata throughout the lifecycle. It functions as the system of record for models specifically. A registry typically tracks:
• Model name, unique identifier, and version history
• Lineage: training data sources, code, hyperparameters, and the experiment that produced the model
• Performance metrics and evaluation results
• Lifecycle stage (experimental, staging, production, deprecated, archived)
• Approval status and sign-offs
• Deployment locations and dependencies
• Artifacts such as model files, containers, and signatures or hashes for integrity
How they relate
Think of the inventory as the governance view and the registry as the engineering view. The registry feeds the inventory with technical detail about models; the inventory adds business context such as ownership, risk tier, regulatory classification, and vendor relationships, and extends coverage to assets that may never appear in a registry, such as a marketing team using a third-party generative AI tool. In exam questions, if the scenario emphasizes enterprise-wide visibility, business ownership, or third-party and shadow AI, the answer usually points to the inventory. If the scenario emphasizes versioning, lineage, reproducibility, promotion to production, or rollback, the answer usually points to the model registry.
3. Core Attributes to Capture
A well-designed inventory record for an AI asset generally includes:
• Identification - unique ID, name, description, type (predictive model, generative, agentic, embedded feature, etc.)
• Ownership - business owner, technical owner, data steward, risk owner
• Purpose and use case - intended use, user population, decisions it influences
• Risk classification - risk tier (e.g., low, medium, high, prohibited), regulatory category (such as EU AI Act high-risk), impact on individuals
• Data - data sources, sensitivity classification (PII, PHI, confidential), data residency, consent basis
• Provenance and supply chain - origin (built, bought, open-source), vendor, licensing, model source and integrity verification
• Technical details - model type, version, deployment environment, integrations, APIs consumed
• Lifecycle status - development, pilot, production, retired; dates of approval, last review, and next review
• Controls and assessments - completed risk assessment, security testing, bias testing, human oversight mechanism, monitoring in place
• Dependencies - upstream and downstream systems, other models it relies on, third-party components
4. How It Works: The Operating Process
Step 1 - Define scope and criteria
The AI governance body, often an AI steering committee or governance council, defines what counts as an AI asset, what must be registered, and the minimum metadata required. Policies should state that no AI system may be deployed to production or used with organizational data unless registered. Definitions must be broad enough to include embedded AI in vendor products and employee use of external tools.
Step 2 - Discovery
Assets are identified through multiple methods, because no single source is complete:
• Self-attestation and surveys of business units
• Procurement and vendor management records (contracts with AI clauses, SaaS subscriptions)
• Integration with the existing IT asset inventory, CMDB, and software asset management tools
• Scanning code repositories and CI/CD pipelines for ML libraries and model files
• Network, proxy, CASB, and DLP telemetry to detect traffic to AI services (the main technique for finding shadow AI)
• Cloud account and API key reviews
• Automated hooks from MLOps platforms and model registries
Step 3 - Registration and classification
Each discovered asset is recorded with its metadata and assigned a risk tier. Classification drives everything that follows: a high-risk model requires a full AI impact assessment, bias testing, security testing, human oversight design, and executive approval; a low-risk tool may require only basic registration and acceptable use confirmation. Classification criteria commonly consider impact on individuals, use of sensitive data, autonomy of decisions, regulatory applicability, and exposure to external users.
Step 4 - Integration with governance gates
The registry and inventory become control points in the AI lifecycle. Promotion from staging to production requires that the model is registered, has passed required evaluations, and is approved by designated authorities. This is where the model registry provides enforcement: deployment pipelines can be configured to reject models not in an approved state. Integrity controls such as cryptographic hashes or signatures stored in the registry protect against model tampering and substitution in the supply chain.
Step 5 - Maintenance and reconciliation
Inventories decay quickly. Processes must include periodic recertification by owners, automated reconciliation against discovery sources, change management triggers (any model retrain, version change, new data source, or new use case requires an inventory update), and metrics such as percentage of discovered assets that are registered, percentage of records recertified on schedule, and number of unregistered AI tools detected.
Step 6 - Retirement
When a model or system is decommissioned, the record is updated rather than deleted to preserve auditability. Retirement should address secure disposal or archival of model artifacts and data, revocation of access and API keys, and notification of dependent systems.
5. Roles and Responsibilities
• AI governance committee / Chief AI Officer - sets policy, approves classification scheme, owns the program
• AI security manager - ensures inventory completeness, uses it to scope security assessments and monitoring, drives shadow AI detection
• Business owner - accountable for the asset, its use case, and recertification
• Technical owner / ML engineers - maintain registry entries, versions, and lineage
• Data steward / privacy office - validates data classification and lawful basis
• Procurement / vendor management - ensures third-party AI is registered at onboarding
• Internal audit - independently verifies accuracy and completeness
6. Framework and Regulatory Alignment
• NIST AI RMF - The Govern and Map functions call for establishing accountability, documenting AI systems, and understanding context and dependencies; an inventory operationalizes these.
• ISO/IEC 42001 - The AI management system standard expects organizations to identify AI systems within scope, assign roles, and maintain documented information, including AI system impact assessments.
• EU AI Act - Classifies AI into risk categories and requires providers and deployers of high-risk systems to maintain technical documentation and, in certain cases, register in an EU database. Knowing which systems fall into which category is impossible without an inventory.
• ISO/IEC 27001 and traditional IT governance - Asset management is a longstanding control objective; AI inventory extends it to a new asset class and should integrate with, not duplicate, existing asset management.
7. Common Challenges
• Shadow AI and embedded AI features that bypass procurement
• Rapid version churn making records stale
• Lack of a clear definition of what counts as AI
• Multiple registries across teams and clouds without a federated view
• Vendors unwilling to disclose model details, requiring contractual transparency requirements
• Treating the inventory as a one-time project instead of a continuous process
8. Exam Tips: Answering Questions on AI Asset Inventory and Model Registry
Tip 1 - The inventory is almost always the first step. When a question asks what an AI security manager should do FIRST when establishing a program, assessing AI risk, responding to a new regulation, or addressing shadow AI, the answer that involves identifying or inventorying AI assets typically outranks options about implementing controls, training, or buying tools. You cannot assess or protect what has not been identified.
Tip 2 - Distinguish inventory from registry. Expect questions that test whether you know the difference. Governance, ownership, risk classification, third-party and enterprise-wide scope point to the inventory. Versioning, lineage, reproducibility, rollback, promotion gates, and artifact integrity point to the model registry.
Tip 3 - Think in ISACA terms: governance over technology. ISACA exams favor answers emphasizing accountability, defined ownership, policy, and alignment with business objectives over purely technical fixes. If one option says assign a business owner and risk classification to every AI asset and another says deploy a scanning tool, the governance answer is usually best unless the question is specifically about detection methods.
Tip 4 - Shadow AI questions. The best detective method is typically network or proxy monitoring, CASB, and DLP telemetry combined with procurement review; the best preventive method is policy plus an approved tool catalog and easy registration process. Blocking everything outright is rarely the best answer because it drives usage further underground and conflicts with business enablement.
Tip 5 - Completeness and currency are the quality measures. If asked how to evaluate the effectiveness of an inventory, look for answers about reconciliation against independent discovery sources, periodic owner recertification, and coverage metrics rather than the number of fields captured.
Tip 6 - Integrate, do not duplicate. Answers that integrate AI inventory with the existing CMDB, IT asset management, and vendor management processes are preferred over standalone spreadsheets or parallel systems.
Tip 7 - Risk classification drives control intensity. Questions about how much assessment a given system needs are answered by its risk tier in the inventory. High-impact, sensitive-data, autonomous-decision systems get the most rigorous treatment.
Tip 8 - Lifecycle and change triggers. Retraining, new data sources, new use cases, and vendor model updates all require inventory and registry updates and possibly re-assessment. A model approved for one purpose is not automatically approved for another.
Tip 9 - Supply chain and integrity. If a scenario involves downloaded open-source or vendor models, look for answers about recording provenance, verifying integrity through hashes or signatures, and documenting licensing in the registry.
Tip 10 - Eliminate extremes. Options that say inventory only high-risk models, rely solely on self-reporting, or delete records of retired models are usually wrong. Comprehensive scope, multiple discovery sources, and retained audit history are the defensible positions.
9. Worked Example
Scenario: An organization discovers that several departments are using generative AI services that were never reviewed by security. The AI security manager has been asked to address the situation. What should be done FIRST?
A. Block all access to external generative AI services at the firewall
B. Conduct discovery to identify and register all AI tools and use cases currently in operation
C. Deliver mandatory AI awareness training to all staff
D. Perform penetration testing of the generative AI services
Analysis: Option A is an extreme control that disrupts business and does not provide understanding. Options C and D are valid later activities but presuppose knowledge of what exists and where risk lies. Option B establishes visibility, ownership, and risk classification, enabling informed decisions about the other actions. Correct answer: B.
10. Key Takeaways
• The AI asset inventory is the foundation of AI governance and security; it defines program scope.
• The model registry is the technical system of record for model versions, lineage, and lifecycle state, and can enforce deployment gates.
• Every asset needs an owner, a purpose, a risk classification, and a lifecycle status.
• Discovery must use multiple sources, especially to find shadow and embedded AI.
• Inventories require continuous maintenance, reconciliation, and recertification.
• On the exam, identify first, govern over technology, integrate with existing processes, and let risk classification drive control intensity.
Test mode:
AAISM - AI Asset Inventory and Model Registry Example Questions
Test your knowledge of AI Asset Inventory and Model Registry
Question 1
A third-party foundation model used across an enterprise is reported to contain a hidden backdoor. The model registry lists every deployed model with its owner, version and approval status. Even so, the security team needs several days to identify the affected production applications, because different business units had fine-tuned their own variants from that model. Which registry deficiency MOST contributed to the delay?
Question 2
A regulator asks an enterprise which model version produced a contested credit decision several months ago, and which training dataset that version used. Several retrained versions have been deployed since then. Which model registry capability would BEST allow the information security manager to answer this request?
Question 3
An enterprise's model registry records each model's name, version, owner and approval status. While investigating suspected data poisoning in a production fraud detection model, incident responders could not determine which datasets had been used to train the deployed version. Which registry enhancement would BEST close this gap?
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20 questions (total)
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