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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsYou cannot secure AI or machine-learning assets your organization does not know exist. Start by building and maintaining an inventory of models, data, pipelines, dependencies, identities, endpoints, environments, and the flows between them; then use it to prioritize and apply controls.
Why discovery comes before AI/ML security controls
Security teams cannot reliably assign an owner, judge exposure, or assess business impact for an unknown model or endpoint. The National Institute of Standards and Technology (NIST) describes discovery and cataloging of enterprise identities, assets, and data flows as an initial step in zero-trust architecture planning. The same operational logic applies to AI/ML: establish what exists and how it connects before deciding which policies and safeguards are needed.
AI systems inherit familiar software and infrastructure risks, including vulnerable dependencies, excessive permissions, and exposed services. They also introduce or amplify risks such as data poisoning, adversarial examples, model extraction, privacy leakage, and prompt injection. NIST’s March 2025 AI 100-2 E2025 publication provides a taxonomy of attacks, including evasion, poisoning, privacy, and misuse attacks across predictive and generative AI. OWASP’s ML Operations guidance describes practical concerns such as malicious serialized model files, exposed MLflow instances, and legacy test models left in production.
What belongs in an AI/ML asset inventory?
Do not limit the inventory to model files. Treat the system as a chain of artifacts, services, people, and data flows. Record a reference to secrets or credentials, not the secret itself.
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| Asset group | What to identify |
|---|---|
| Models | Model name and version, source or registry, purpose, owner, training or fine-tuning lineage, and deployment status. |
| Data | Training, validation, fine-tuning, and inference data sources; dataset versions; lineage; data classification; and whether personal information is involved. |
| Build and serving chain | Training and deployment pipelines, code repositories, registries, serialized artifacts, libraries and other dependencies, configuration, and environment. |
| Access and exposure | Service identities and roles, endpoint or API location, network exposure, serving credentials, and the users or systems that can invoke the model. |
| Flows and accountability | Data sent to and returned from the system, upstream and downstream connections, business purpose, accountable owner, provenance, license, and last-verified date. |
This is an AI-bill-of-materials-style record, not a claim that one universal AI-BOM format covers every organization. The useful minimum is enough information to establish what an asset is, where it came from, who is responsible for it, what it depends on, what data it touches, and where it runs.
How to find models and unknown ML endpoints
- Set scope and ownership. Bring together security, data science, engineering, procurement, and business teams. Decide which business units, cloud accounts, environments, and third-party services are in scope, and name who can resolve ownership questions.
- Collect records from multiple systems. Review cloud accounts, source-code repositories, CI/CD systems, model registries, data catalogs, API or endpoint gateways, identity providers, and network telemetry. Each view is incomplete on its own: for example, a registry can show model versions but not necessarily exposed endpoints or the identities allowed to call them.
- Normalize records. Give each discovered item a consistent identity and capture its type, owner, purpose, version, provenance, license, dependencies, environment, endpoint, relevant identities, data classification, and update date. Link components that form one deployed system instead of treating them as unrelated rows.
- Reconcile and investigate. Merge duplicate records while preserving distinct versions and deployments. Follow up on assets with no owner, missing lineage, unknown endpoints, or unclear status. OWASP warns that legacy test models can remain in production and that MLflow instances may be exposed; check staging and development environments as well as production.
- Classify exposure and impact. Assign tiers based on factors such as data sensitivity, public or internal reachability, business function, and the consequences of misuse or outage. Use those tiers to sequence threat analysis and remediation rather than assuming every model has the same risk.
- Record gaps and exceptions. If a source system cannot provide reliable version, ownership, or lineage information, mark the field as unknown and assign follow-up work. An explicit gap is more actionable than a confident but unsupported entry.
How to turn the inventory into security controls
Use each asset record to map relevant threats to the part of the system that can address them. NIST’s AI 100-2 E2025 threat taxonomy and OWASP’s ML operations examples help distinguish model-specific attacks from ordinary infrastructure and software weaknesses.
- Artifacts and dependencies: record source, version, license, and lineage; assess integrity; and scan externally sourced serialized model files and dependencies before loading or deploying them.
- Identities and pipelines: constrain permissions for training, deployment, and serving identities to the tasks they need. Protect CI/CD and registry access so an unauthorized change cannot quietly replace a model or its dependencies.
- Endpoints and inference paths: identify who can reach each endpoint, limit serving credentials, and apply protections appropriate to the data and business function. Monitor inputs and outputs for abuse or unexpected behavior, including prompt-injection attempts where generative systems are involved.
- Privacy and identity use: identify systems processing personal information and assess privacy risks. NIST’s Digital Identity Guidelines call for AI/ML use in identity systems to be documented and communicated to relying entities, including training methods, datasets, update frequency, and testing results.
Inventory is not a substitute for threat modeling or testing. It provides the map those activities need: what to examine, who owns it, and which data, identities, and services are in the path.
How to keep the inventory accurate
AI/ML deployments change as models are retrained, dependencies are updated, endpoints are created, and access is modified. NIST notes that AI security challenges are rapidly evolving, so a one-time discovery exercise will become stale.
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- Schedule recurring discovery scans across the systems that hold asset records.
- Trigger inventory updates when a model is registered, promoted, deployed, replaced, or retired, and when CI/CD or identity configuration changes.
- Assign an owner and remediation status to discovered gaps, exposed services, and unmanaged assets; retain an audit history of changes.
- Reconcile inventory records against cloud, registry, endpoint, and identity sources so orphaned assets and inactive-looking but reachable deployments are not overlooked.
How to evaluate AI asset-discovery approaches
Whether the process is built from existing security platforms or supported by specialized tooling, assess it against the same operational questions:
- Coverage: Does it discover models, datasets, pipelines, dependencies, endpoints, identities, and data flows, or only model files?
- Freshness: Does it update from deployment and configuration events, periodic scans, or both?
- Provenance: Can it preserve source, version, license, lineage, and evidence of artifact integrity?
- Runtime visibility: Can it identify endpoint exposure and help monitor inference inputs, outputs, and abuse?
- Ownership and workflow: Can teams assign owners, track remediation, and review an audit history?
- Integration: Does it connect to the organization’s cloud platforms, registries, CI/CD, identity management, SIEM, and data catalogs?
No single source should be assumed to reveal every AI/ML asset. A dependable inventory combines evidence from the systems where assets are built, stored, deployed, accessed, and used—and makes unresolved gaps visible.
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