Audits and lawsuits around AI misuse are accelerating - and one question keeps catching organizations off guard:
Where are we using AI?
If you can’t answer that confidently, you’ve got work to do.
Here’s how to get ahead.
What is AI asset management?
AI asset management involves cataloguing every AI component your organization touches, including:
Models & Systems: Any AI functionality your organization uses like chatbots, recommendation engines, or embedded AI features in vendor tools
Data Assets: The datasets used to train or fine-tune AI systems, including user data, documents, and metadata
Prompts & Templates: Instructions or configurations that tell AI what to do, including automated workflows and prompt testing
Infrastructure & Tools: The software and systems powering your AI - APIs, cloud services, and deployment platforms
Governance Info: Who owns each asset, how risky it is, its compliance status, and whether it’s been reviewed or audited
Without an inventory of your AI assets, security teams are left in the dark.
Tailoring AI asset management to deployment type
Not all AI is deployed the same way. Your tracking and governance approach should reflect this.
Vendor-Managed AI Systems
Tools and models built and managed by third-party vendors.
Embedded Features (e.g., Zoom, Canva): Document what AI features are built-in and where user data is processed.
User Interfaces (e.g., ChatGPT, Gemini): Track who’s using them, what licenses you hold, and make sure activity is logged.
APIs (e.g., OpenAI, Anthropic): Secure API keys, monitor usage, and validate data going in and out.
Enterprise Licenses (e.g., Microsoft Copilot, Google Gemini): Record setup details, permissions, and change approvals.
Enterprise-controlled AI systems
Models you manage directly, whether partially or fully developed in-house.
Pre-Trained + Fine-Tuned Models: Track which base models you're using, what data they were fine-tuned on, and maintain documentation.
Specialized Fine-Tunes: Assign clear ownership, monitor output behavior, and log explanations for decisions.
Fully Custom Models: Govern everything - from architecture and training data to infrastructure and risk classification - throughout the model’s lifecycle.
Aligning AI asset management to each deployment type gives you the detail needed to take meaningful, actionable steps.
Why AI asset management is now a C-level priority
1. Security: AI Is the new attack surface
AI tech platforms can expose organizations to threats such as prompt injection, data leakage, bias and biometric misuse.
AIUC-1 is a trusted framework for evaluating enterprise AI risks - covering privacy, security, safety, reliability, and accountability. It includes third-party audits and technical tests like adversarial and hallucination checks, and was developed by leaders including MITRE, Stanford, and Google Cloud.
AIUC-1 standards require robust asset management:
Model Deployment Protection: Secure where and how your AI models are accessed - encrypt endpoints, limit who can use them, and run regular integrity checks to ensure they haven’t been tampered with.
Logging: Keep records of what goes into and comes out of your AI systems so you can investigate and respond quickly if something goes wrong.
2. AI compliance: global laws are catching up fast
Regulations like the EU AI Act, GDPR, Colorado AI Act, and NYC Local Law 144 require:
Independent bias audits
Documentation and transparency
Human oversight of high-risk AI
The HR Tech assessment showed most platforms lack transparency documentation and only 3 out of 100 platforms had completed independent bias audits.
3. Strategic value: cut redundancy, reduce risk
Asset inventories help eliminate waste and identify high-risk vendors early.
Workday customers that used its AI hiring tools are now at risk of being scrutinized in a federal age discrimination lawsuit. These organizations face reputational harm and potential legal exposure.
The C-suite must act now to secure AI, ensure compliance, and unlock its full value.
A practical roadmap: how to manage your AI assets
AI asset management should include:
Discovery: Scan for AI APIs, survey departments, pull procurement records
Classification: Apply risk frameworks like AIUC-1, NIST AI RMF, EU AI Act
Governance: Assign ownership, log change approvals, implement policies
Automation: Enable continuous monitoring, repo scans, and compliance dashboards
Measuring success
Tracking the right metrics helps security leaders prove impact, prioritize resources, and demonstrate readiness to both executives and regulators. Useful measures include:
Coverage: % of known AI usage captured
Risk Reduction: Number of high-risk assets mitigated
Compliance Readiness: Time to produce regulator-ready documentation
Cost Optimization: Savings from eliminating redundant tools
The bottom line
One thing’s for sure, AI is here to stay. If you're not tracking where and how it's being used across your enterprise, you're exposing yourself to regulators, legal action and bias audits.
HR tech has become ground zero for AI accountability and regulators are watching closely wherever AI affects people. Creating solid AI asset management now gives you a strategic head start.
But AI doesn’t have to be a C-suite headache. With the right asset management, your organization gains:
Faster compliance reporting
Stronger security postures
Reduced liability exposure
Better visibility for strategic AI investments
Deploy AI with confidence: automate asset management and eliminate blind spots
You can manage your AI assets seamlessly within the SPLX enterprise platform. It’s a fast, reliable solution that gives security teams full, automated visibility across their AI stack.
Map models, workflows, and infrastructure in one place - streamlining inventory, simplifying compliance, and reducing risk at scale.
👉 Learn more about our AI asset management feature.
Table of contents
