Without an AI asset inventory, organizations are flying blind, exposing sensitive data and missing critical compliance risks.
Employees are using AI-powered tools—often without oversight—and organizations need to get a grip on this rapidly evolving landscape. Cataloging AI usage and ensuring sensitive data isn’t unintentionally exposed is now a fundamental responsibility. The days of setting policies and hoping for compliance are over. To govern AI effectively, you first need visibility.
The Critical Role of an AI Asset Inventory
Creating an AI asset inventory is the essential first step in establishing AI governance. Regulatory frameworks such as the EU AI Act, ISO 42001, and the NIST AI Risk Management Framework (AI RMF) now make this a foundational requirement. It’s no longer just a best practice—it’s becoming a compliance expectation.
But even defining what qualifies as AI is tricky. The EU AI Act takes an incredibly broad stance, capturing almost anything under its scope. Organizations need to decide what’s relevant to them. Should they track every AI-infused feature, or focus on generative AI tools, large language models, and content creation systems? Tightening the scope to specific AI categories can make this effort far more manageable.
Third-party vendor assessments are also starting to demand AI inventories, often calling them “audits” or “catalogs of services”. But beyond compliance, without a full understanding of what AI tools employees are using, it’s impossible to implement meaningful governance. Effective governance needs more than just what’s been formally purchased—it’s about uncovering the shadow AI that’s already woven into daily workflows.
The Growing Risks of Shadow AI
Unauthorized AI usage is a ticking time bomb. Employees are integrating AI tools into their work, sometimes unknowingly exposing sensitive data to third-party models. Recent incidents highlight these risks. Take DeepSeek, for example, which surfaced as a tool employees were using without security teams being aware. Or consider OmniGPT, which suffered a breach, potentially leaking confidential corporate data.
This kind of risk isn’t hypothetical—it’s already happening. Companies that aren’t actively monitoring AI usage are flying blind.
Why Tracking AI Usage Is So Challenging
Despite its importance, AI asset tracking remains difficult. Most organizations rely on outdated or ineffective methods to identify AI usage, and traditional IT governance tools fall short.
Security teams can take six key approaches to improve AI asset visibility:
- Procurement-Based Tracking – Effective for monitoring new AI acquisitions but fails to detect AI features added to existing tools.
- Manual Log Gathering – Analyzing network traffic and logs can help identify AI-related activity, though it falls short for SaaS-based AI.
- Cloud Security and DLP – Solutions like ZScaler and Netskope offer some visibility, but enforcing policies remains a challenge. Wiz and other CSPMs cna provide good insight into AWS/Google AI Use.
- Identity and OAuth – Reviewing access logs from providers like Okta or Entra can help track AI application usage.
- Extending Existing Inventories – Classifying AI tools based on risk ensures alignment with enterprise governance, but adoption moves quickly.
- Specialized Tooling – Continuous monitoring tools detect AI usage, including personal and free accounts, and identify the apps training on your data. This includes the likes of Harmonic Security
More Than Just a List: Understanding AI Risks
An AI asset inventory isn’t just about making a list—it’s about understanding the risks that come with AI adoption. Security leaders need to ask critical questions. Are these tools training on employee-provided data? What are their data retention policies? How do they handle privacy concerns under GDPR, HIPAA, or other regulations?
Generative AI within SaaS applications makes this even harder. A tool that wasn’t considered a risk yesterday may introduce new AI-powered features overnight. Relying solely on Cloud Access Security Brokers (CASB) or procurement approvals is no longer enough. Guardrails must be put in place to manage risk at scale.
Securing AI Usage: What’s Next?
Once organizations establish AI visibility, they must take the next step: protecting sensitive data from being shared with non-approved AI systems. Security teams should ask themselves what protections are in place to prevent employees from inadvertently exposing confidential information. Do employees even know which AI tools are safe to use?
However, it’s important to treat AI governance as an opportunity and not just risk mitigation. Companies that get ahead of AI tracking can proactively engage employees, guiding them toward secure, approved AI solutions. Security leaders who bring this data to AI committees and executive teams provide valuable insights into how AI is actually being used, rather than just theorizing about governance from a policy standpoint.
AI adoption is moving fast, and organizations that don’t act now risk falling behind. A well-implemented AI asset inventory ensures visibility, reduces risk, and lays the groundwork for responsible AI governance in an increasingly AI-powered world.
To learn more about how Harmonic Security can help track AI applications and usage for your organization, get a demo with our team.
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