DISC InfoSec blogInvisible AI Threats Archives

Sep 15 2025

The Hidden Threat: Managing Invisible AI Use Within Organizations

Category: AI,AI Governance,Cyber Threats — disc7 @ 1:05 pm

Hidden AI activity poses risk
A new report from Lanai reveals that around 89% of AI usage inside organizations goes unnoticed by IT or security teams. This widespread invisibility raises serious concerns over data privacy, compliance violations, and governance lapses.
How AI is hiding in everyday tools
Many business applications—both SaaS and in-house—have built-in AI features employees use without oversight. Workers sometimes use personal AI accounts on work devices or adopt unsanctioned services. These practices make it difficult for security teams to monitor or block potentially risky AI workflows.
Real examples of risky use
The article gives concrete instances: Healthcare staff summarizing patient data via AI (raising HIPAA privacy concerns), employees moving sensitive, IPO-prep data into personal ChatGPT accounts, and insurance companies using demographic data in AI workflows in ways that may violate anti-discrimination rules.
Approved platforms don’t guarantee safety
Even with apps that have been officially approved (e.g. Salesforce, Microsoft Office, EHR systems), embedded AI features can introduce new risk. For example, using AI in Salesforce to analyze ZIP code demographic data for upselling violated regional insurance regulations—even though Salesforce itself was an approved tool.
How Lanai addresses the visibility gap
Lanai’s solution is an edge-based AI observability agent. It installs lightweight detection software on user devices (laptops, browsers) that can monitor AI activity in real time—without routing all traffic to central servers. This avoids both heavy performance impact and exposing data unnecessarily.
Distinguishing safe from risky AI workflows
The system doesn’t simply block AI features wholesale. Instead, it tries to recognize which workflows are safe or risky, often by examining the specific “prompt + data” patterns, rather than just the tool name. This enables organizations to allow compliant innovation while identifying misuse.
Measured impact
After deploying Lanai’s platform, organizations report marked reductions in AI-related incidents: for instance, up to an 80% drop in data exposure incidents in a healthcare system within 60 days. Financial services firms saw up to a 70% reduction in unapproved AI usage in confidential data tasks over a quarter. These improvements come not necessarily by banning AI, but by bringing usage into safer, approved workflows.

Source: Most enterprise AI use is invisible to security teams

On the “Invisible Security Team” / Invisible AI Risk

The “invisible security team” metaphor (or more precisely, invisible AI use that escapes security oversight) is a real and growing problem. Organizations can’t protect what they don’t see. Here are a few thoughts:

An invisible AI footprint is like having shadow infrastructure: it creates unknown vulnerabilities. You don’t know what data is being shared, where it ends up, or whether it violates regulatory or ethical norms.
This invisibility compromises governance. Policies are only effective if there is awareness and ability to enforce them. If workflows are escaping oversight, policies can’t catch what they don’t observe.
On the other hand, trying to monitor everything could lead to overreach, privacy concerns, and heavy performance hits—or a culture of distrust. So the goal should be balanced visibility: enough to manage risk, but designed in ways that respect employee privacy and enable innovation.
Tools like Lanai’s seem promising, because they try to strike that balance: detecting patterns at the edge, recognizing safe vs unsafe workflows rather than black-listing whole applications, enabling security leaders to see without necessarily blocking everything blindly.

In short: yes, lack of visibility is a serious risk—and one that organizations must address proactively. But the solution shouldn’t be draconian monitoring; it should be smart, policy-driven observability, aligned with compliance and culture.

Here’s a practical framework and best practices for managing invisible AI risk inside organizations. I’ve structured it into four layers—Visibility, Governance, Control, and Culture—so you can apply it like an internal playbook.

1. Visibility: See the AI Footprint

AI Discovery Tools – Deploy edge or network-based monitoring solutions (like Lanai, CASBs, or DLP tools) to identify where AI is being used, both in sanctioned and shadow workflows.
Shadow AI Inventory – Maintain a regularly updated inventory of AI tools, including embedded features inside approved applications (e.g., Microsoft Copilot, Salesforce AI).
Contextual Monitoring – Track not just which tools are used, but how they’re used (e.g., what data types are being processed).

2. Governance: Define the Rules

AI Acceptable Use Policy (AUP) – Define what types of data can/cannot be shared with AI tools, mapped to sensitivity levels.
Risk-Based Categorization – Classify AI tools into tiers: Approved, Conditional, Restricted, Prohibited.
Alignment with Standards – Integrate AI governance into ISO/IEC 42001 (AI Management System), NIST AI RMF, or internal ISMS so that AI risk is part of enterprise risk management.
Legal & Compliance Review – Ensure workflows align with GDPR, HIPAA, financial conduct regulations, and industry-specific rules.

3. Controls: Enable Safe AI Usage

Data Loss Prevention (DLP) Guardrails – Prevent sensitive data (PII, PHI, trade secrets) from being uploaded to external AI tools.
Approved AI Gateways – Provide employees with sanctioned, enterprise-grade AI platforms so they don’t resort to personal accounts.
Granular Workflow Policies – Allow safe uses (e.g., summarizing internal docs) but block risky ones (e.g., uploading patient data).
Audit Trails – Log AI interactions for accountability, incident response, and compliance audits.

4. Culture: Build AI Risk Awareness

Employee Training – Educate staff on invisible AI risks, e.g., data exposure, compliance violations, and ethical misuse.
Transparent Communication – Explain why monitoring is necessary, to avoid a “surveillance culture” and instead foster trust.
Innovation Channels – Provide a safe process for employees to request new AI tools, so security is seen as an enabler, not a blocker.
AI Champions Program – Appoint business-unit representatives who promote safe AI use and act as liaisons with security.

5. Continuous Improvement

Metrics & KPIs – Track metrics like % of AI usage visible, # of incidents prevented, % of workflows compliant.
Red Team / Purple Team AI Testing – Simulate risky AI usage (e.g., prompt injection, data leakage) to validate defenses.
Regular Reviews – Update AI risk policies every quarter as tools and regulations evolve.

✅ Opinion:
The most effective organizations will treat invisible AI risk the same way they treated shadow IT a decade ago: not just a security problem, but a governance + cultural challenge. Total bans or heavy-handed monitoring won’t work. Instead, the framework should combine visibility tech, risk-based policies, flexible controls, and ongoing awareness. This balance enables safe adoption without stifling innovation.

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