Reduce shadow AI by transitioning from “forbidden” to “governed.” Identify unauthorized tools via network telemetry, provide secure, sanctioned AI alternatives that meet CJIS or HIPAA standards, and implement a continuous monitoring framework to ensure sensitive data never enters public training sets.
Imagine a public safety dispatch center in Grove City, Ohio. An officer, tasked with summarizing a series of complex incident reports for a shift briefing, decides to use a free, public LLM to speed up the process. They paste a few redacted—but still detailed—case summaries into the prompt. Within seconds, the officer has a perfectly formatted briefing. But in that moment, the agency has just suffered a data breach. That sensitive information is now part of a public training set, and the agency is suddenly in the crosshairs of a CJIS (Criminal Justice Information Services) Security Policy audit 1.
This is the reality of “Shadow AI.” It isn’t born out of malice; it’s born out of a desire for efficiency. Your teams in Dublin, OH, or your school districts in the Central Valley aren’t trying to bypass security—they’re trying to do their jobs faster. But when the tool they use isn’t sanctioned by IT, they are creating a massive, invisible risk profile that your current security stack might not even be seeing.
The Shadow AI Paradox: Why “Just Saying No” Fails
For too long, the standard MSP response to new, risky technology has been the “Block List.” You block the URL, you disable the API, and you tell the staff, “This is against policy.”
In the world of AI, that approach is an absolute failure. AI tools are now embedded in everything from browser extensions to PDF readers and project management software. If you simply block the top three public LLMs, your employees will find a fourth, fifth, or sixth option—perhaps a “productivity wrapper” that promises more privacy but actually sends data to an unverified server in another country.
When you ban a tool that solves a real pain point—like a Fresno-area district administrator trying to analyze student performance trends across 10,000 records—you don’t stop the behavior; you just push the behavior into the shadows. This is how you end up with PII (Personally Identifiable Information) leaking into public models, which is a nightmare for any organization governed by FERPA or HIPAA.
How do we actually find shadow AI without spying on employees?
Finding shadow AI doesn’t require you to look over every employee’s shoulder. It requires a shift in how we analyze network telemetry. Most organizations are looking for “known bad” signatures. To find shadow AI, we look for “known patterns of unauthorized data movement.”
Analyzing DNS and Firewall Logs
We start by auditing DNS requests. If we see a surge in traffic to domains associated with AI wrappers or “AI assistants” that aren’t on the sanctioned list, we have a lead. We don’t just look for “openai.com”; we look for the thousands of API-driven mirrors that provide a similar interface.
CASB and Cloud Access Security Brokers
For our clients in the Columbus and New Albany areas, we often implement Cloud Access Security Brokers (CASB). A CASB allows us to see not just that a user is visiting a site, but what they are doing. Are they uploading a CSV? Are they pasting a 2,000-word block of text? By identifying these patterns, we can pinpoint exactly which departments are relying on shadow AI to get their work done.
Identifying “AI-Adjacent” Software
Shadow AI isn’t always a standalone website. It’s often a feature in a tool you already pay for. Suddenly, your project management tool has an “AI Summary” button. If that feature hasn’t been vetted for HIPAA or CJIS compliance, using it constitutes shadow AI. We help you audit your existing SaaS stack to see which “AI features” are turned on by default and whether they adhere to your data residency requirements.
The Governance Bridge: From “No” to “Here is How”
The goal isn’t to stop AI usage—it’s to move the usage from an unmanaged environment to a managed one. We call this the “Governance Bridge.” Instead of a policy that says “Do not use AI,” we create a policy that says, “Here is the secure, sanctioned way to use AI at this company.”
This requires providing an alternative that is just as easy to use as the public version but provides the security guarantees your industry demands. For a clinic in Dublin, OH, this means an enterprise instance of an LLM where the provider guarantees that data is not used for training and is encrypted at rest.
Comparing AI Deployment Models for Regulated Industries
To choose the right path, you need to understand the trade-offs between convenience and compliance. Here is how we categorize the options for our mid-market and government clients:
| Model Type | Data Privacy | Compliance Fit | Performance | Best Fit For… |
|---|---|---|---|---|
| Public LLM | Low (Training data) | None | Extremely High | General research, non-sensitive brainstorming |
| Enterprise LLM | Medium (SaaS Agreement) | HIPAA/SOC2 | High | Mid-market business operations, generic corporate use |
| Private/Local LLM | High (Air-gapped/VPN) | CJIS/HIPAA | Medium | Public safety, healthcare clinics, high-security government |
| Hybrid AI Gateway | High (Filtered) | Tailored | High | Large K-12 districts, county IT portfolios |
What are the specific risks for our verticals?
Not all shadow AI is created equal. The risk profile changes based on the data being handled.
Public Safety and Government (CJIS)
In a Columbus-area dispatch center, the risk is the violation of the CJIS Security Policy. If sensitive criminal justice information is leaked into a public model, it can compromise active investigations and lead to severe federal penalties. A sanctioned AI environment must ensure that data remains within the US and is accessed only by authorized personnel with a background check.
Healthcare and Clinics (HIPAA)
For a clinic in Dublin or New Albany, the risk is a HIPAA violation. Using a public AI to summarize patient notes or analyze EHR (Electronic Health Record) data without a Business Associate Agreement (BAA) in place is a direct breach of federal law. We ensure that any AI tool deployed in a medical environment is backed by a signed BAA and strictly adheres to the HIPAA Security Rule.
K-12 School Districts (FERPA)
For districts in the Central Valley or Fresno area, the focus is on student privacy. AI tools used by teachers to generate lesson plans are usually fine; however, using AI to analyze student IEPs (Individualized Education Programs) or behavioral data without a FERPA-compliant agreement is a major risk. We help districts establish a “Sanctioned Tool List” so teachers have a safe way to leverage AI without risking student data.
The Sanctioned AI Implementation Checklist
If you suspect shadow AI is proliferating in your organization, don’t start by firing people—start by building the framework. Use this checklist to move toward a governed environment:
- Audit Network Telemetry: Review DNS and firewall logs for unauthorized AI domain access.
- Inventory SaaS Features: Identify which existing software tools have enabled AI features by default.
- Define Data Tiers: Categorize your data into “Public,” “Internal,” and “Restricted” (e.g., PII/PHI) to determine what can be processed by which AI model.
- Procure an Enterprise Agreement: Secure a version of the tool that includes a BAA or a CJIS-compliant data residency guarantee.
- Establish an AI Acceptable Use Policy (AUP): Clearly state what is allowed, what is forbidden, and where the sanctioned tools are located.
- User Training: Teach employees how to “prompt” without including PII, and show them the benefits of the sanctioned tool over the public one.
- Continuous Monitoring: Use a CASB or similar tool to ensure the “bridge” is working and that users aren’t slipping back into shadow tools.
Moving toward Accountability
At Datapath, we don’t just provide “IT support”; we provide outcomes. In the case of shadow AI, that outcome is accountability. You should know exactly where your data is going and who is processing it.
Whether you are managing a healthcare facility in the Dublin metro or a government office in the Central Valley, the goal is the same: uptime, security, and absolute compliance. If you aren’t sure whether your team is using unauthorized AI tools, or if you need help standing up a secure, sanctioned environment, we can help.
Explore our cybersecurity services or our compliance support to learn how we secure regulated industries across Ohio and California. If you’re looking for a partner who understands the specific needs of the Dublin, OH or Fresno/Central Valley markets, let’s start a conversation about your AI roadmap.
