You might have firewalls and discovery scans in place, but a new kind of risk is slipping right past them. Your clients’ employees just want to get their work done faster, but that drive for efficiency is creating a massive blind spot. We call it shadow AI, and it happens when employees grab AI tools like ChatGPT, Claude or even sneaky AI features baked into everyday apps without running it by IT or security first.
As an MSP serving businesses, you know this is a step beyond classic shadow IT. These tools often hide in browser extensions, personal SaaS logins or plugins within approved apps like Microsoft Office or CRMs, dodging your usual firewalls and discovery scans.
Reports show up to 70% of workers are already doing this, turning quick productivity boosts into hidden risks for your clients.
Real-world consequences
To understand the stakes, we have to look beyond the statistics. Real-world examples make the problem crystal clear.
- Customer data leaks: Picture a support rep pasting full customer tickets with information like names, emails and even payment details, into a free chatbot for instant replies, only for that data to get stored or fed into the AI’s training model.
- Financial exposure: Finance teams often upload expense spreadsheets to AI summarizers, accidentally leaking vendor info.
- IP theft: Marketers feed client campaign data into copywriting AIs, or developers test code snippets in unapproved copilots.
We’ve seen high-profile cases like Samsung banning ChatGPT after engineers leaked chip designs and Amazon restricting tools after confidential code went public. These incidents mirror what smaller clients face daily, with 57-68% of employees admitting to inputting sensitive info into personal AI accounts.
The business impact
The business fallout of shadow AI is brutal and hits your clients where it hurts. Here’s what’s at stake:
- Data exfiltration: Trade secrets, intellectual property or customer PII end up in uncontrolled systems, sparking breaches, NDA violations, regulatory issues or mandatory notifications.
- Compliance failures: GDPR, HIPAA, PCI DSS or SOC 2 requirements crumble when you can’t prove data residency, retention or processing standards.
- Audit nightmares: AI-driven decisions, like HR hires, sales forecasts or vendor picks, become untraceable black boxes, inviting lawsuits or audits with no defense
- Vendor sprawl: An unknown ecosystem of AI vendors weakens your security posture and increases risk
- Incident response chaos: IT can’t respond effectively to breaches when they don’t even know which tools are in use
How to help your clients fight back
You can help your clients take control by implementing a rock-solid AI Acceptable Use Policy. Position this not just as a rule, but as your MSP differentiator.
Here’s a framework to keep it simple and effective:
- Define the terms: Clearly define “AI tools” (chatbots, copilots, generators) and “shadow AI” as any unapproved use.
- Classify data: Strictly ban inputting regulated data, secrets, customer info or IP into non-vetted tools. Only “public” data should be allowed, and even then through approved channels.
- Mandate a fast-track approval process: Staff should submit requests via ticket with the business need, data types and vendor details.
- Inventory everything: Centrally inventory all tools as approved, limited or banned with quarterly reviews.
- Require security checks: Before greenlighting a tool,require SOC 2 reports, data processing agreements, responsible AI governance and sub-processor lists.
To make this stick, assign owners: IT for tech, legal for compliance and business leads for use cases. Roll out short annual training with real examples of “bad prompts“ so employees understand the risk. And finally, include monitoring notices and tie violations to existing discipline rules.
Prevention through browser security
Policies are essential, but technical controls are what verify them. ean heavily into browser controls you already should be managing for clients.
- Lock down the browser: Use DNS filtering, content filters or browser security tools (like DefensX) to block public AI domains while whitelisting safe ones.
- Create security baselines: Lock browsers to managed profiles and security configurations.
- Audit and zap: Audit fleets weekly to spot new AI add-ons and remove them immediately.
- Deploy DLP: Use DLP or CASB to scan uploads and halt sensitive patterns (PII, code, contracts) heading to AI sites.
- SSO + MFA: Enforce SSO and MFA for approved tools only, and pipe logs from proxies, EDR and browsers into one dashboard to flag anomalies like sudden AI traffic spikes.
To avoid pushback, provide alternatives like enterprise AI in M365, governed IDE copilots or custom workflows that deliver the speed without the risk.
Turn a liability into an opportunity
As an MSP, you must own this space. Craft policies, deploy controls, run trainings and report on shadow AI trends monthly. You can turn a lurking liability into a revenue stream by securing AI governance as a managed service. This protects data, nails compliance and lets clients innovate safely. Your clients win big, and you become their go-to AI security partner.
Shadow AI isn’t going away. Your clients’ employees will keep using these tools because they’re fast, helpful and easy to access. The question now is whether you’ll help them use AI safely. By stepping in with clear policies, smart controls and ongoing education, you turn a hidden risk into a competitive advantage.
Don’t let shadow AI catch your clients off guard. Start the conversation today.
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