With AI adoption increasing, it's no longer just early adopters experimenting with the latest automation or chatbot. AI is now mainstream. It's used day to day across almost every type of business — small law firms, credit unions, clinics, accounting firms — often as part of core workflows.

In this blog series, we'll look at how AI impacts MSPs and their customers when it comes to security risk, and also the business opportunities this shift creates.

What Do We Mean by AI?

When we say "AI" in this context, we're usually talking about large language models (LLMs) and AI-powered assistants that can perform tasks which traditionally required human judgment — answering complex questions, summarizing information, conducting research, or reasoning across multiple data sources.

What makes these systems different from traditional software is that they are non-deterministic and context-driven. The quality of their output depends directly on:

The data they can see
The systems they can access
The tools they're allowed to use

That dependency on broad context is what fundamentally changes the security model.

How Is AI Actually Being Used by Businesses?

In many white-collar professions, a large portion of junior or entry-level work involves manual data collection, summarization, and analysis. AI is increasingly used to offload this work in industries like law, consulting, finance, healthcare, and tech.

Organizations ask questions like:

Would you want a 24-hour intelligent support function that improves customer satisfaction while lowering labor costs?
Would you rather have your best engineers writing notes, or building?

Naturally, there are real productivity gains here. But there's also a less discussed side: AI agents are often deployed quickly, with excessive permissions, little review, and almost no formal approval process.

From an MSP perspective, this matters because AI systems often end up with broader access than any single human user ever had.

Traditional SaaS vs AI Security Models

Most existing security controls were built around deterministic applications with relatively predictable data flows. AI breaks that assumption.

Traditional SaaS vs AI Security Models

AI introduces a reasoning layer that sits on top of your data and tooling. If security controls aren't applied at that layer, you're effectively blind to what the system is doing.

Foundational AI Risks

There are a lot of opportunities with AI, but as with most things in security, there's a clear Yin and Yang.

AI introduces a new attack surface by requiring large amounts of context (your data) and, increasingly, access to tools that can take action. Combined with the pace at which these systems are being adopted, this creates several foundational risks.

Foundational AI Risks

The more context and permissions an AI system has, the harder it becomes to reason about where data flows and what actions are possible.

Data Access & Tool Access

Users share sensitive data with AI tools, often unintentionally
AI tools are interconnected, meaning data shared in one context can surface in another
This can easily become data leakage, even without malicious intent

There's also the inverse problem:

Users may gain access to data they are not authorized to see through poorly scoped AI prompts
In malicious cases, this becomes prompt injection

And it's not just about reading data. Many AI systems now have write access:

Changing configurations
Running utilities
Triggering workflows

Autonomous or semi-autonomous agents with broad permissions can create significant risk very quickly.

Compliance & Audit Challenges

AI systems process and store sensitive data just like any other data processor. That means they are subject to the same regulatory requirements — HIPAA, GDPR, SOC 2, and others.

In practice, many AI tools lack:

Clear audit trails for inputs and outputs
Well-defined data retention policies
Transparent model training guarantees

This makes compliance conversations harder, especially in regulated SMB environments.

Hallucinations and Trust Risk

AI can synthesize large amounts of information, but it can also be wrong — confidently.

While hallucinations aren't a traditional security issue, they introduce business, legal, and reputational risk when AI output is treated as authoritative without verification.

Shadow AI

Shadow AI is already widespread.

Shadow AI

Employees use unauthorized AI tools, personal accounts, and AI features embedded into existing SaaS products — often without IT or MSP visibility. Every application is becoming an AI application, whether it's explicitly marketed that way or not.

Why This Matters for MSPs

AI doesn't replace existing security responsibilities — it adds to them.

Most MSP tooling today wasn't designed to understand:

Prompts and context
AI agent behavior
Tool-level authorization and chaining

Blocking AI outright is rarely realistic. Ignoring it is no longer viable.

MSPs will increasingly be expected to help customers use AI safely, with visibility, policy, and guardrails — not just say yes or no.

What's Next

In the next post, we'll look at why traditional endpoint, SaaS, and DLP controls struggle with AI use — and what practical controls MSPs can put in place today without killing productivity.

AI risk is now an MSP problem. Whether it becomes a liability or an opportunity depends on how it's handled.