Earlier this year, an employee at a Pennsylvania bank fed customer names, social security numbers, and birth dates into an unsanctioned AI tool. The immediate consequences were an SEC 8-K disclosure, a 36-hour notice to the bank’s regulator, and customer notifications. No hacker was involved, and the bank’s operations experienced no interruptions, largely because it had brought in a team of cybersecurity advisors and financial regulators to quickly mitigate the issue.
Now consider if the same mistake was made within a business that didn’t have access to enterprise-level budgets and infrastructure. The instinct in both cases is identical: an employee just trying to get their job done faster. The difference is whether proper guardrails were built in before access was granted to AI, or bolted on for the first time after something goes wrong. SMBs specifically can close this widening security gap without an enterprise-sized budget, but only by prioritizing proactive governance, and enlisting a partner that’s equipped to help enforce them.
Facing the AI Reality Check
From my own conversations with SMBs, I’ve learned one thing: contrary to leadership’s confidence that employees are acting with cybersecurity and safe data sharing in mind, most of their employees are using unapproved AI tools in the background, if approved tools and guidance have not explicitly been provided.
Nearly half of SMBs are still operating with no AI policy, even as adoption is snowballing. AI tools are simply evolving faster than the approved process, and leadership is behind in thoroughly defining where the boundaries are. Even small businesses with a policy on paper often lack the automation detection layer to catch a problem while it’s still small, so incidents are only surfacing after they’ve caused damage.
Recovery looks different by size too. The same low-impact security incident for a large enterprise can sink an SMB’s bottom line.
None of this is a reason to slow down or stop AI adoption, but a signal to take a hard look at existing frameworks now before the gap is discovered the hard way, and make sure AI is being held to the same standard already expected from any other system that touches business-critical data.
Hardening Governance Before Handing AI The Keys
As SMBs increasingly adopt agents that work independently with business data, effective policies can’t be an afterthought fixed onto existing systems. Boundaries for AI must be established first, before these tools access sensitive customer information or source code.
While agents should have access to workplace data and files to produce accurate outputs, privacy of information should be permissioned and tiered by sensitivity. Routine operational information can be treated differently from customer records, financial data, or regulated information, and when the decision involves sensitive data, human judgment should remain part of the process. The same mindset can be applied to agent autonomy: leaders have to set clear rules for what agents can do on their own versus with humans in the loop.
When a majority of employees are creating unsanctioned workarounds, the way the Pennsylvania Bank employee did, the signals are practically begging for leaders to take immediate action and establish effective oversight before expanding AI’s access. From there, the responsibility spreads to the entire company, because accountability can no longer sit with one person or one department.
Expanding Trusted Partner Relationships
At first glance, building safe AI infrastructure looks expensive and complicated, but it doesn’t need to be. To take on this new governance standard, an SMB doesn’t need to build a new compliance department or set up an entirely new security system from scratch. That capability can be sourced from an existing, trusted relationship.
Small businesses have long relied on the support of managed service providers (MSPs) to help handle IT operations and software integration. As agentic AI expands, it’s time to ask more of those relationships. The same partners employed by SMBs already vetting tools, managing access controls, and monitoring suspicious activity are in the perfect position to extend that discipline to AI models. That’s the essence of what it means to transition from a MSP to a managed intelligence provider (MIP).
This isn’t a hypothetical use case. 70% of SMBs report that they’d need an outside vendor to implement and fully benefit from AI. An MIP steps into that role perfectly, becoming the organization’s architect of intelligence infrastructure, builder of agentic systems, and orchestrator of business outcomes.
For a small business wondering if a partner is ready to make that shift towards managed intelligence, a simple evaluation of existing capabilities is needed: does the vendor vet tools in the same way AI tools must be aggressively vetted? Can they produce a record of what an agent accessed, what it did with that data, and why? Can they help enforce new policies and boundaries for AI agents?
A partner who can say yes to those three questions isn’t starting from scratch, they’re just expanding the proven workflows and systems they’ve already built.
The reality is that SMBs don’t have the option to govern reactively. They’re forced to establish proactive policies to stay one step ahead. Setting these expectations and guidelines with partners now means that down the line, as AI expands and incidents inevitably happen, small businesses that did the work early won’t be stuck scrambling to define boundaries mid-crisis. They’ll already have them, and they’ll be the ones to keep moving forward while competitors are still struggling to explain what happened.
