Using AI Safely at Work: Practical Ways to Protect Data Without Slowing Teams Down

AI tools are embedded in many workflows. They are fast, easy to use, and increasingly expected.

Given that AI introduces risk, the question is whether organizations are prepared to manage it as part of everyday operations. The same accessibility that drives adoption also creates new exposure, particularly around how data is shared, processed, and validated.

This creates tension. Organizations need to enable AI to realize its benefits, but they must do so without compromising confidentiality, accuracy, or compliance. Traditional approaches to governance often add friction, while unrestricted use introduces risk.

This post focuses on how knowledge and information professionals can use their expertise to help organizations strike that balance. It outlines where AI use typically creates problems and the practical safeguards that allow teams to use these tools confidently, without slowing work down.

Where AI Use Creates Risk

In everyday work, most AI-related risk does not come from malicious intent, but from well-intentioned actions taken in the pursuit of efficiency. The following are some of the most common risks.

Why Traditional Controls and Barriers Fall Short

Efforts to manage AI risk often fail because they do not align with how people actually work. Many organizations respond by introducing strict controls, such as approval processes, restricted access, or blanket prohibitions.

When governance creates too much friction, employees find workarounds. They turn to external tools that are faster and easier to use, but far less controlled. This creates a paradox: the more restrictive the system, the more likely it is to be bypassed.

Effective AI safety does not come from adding barriers. The question is what it should look like in practice.

1. Clear rules on what can be shared
People need straightforward, well-thought-out guidance on what information can and cannot be used with AI tools. For example, any sensitive data – such as client information, confidential documents, or proprietary material – should not be entered into external systems.

2. “No publish without review”
AI-generated content should never be treated as final. A simple “no publish without review” rule ensures that outputs are checked before they are used or shared.

Draft outputs may be used for internal review or early-stage thinking, but require validation before being included in client-facing work, formal advice, or published materials. This reinforces professional judgment and ensures that speed does not come at the expense of accuracy.

3. Use of approved and controlled tools
Providing access to approved AI tools reduces the need for employees to seek alternatives. Where possible, these tools should operate within secure environments or use defined, trusted data sources. This approach enables adoption while maintaining consistency and control.

4. Visibility and auditability
Organizations need to understand how AI is being used. This does not require intrusive monitoring, but it does require visibility.

This may involve tracking which tools are in use, maintaining logs of access to sensitive systems, or requiring teams to document where AI has been used in key workflows. These lightweight approaches provide accountability without slowing work down.

5. Starting with low-risk use cases
Safe adoption often begins with low-risk, high-volume tasks. Activities such as summarizing internal documents or calls, drafting routine communications, or organizing notes allow teams to build confidence without exposing sensitive data.

This creates space to learn what works, refine internal practices, and gradually expand usage in a controlled way.

The Role of Knowledge Professionals in Making AI Work in Practice

All employees need to understand where AI adds value, where it introduces risk, and how to recognize the difference. In this context, a degree of skepticism is not a barrier to adoption, but a necessary condition for using AI responsibly.

Knowledge and information professionals play a central role in this “open minded skepticism”.

• They are experts in evaluating sources, managing content quality, and applying context, positioning them to guide both tool selection and day-to-day usage.
• They understand how information behaves, how it degrades, and how it can be misinterpreted when removed from context. This perspective is essential in ensuring that AI outputs are not only fast, but reliable.

Knowledge professionals are not out of step with AI adoption. They are ahead of the curve, bringing the critical thinking and discipline needed to ensure that speed does not come at the expense of trust.

Striking a Practical Balance is Key to Success

AI can deliver significant gains in efficiency, but only when it operates within clear and controlled boundaries.

The challenge is to manage risk in a way that supports how people actually work. Overly complex controls slow adoption, while unrestricted use introduces exposure. Organizations that succeed will be those that strike a practical balance between the two.

In this environment, it is not simply about speed or perceived efficiency. The ability to interpret, validate, and apply information remains critical, placing knowledge professionals at the center of adoption. Their role is not only to highlight risks, but to guide responsible use, build awareness, and ensure that AI is grounded in reliable information and sound judgment.

Future posts in this series will explore these themes in greater depth, including how to connect systems without introducing complexity, how to build the support and ownership needed for successful adoption, and how to ensure collections remain visible and usable in an AI-driven environment.