AI is already becoming part of everyday business.
Employees are using it to draft emails, summarize meetings, review documents, analyze information, create presentations, write code, and move through repetitive work more quickly. That is not something business leaders should fear. Used well, AI can improve productivity, free up employee capacity, and help organizations make better use of the information they already have.
AI adoption is not the problem.
The problem begins when adoption moves faster than leadership’s ability to see and manage it.
Many organizations do not have a complete picture of which AI tools employees are using, what information is being entered, where that information is processed or stored, or what systems those tools can access. At the same time, AI can make old permission problems, weak data controls, and other existing cyber gaps much easier to use.
The result is a strange contradiction: AI can make information more accessible to employees while making the flow of that information less visible to the business.
Employees Are Not Waiting for a Formal AI Rollout
In many businesses, AI adoption is not beginning with a strategy document or company-wide launch. It is beginning with an employee trying to do their job more efficiently.
Someone uses a chatbot to improve an email. A manager uploads meeting notes to create a summary. A team adds an AI transcription service to an online meeting. An employee installs a browser extension or connects an AI assistant to a cloud application. Another person starts using a personal AI account because the company has not provided an approved alternative.
These employees are not usually trying to bypass security or create risk. They are trying to save time and produce better work.
That initiative should be encouraged. But good intentions do not answer important questions about confidentiality, privacy, intellectual property, data residency, retention, regulatory obligations, or how a provider may use the information it receives.
If the business has not provided approved tools and clear direction, employees will make those decisions for themselves.
AI Can Amplify Gaps That Already Exist
AI does not create every security problem. Often, it makes an existing problem easier to find, use, or spread.
Imagine that an employee has access to an old SharePoint folder containing confidential financial, HR, or client information. That access may have been granted years ago and forgotten. Before AI, the information might have remained buried among thousands of files. Once an AI assistant can search, summarize, and connect information across the environment, that same employee may be able to surface it in seconds.
The AI did not create the excessive permission. It exposed the consequences of it.
The same principle applies to poorly classified data, outdated accounts, unnecessary vendor access, weak retention practices, and applications connected with permissions that are broader than the business requires.
AI increases speed and reach. That makes the quality of the underlying security, data, and access controls far more important.
Key Takeaway
AI can make existing cyber gaps easier to find, use, or spread. Strong permissions, data controls, and visibility matter even more as AI becomes part of everyday work.
BYOAI Is Creating a New Kind of Shadow IT
Shadow AI refers to AI applications or agents being used without formal visibility or approval. Microsoft identifies data leakage, compliance violations, security vulnerabilities, and lack of auditability among the risks of unmanaged use.
I sometimes call this BYOAI: Bring Your Own AI.
It may be a public chatbot, a personal account, a browser extension, an AI meeting assistant, a writing tool, or an agent connected to email and company files. Each tool may have different terms, security controls, retention settings, hosting locations, and rules governing how submitted information is used.
The Canadian Centre for Cyber Security warns that information entered into a large language model may move outside the organization’s control and into the custody of the service provider. Canada’s privacy regulators have also made clear that organizations using generative AI remain responsible for complying with applicable privacy laws.
This does not mean every AI platform is unsafe. It means the business needs to understand the difference between an assessed enterprise platform and an unreviewed consumer tool before confidential company, client, or employee information is entered into it.
Without that visibility, leadership may not know where sensitive information has travelled, whether intellectual property has been exposed, or whether contractual, privacy, and data-residency obligations have been affected.
Do Not Ban AI. Give Employees a Safe Way to Use It.
A blanket ban may feel simple, but it is unlikely to stop AI use. It may only push that use further out of sight.
The better approach is to steer adoption.
Provide approved tools. Explain why they were selected. Give employees practical training connected to their work. Make it easy to ask whether a new tool is acceptable before using it with business information.
Every organization adopting AI should also have an AI Responsible and Acceptable Use Policy. It should explain:
- Which AI platforms are approved for business use
- What confidential, personal, client, or regulated information may not be entered
- When company information may be used and under what conditions
- When human review and fact-checking are required
- Who remains accountable for AI-assisted work
- How employees can request approval for a new tool or use case
- How suspected exposure or misuse should be reported
A policy sitting unread in a folder is not enough. Employees need examples, training, and leadership that models the same responsible behaviour expected from everyone else.
Attackers Are Using AI Too
Businesses are not the only ones gaining speed from AI.
The Canadian Centre for Cyber Security reports that cybercriminals and state-sponsored actors are using AI to increase the quality, scale, and precision of malicious activity. The UK’s National Cyber Security Centre has also identified AI-assisted reconnaissance, vulnerability research, exploit development, social engineering, basic malware generation, and analysis of stolen information.
This does not mean AI has created an entirely new category of cyberattack. In many cases, it is making familiar attacks faster, cheaper, more convincing, and easier to scale.
A phishing email can be better written and more personalized. Public information can be analyzed more quickly. Vulnerabilities can be researched faster. Stolen data can be sorted and understood with less manual effort.
At the same time, attackers are becoming better at blending into normal activity and using legitimate tools already present in the environment. That can make an intrusion more difficult to recognize until the attacker has established a foothold.
This is why AI-specific controls cannot replace the fundamentals. Businesses still need layered security, strong identity controls, least-privilege access, endpoint protection, monitoring, protected and tested backups, vendor oversight, employee awareness, and an incident response plan the team has actually practised.
The Questions Leadership Should Be Asking
Business leaders do not need to understand how every AI model works. They do need clear answers to practical questions:
- What AI tools are employees already using?
- Which platforms have been reviewed and approved?
- What company, client, or employee information is being entered into them?
- What applications, files, email, or other systems can those tools access?
- Do our current permissions allow people to see more information than they need?
- Do we understand the provider’s security, privacy, retention, data-residency, and model-training terms?
- Have employees been trained to use AI safely and verify its output?
- Do our cybersecurity controls account for both internal AI use and AI-enabled threats?
- If information is exposed, do we know how we will detect, contain, report, and respond to it?
If the answer to several of these questions is “we are not sure,” that does not mean the organization should stop using AI. It means leadership needs better visibility.
Key Takeaway
Responsible AI adoption depends on visibility. Leaders need to understand which tools are being used, what information they can access, and whether the cybersecurity foundations underneath them are strong enough.
Confidence Starts With Visibility
AI will continue changing how work gets done. Businesses that learn to use it well will have an advantage, and employees should be encouraged to explore how it can improve their work.
But responsible adoption requires more than purchasing licences or writing a policy. It requires secure technology, organized data, appropriate permissions, approved platforms, employee training, layered cybersecurity, and clear leadership accountability.
A Cyber Gap Report can provide a practical starting point by identifying areas of hidden exposure, showing where existing controls may not match today’s risks, and helping leadership prioritize what needs attention first.
AI is not the problem. Invisible adoption, unclear responsibility, and weak foundations are the problem.
Use AI. Give your people the tools and guidance to use it well. But make sure you understand the gaps underneath it before those gaps are used against you.
Get your Cyber Gap Report and uncover what most don’t see.
Understand Shadow AI in Microsoft 365 admin center – Microsoft 365 admin | Microsoft Learn
