Artificial Intelligence (AI) tools have now become part of everyday work for many teams—but while their benefits are real, so are the security risks of using them. Yet too many teams that use AI lack awareness of these risks and don’t know how to address them.
They still rely on security checklists that were written before AI—checklists that don’t account for chatbots processing sensitive text, AI plugins with access to company files, or employees sharing data with third-party LLM models.
If you are one such team, here’s an updated security checklist that takes into account AI. This guide covers what to check, what to update, and what to stop ignoring when using AI.
Why AI Tools Create New Security Risks
Traditional security checklists don't work for AI because it works differently from conventionally designed software.
The Data Goes Somewhere
With traditional software, your data handling is more predictable and often does not leave your own organization's systems. AI, on the other hand, requires your data as input and sends it to external servers to be processed (where it may be stored or used in ways you cannot control).
This is a growing issue, as up to 11% of the data that employees paste into ChatGPT is confidential. This includes information like trade secrets, personally identifiable information, and internal documents.
Take, for example, a team member pasting the text of a client’s email into an AI chatbot. This data leaves your systems, and it might be stored, used for model training, or even exposed in a breach of the provider.
Shadow AI Is Already Happening
Just as shadow IT has been a longstanding problem, many employees are now engaging in shadow AI.
This refers to the use of tools that are not within the company’s software infrastructure or officially approved for use. It is therefore more difficult to secure such software, since AI-based services pose more risks; the consequences are naturally more severe.
With more and more free AI agents and software flooding the market, anyone can sign up and start pairing it with one of the company’s projects within seconds. In fact, according to IBM's 2025 Cost of a Data Breach Report,one in five organizations reported a shadow AI-related data breach.
The Checklist: What to Review Right Now
In light of this, how can teams update their checklists to stay secure when using AI tools? Here’s what to check.
1. Know Which AI Tools Your Team Is Using
Secure AI use starts with knowing what tools teams are using in the first place. Conduct a simple audit by asking each department to list every AI tool they use, including free ones.
You will likely find more than you expect.
Look for:
● Browser-based AI assistants.
● AI writing and summarization tools.
● AI features are built into existing software (email, project management, spreadsheets).
● AI coding assistants.
● AI search tools connected to internal data.
Once you have the list, have IT review each tool before categorizing it as approved, under review, or prohibited. Then publish the final list so staff know which tools they can or can't use.
2. Define What Data Can and Cannot Be Used With AI Tools
Even with a list of approved AI tools, however, it’s important to know which pieces of data can be used with which tools. After all, different tools handle data differently, and not all data carries the same risk.
For example, pasting the draft of a blog post (which will become public anyway) is very different from the aforementioned example of pasting a private email.
As such, create a clear policy that tells employees what they can and cannot feed into an AI tool. A simple way to address this is to categorize data according to risk level and safety:
● Safe to use: Publicly available information, draft copy without names, general research.
● Use with caution: Client names, financial data, internal strategy documents.
● Never use with AI: Personal data covered by GDPR, passwords, legally privileged material, and healthcare records.
Write this in plain language and make it easy to find. If staff have to interpret it, they might interpret it differently—and that is where mistakes happen.
3. Review Access Permissions on AI Integrations
Many AI apps and websites request access to your files, email, or calendar. Check which permissions are enabled for each service in use and disable those that are not necessary or have not been used for a long time.
With many AI tools being connected to cloud storage or communication platforms, such permissions allow them to access far more than the user might have intended to share.
Ask these questions when examining permissions:
● What data can this tool access?
● Does it need all of that access to function?
● Who approved the integration?
● Can access be scoped down to the minimum required?
It’s also crucial to audit these permissions regularly and not just when a tool is first approved.
4. Enable Multi-Factor Authentication (MFA) on All AI Platforms
Don’t forget the basics as well: strong passwords and MFA. Like with regular software, compromised accounts expose any data shared with that tool.
Check that MFA is enabled for:
● Every AI tool account used by your team.
● Single sign-on (SSO) for tool access.
● Admin accounts that manage AI platform settings.
This applies even to free tools. A free account with weak authentication is still an entry point for potential attackers.
5. Secure the Network Your Team Works From
Since AI tools are accessed over the internet, it’s also critical to secure every employee’s connection.
This is particularly important for remote workers who might be using these tools and accessing company data on public Wi-Fi, where data traffic can be more easily intercepted.
Your AI use policy should include:
● Mandatory use of a VPN for Windows and Mac for remote access.
● DNS filtering to block known risky AI platforms.
●Monitoring for unusual data transfer volumes.
In fact, these are baseline measures that apply to all tools, not just AI. If your team is not already doing them, start here before anything else.
Put It in Writing — and Make It Stick
All of the above should be captured in a formal yet accessible acceptable use policy—signed by employees and reviewed at least once a year.
Additionally, ensure that it is comprehensively covered during onboarding for new employees. The earlier habits are built, the better they stick.
Remember: a checklist is only as good as it is understood and enforced.
Monitoring and Incident Response
Putting controls in place is not enough. You also need visibility into what is happening and a clear plan for when things go wrong.
Set Up Alerts for Unusual AI-Related Activity
Your security monitoring should now include AI-specific signals. Look for:
● Large volumes of data are being copied to AI platforms.
● New AI integrations are appearing on company accounts.
● AI tools accessing sensitive company files.
Have a Plan for When Something Goes Wrong
Regardless of how rigorous your checklist is, the exposure risk still exists.
In such cases, being able to address the situation is crucial, including knowing:
● Who leads the response?
● What needs to be logged?
● Who needs to be informed about legal matters?
A clear incident response plan reduces the damage of any breach.
After all, people are human and will make mistakes. A file gets pasted into the wrong tool. An integration turns out to have access it shouldn't have. No system is fully immune to leaks or breaches, either.
Final Thoughts
AI tools are powerful, but they need to be used with security as a priority since the risks tend to be higher than traditional software, and higher than most teams realize.
With a proper checklist and incident response plan, however, teams can use AI tools confidently and securely.
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