What Does Good AI Security Look Like in 2026?

Artificial intelligence is already becoming part of everyday working life.

Teams are using AI tools to draft content, summarise information, analyse data, support customers and speed up routine tasks. In many organisations, these tools are no longer being treated as something experimental. They are becoming part of normal business processes.

That brings clear benefits, but it also creates new security questions.

What information are employees putting into these tools? Can the answers they produce be trusted? Who is responsible for checking how AI is being used across the business? And what happens if a tool exposes sensitive information or behaves in a way that was not expected?

Cyber Security Awareness Month is a good opportunity for organisations to think about these questions.

Good AI security is not about avoiding the technology. It is about understanding how it is being used and making sure the right safeguards are in place.

Know where AI is being used

A sensible starting point is understanding how AI is already being used across the organisation.

Employees may be using public AI tools, features built into existing software, customer service systems or specialist platforms introduced by individual teams.

Some of that use may be formally approved. Some may not be.

Before a business can manage the risks properly, it needs a clear view of what is happening.

That means understanding:

• Which AI tools are being used

• What employees are using them for

• What information is being entered into them

• Whether those tools can access sensitive systems or data

• Who is responsible for approving and reviewing their use

Without that visibility, it is difficult to know where the biggest risks sit.

Protect sensitive information

One of the most straightforward risks is employees sharing information that should remain confidential.

AI tools can feel much like search engines or everyday productivity software. Because of that, it can be easy to forget that the information entered into them may be processed or stored elsewhere.

That information could include customer data, internal documents, financial information, commercially sensitive material, source code or personal information.

Organisations should therefore give employees clear guidance on what they can and cannot enter into AI tools.

They should also understand how the tools they use handle information, including how data is stored, processed and retained.

A useful rule is simple: if the information is confidential or sensitive, do not put it into an AI tool unless you know it is safe and approved to do so.

Check the answers

AI tools can produce answers that sound confident and convincing.

That does not mean those answers are always correct.

They can misunderstand a question, miss important context or produce information that is inaccurate.

That becomes more important when AI is being used to support customer communications, financial decisions, security work or other important business processes.

Human oversight still matters.

Employees should understand when information needs to be checked before it is used, shared or acted upon.

AI can support decision-making, but accountability still sits with the people using it.

Think about how AI could be misused

Businesses also need to consider how someone might deliberately try to manipulate an AI system.

One example is prompt injection, where an attacker gives the system instructions designed to make it ignore its normal rules or behave in a way it should not.

Depending on how the system has been set up, this could potentially lead to sensitive information being exposed or actions being carried out that were never intended.

The risk becomes greater when AI tools are connected to company systems, documents or customer information.

Testing should therefore look at more than whether a system works normally. It should also consider what happens when someone deliberately tries to make it behave differently.

Limit access

AI tools should not have unrestricted access to company information.

If a system can search internal documents, view customer information or interact with other business systems, it should only be given the access it genuinely needs.

This is the same principle used across wider cyber security.

People should only have access to the systems and information they need to do their jobs. AI tools should be treated in much the same way.

Limiting access can reduce the impact if a system is misused or compromised.

Make AI part of security awareness

AI security is not just an issue for IT or cyber security teams.

Employees need to understand how to use these tools safely.

Security awareness training should therefore cover AI as part of normal cyber security guidance.

Staff should feel comfortable asking questions such as:

• Am I allowed to use this tool?

• Is the information I am entering sensitive?

• Do I need to check the answer before using it?

• Could this affect a customer or an important business decision?

• Do I understand what happens to the information I enter?

The aim should be to help people use AI sensibly, rather than simply telling them not to use it.

Make responsibilities clear

As AI use grows, organisations also need to be clear about who is responsible for it.

IT, cyber security, legal, compliance and data protection teams may all have a role to play, depending on the organisation.

What matters is that someone has clear ownership.

Businesses should know who approves new tools, who reviews the risks, who decides when systems need to be reassessed and who takes responsibility if something goes wrong.

Without clear ownership, important issues can easily be missed.

Review AI use regularly

AI security should not be treated as a one-off exercise.

Tools change. New features are introduced. Employees start using them in different ways. Systems become connected to more information.

Something that was considered low risk when it was first introduced may look very different several months later.

Organisations should therefore review AI use regularly.

That could include checking access permissions, reviewing how tools are being used, reassessing risks and testing whether existing controls are still working properly.

Keep the basics strong

AI may introduce new risks, but it does not replace the need for good cyber security basics.

Strong passwords, multi-factor authentication, secure access controls, regular updates, staff awareness and security testing remain just as important.

In many cases, these controls provide the foundation for using AI safely.

Good AI security is therefore not about creating an entirely separate security programme. It is about making sure AI is included within the organisation's wider approach to cyber security.

Conclusion

For organisations using or developing AI-enabled services, independent testing can help identify weaknesses that may not be obvious during day-to-day use.

WorkNest Secure’s LLM Security Assessment is designed to test how AI systems respond to real-world attack techniques, including attempts to bypass safeguards, expose sensitive information or manipulate outputs.

It can give organisations a clearer view of where risks may exist and where controls could be strengthened as their use of AI develops.

Learn more about our LLM Security Assessment.