AI has exploded in the last few years, with many businesses evaluating how they can leverage it best and gain productivity-boosting benefits.

However, AI is still a relatively new concept for the average person. Up until two years ago, it seemed like a thing of science fiction rather than business process. Many people still won’t know exactly how AI works, leading to uncertainties and resistance when implementing it into daily work.

Add to this that regulation and policing around AI is still a work in progress (plus the fact cyber criminals are using it to exacerbate their malicious activity) and it’s unsurprising many businesses fear potential negative consequences.

The truth of the matter is AI does bring security risks. However, these risks can be easily mitigated by applying the right practices, using trusted AI tools and introducing careful guidance around usage.

This guide explores the key AI security risks you may be concerned about, alongside best practice to minimise the danger while reaping the rewards.

What security risks and concerns are there around AI?

According to a recent ISMG survey, 30% of respondents are holding back on AI adoption across their organisations, due to security risks and concerns.

There were five ‘themes’ of concern that prevent business embracing AI:

• Data security (affecting 82% of concerned respondents) – protecting data and IP from being leaked outside of its intended audience
• Hallucinations (73%) – misunderstanding from the AI, leading to inaccuracies and incorrect information
• Threat actors (60%) – criminal using AI to steal data or exploit vulnerabilities in your cyber security
• Biases (57%) – using AI output that is based on inequality, discriminatory or unfair
• Legal and regulatory issues (55%) – lack of awareness about how AI should be regulated across industries

Fortunately, each of these issues can be mitigated by applying specific practices designed to protect your business data and control AI output.

How to combat AI security risks_

Data security_

Data security is understandably the leading concern for business leaders. Large language models learn from the information given to it by the public. By giving AI access across your data, there is the risk it may then share it to the wider world, especially with open tools.

Even within your business, there’s a chance information can be given to the wrong team, such as personal salary details.

To prevent data being leaked to the wrong people, you need to implement robust data governance frameworks. Establish clear policies and procedures for data collection, storage, access and usage. Within this, you should be classifying, anonymising and encrypting data where possible to avoid the risk of sensitive information being breached.

On top of this, you should implement secure data storage, with robust access controls, authentication mechanisms and encryption to minimise unauthorised access. Tools like Microsoft Entra ID can help you embed this. It’s also recommended that you implement strict data permissions and assign user permissions based on their roles and group memberships, with an emphasis on giving them access to the least amount of data possible (without impeding their ability to do their job).

Finally, when using AI, you want to comply with all relevant data privacy regulations, including GDPR. This means handling data responsibly, as you otherwise would. Ensure that this is built into any AI policies you create internally (and if you haven’t got one, you should). This will reduce the risk of costly fines, legal action and reputational damage.

Hallucinations_

Hallucinations are a common occurrence with generative AI tools, though it’s becoming less frequent as the tools develop and become smarter. The biggest risk to businesses is when users don’t notice the hallucinations and go on to share inaccurate output more widely.

The first step to reducing hallucinations is improving your model training. Ensure that the business data the AI has access to is updated, clean and accurate, as this will shape the output.

Specific types of prompts can also help prevent hallucinations. Chain of thought prompting, for example, asks the AI to explain how it arrived at the conclusion, enabling you to dissect the approach and pull out any issues. Similarly, it is worth adding a sentence at the end of any prompt stating you only want it to share information it knows is true, and to clarify any uncertainties it may have. Exploration of prompts over time will also enable you to finetune the input that gets the most accurate responses.

Finally, remember that human oversight is key. There should be human review processes for AI-generated output, especially for critical uses, to guarantee accuracy. This should be flagged to all your staff as part of their everyday usage. The key message should be, while AI can help you do the job faster, you still need to factcheck it.

Threat actors_

As AI becomes more readily available, criminals are utilising it. This could include using it to impersonate trusted individuals, more easily target high volumes of businesses or gain information that can be used to force access to your data.

Due to this, it’s never been more critical to have robust security measures in place. This includes common best practices, such as firewalls, intrusion detection systems, patching and encryption. You should also perform regular penetration testing, vulnerability scanning and security audits to identify and address potential weaknesses, especially within AI systems.

Aim to take a zero-trust approach to any security measures, ensuring any user is authenticated before they gain access.

For additional peace of mind, you may want a 24/7 security operations centre. This offers around the clock monitoring of potential threats to your business, enabling you to promptly address issues before they cause damage. With cyber attacks skyrocketing, fuelled by AI, this can make a substantial difference.

Finally, it’s worth remembering you can use AI to fight the risk too. AI tools are best placed to spot patterns and warning signs of malicious AI, making it easier to identify and respond to attacks. AI security systems can also help you overcome resource gaps in your cyber security, process a higher volume of signals and respond even faster. You can find out more about this in our guide.

Biases_

Unfortunately, bias can be prevalent in AI output, where the information given can represent an unfair view or not take an objective stance.

In some cases, bias can be grounded in the data shared with AI. As such, you should aim to use diverse and representative datasets to train AI models, minimising biases unfair or discriminatory outcomes. Before introducing AI, it’s recommended to vet your existing data and ensure any biases are removed.

Fortunately, leading companies are investing in ethical AI, reducing the risk of bias. That being said, it’s still crucial to understand the bias that may impact your usage. Think of this in two ways:

• Target-first – who is likely to be negatively impacted and how?
• Attacker-first – who might leverage this bias to advantage themselves?

By making these two considerations, you can better understand where bias appears and how. Once again, human verification is a key factor in finding this bias and preventing it from going any further. User feedback and focus groups, representing people across demographics and business areas, can also help to uncover bias.

While bias won’t always be obvious, it’s worth investing time into user training and awareness so they’re more likely to spot it. You should also consider regular audits to assess AI models for potential biases and ensure they are aligned with ethical principles.

Most crucially, AI alone shouldn’t be used to make decisions. Human input remains key to fairness and overcoming bias.

Legal and regulatory issues_

Given the risks around AI, it’s natural for there to be hesitancy about how to use it, especially in highly regulated industries. It is true that, if used irresponsibly, AI can lead to data breaches that break GDPR rules. This can then result in fines and reputational damage.

However, with the right practices, this risk can be minimised. As part of this, we recommend establishing AI governance frameworks. These are internal policies and procedures for AI development, deployment, and usage, addressing legal and ethical considerations.

Endeavour also to use reputable AI tools from trusted providers, as these should abide by the latest regulations around AI to help your compliance. This includes transparency and accountability about how the AI has been designed.

Alongside your own efforts to use AI safely and ethically, ensure your suppliers are following suit. This will help to build supply chains leveraging responsible AI, minimising any knock-on effects if one is affected by data breaches, biases or other risks.

Do the AI tools your business uses matter?

Not all AI tools are created equal. There are a few factors you need to consider:

1. Security and privacy: Different AI tools have varying levels of security and privacy built in. Some tools might have robust encryption and access controls, while others might be more vulnerable to data breaches or unauthorised access. You should carefully evaluate the security features of AI tools to ensure they align with your data protection policies and relevant regulations. The best tools will have the most robust security measures.
2. Fairness: AI tools are trained on data, and if that data contains biases, the AI model will likely perpetuate those biases. Some AI tools might have built-in mechanisms for bias detection and mitigation, while others might not. Ideally, you want an AI tool with minimal bias.
3. Explainability and transparency: Some AI tools are more transparent than others, meaning they provide insights into how they arrive at their outputs. This explainability is crucial for understanding potential flaws, biases or limitations in AI models. Organisations should prioritise AI tools that offer transparency, especially in high-stakes applications where understanding the reasoning behind AI decisions is essential.
4. Compliance and legal considerations: Different AI tools might have varying levels of compliance with relevant laws and regulations, such as GDPR. You must ensure that your chosen AI tools they use comply with all applicable legal requirements and ethical guidelines.
5. Integration and scalability: AI tools vary in their ability to integrate with existing systems and scale. Organisations should choose AI tools that seamlessly integrate with their infrastructure and can handle the volume of data and tasks required for their specific use cases.

Due to these differences, it’s crucial to spend time researching any AI tools you intend to use as an organisation. Most importantly, you need to encourage users not to use tools outside of these, as this could weaken your efforts to enhance security.

Why Copilot is the best AI choice for your business_

When it comes to secure, ethical AI you can trust, Copilot ticks every box. Microsoft place a significant on responsible AI, ensuring that their AI offerings are designed to be fair, transparent, inclusive, reliable, accountable and secure. This reduces the risk of bias, data breaches, regulatory issues, hallucinations and threat actors.

Integration with Azure and Fabric significantly strengthens its security posture, mitigating risks like data breaches. Azure provides the foundational security layer with data encryption, robust access controls, threat detection and compliance with industry standards. This ensures that Copilot operates within a secure cloud environment, protecting data both in transit and at rest, and preventing it being spread beyond your organisation. Furthermore, Azure’s sophisticated security measures help prevent unauthorised access and malicious attacks, minimising the potential for breaches at the infrastructure level.

Fabric enhances this security by adding a layer of data governance and control. Its centralised platform allows for consistent data security policies, while data lineage tracking and auditing provide valuable insights for investigating potential breaches. Role-based access control and data masking further protect sensitive information, ensuring Copilot only accesses authorised data and can process it without exposing confidential details.

The combined effect of Azure and Fabric creates a secure ecosystem for Copilot, offering end-to-end protection, robust data loss prevention, and improved compliance with data privacy regulations.

Moreover, Copilot is embedded in the Microsoft tools your teams already use. This streamlines workflows and improves accessibility, making it convenient for people to use and preventing them moving towards less secure tools.

Plus, with Copilots designed for specific purposes and increasing innovation (including the newly launched Copilot Agents), you can get AI that is tailored to your business goals and personalised to user needs. The possibilities are endless, while the risks are constantly mitigated.

This means you can focus on getting the benefits without worrying about negative consequences.

Find out more about secure, reliable AI through Copilot_

The choice to embed AI in your business is often coupled with concerns. Often, it can feel like opening Pandora’s box – but with the right tools, it doesn’t need to.

We’ve got a range of resources ready to help you understand Copilot better and how it can positively impact your business:

• Discover 45 Copilot use cases to test in your business
• Hear AI explained in just six minutes
• Listen to experts from Infinity Group and Microsoft discuss AI risks and how to address them