AI Cybersecurity: 8 Things Your IT Teams Need to Know In 2026

AI is changing how attackers work and how organisations manage risk. When deciding how your organisation should embrace AI, cybersecurity should be top of the consideration list.

For IT leaders, a priority is control of AI tools that impact your organisation. As AI tools can help teams work faster, it is not uncommon for users to start experimenting with new solutions. Each tool can introduce a new AI cybersecurity risk if not managed effectively.

Every organisation needs to minimise the risk from AI tools by implementing an AI policy and introducing guardrails about AI usage. The aim is not to stop teams using AI tools, but to give them the guardrails needed to work safely without creating weaknesses attackers can exploit. It means helping your organisation benefit from AI while keeping control of data, access, behaviour and security risk.

If your organisation needs support managing the cybersecurity risks created by AI, CloudGuard can help. Our managed security specialists provide ongoing monitoring, expert guidance and practical controls to help you adopt AI safely while protecting your users, systems and data. Speak to our team to discuss your AI cybersecurity requirements.

Contact Us

How AI is reshaping phishing tactics

Phishing has always relied on trust, timing and pressure. AI makes those tactics easier to scale, personalise and refine, helping attackers create convincing messages without the time and effort once needed to research each target manually.

A phishing email no longer needs poor spelling or generic wording. Attackers can use information from company websites, professional profiles, social media and previous data breaches to create messages that reflect a person’s role, responsibilities and working relationships.

A finance employee might receive what appears to be an urgent request from a senior executive. An HR team could be sent a believable message about an employee record, while a supplier email may refer to a genuine project, invoice or colleague. These familiar details make the approach harder to recognise as fraudulent.

AI can support increasingly sophisticated attacks, including:

• Personalised emails based on a person’s role and employer
• Supplier, executive or colleague impersonation
• Voice cloning used to reinforce urgent requests
• Deepfake video supporting fraudulent instructions
• High-volume campaigns that are tested and adjusted quickly
• Longer conversations where attackers adapt their tone and responses

The risk is not limited to the first message. AI can help attackers maintain a convincing conversation across email, messaging platforms and phone calls. A fraudulent payment request, for example, could be followed by a cloned voice message that appears to confirm the instruction.

Understanding what happens after a phishing attack is just as important. Once credentials are entered into a fraudulent page, an attacker may gain access to email, SharePoint, cloud storage and other business systems through a legitimate account. Because valid credentials and permissions are being used, the activity can initially look like normal work.

From there, the attacker may study previous conversations, identify suppliers and senior decision-makers, create inbox rules that conceal alerts, access sensitive documents or send further phishing emails from a trusted internal address. AI tools could also be used to search and summarise large volumes of accessible information, allowing attackers to extract commercial knowledge without simply copying individual files.

This means organisations need to consider more than traditional data theft. A compromised account could expose pricing strategies, customer relationships, internal processes, source code, regulatory information and wider commercial decision-making.

Awareness training remains important, but employees cannot be expected to identify every polished, contextually accurate message. Training should focus on unusual behaviour and requests rather than relying on spelling mistakes or poor formatting. Payment changes, authentication requests, sensitive data transfers and attempts to bypass approval processes should always be verified through a separate channel.

Layered security controls must support employees. Strong identity protection, phishing-resistant authentication, email security, conditional access, sensitivity labels and data loss prevention can all reduce the impact of a successful attempt. Least-privilege access also limits how much information a compromised account can reach.

Security teams should monitor whether activity matches a user’s normal behaviour, not just whether they have permission to access the data. Unexpected SharePoint activity, unusual document access, unfamiliar applications or automated requests can all indicate that a legitimate account is being misused.

AI is making phishing more convincing and harder to detect through traditional warning signs alone. Effective protection combines informed employees with strong identity controls, behavioural monitoring, limited access to sensitive data and a clear incident response process.

Why weaknesses can turn into incidents faster

AI risk is not limited to scam emails or fake messages. Threat actors can use AI to review code, summarise vulnerability research, adapt scripts and test potential attack paths. Exploiting a serious software flaw still requires technical knowledge, but AI can shorten the time between a weakness being identified and attackers attempting to use it.

Recent incidents show how quickly cyber threats can move from an IT issue to a wider operational and commercial problem.

In September 2025, Jaguar Land Rover experienced a cyber incident that severely disrupted its retail and production activities. The company shut down systems to contain the attack, paused production across its operations and extended the shutdown while forensic investigations and a controlled restart continued. The disruption also affected suppliers, retailers and the wider manufacturing network around the business.

Eurail, which operates Eurail and Interrail passes, also experienced a major data security incident. Personal information belonging to more than 300,000 travellers was reportedly accessed, including names, contact details, dates of birth and passport information. Eurail later confirmed that data connected to the incident had been offered for sale online, creating further risks around identity theft and targeted phishing.

These examples involve well-known organisations, but cyber threats are not limited to large businesses. SMEs often rely on the same cloud platforms, software suppliers, remote access tools and identity systems, while having smaller IT teams and fewer resources available to monitor alerts, apply patches and investigate unusual activity.

Related Article: Should You Pay A Ransom? The Hidden Cost Nobody Tells You

The UK Government’s 2025/2026 Cyber Security Breaches Survey found that 42% of micro businesses and 46% of small businesses had identified a cyber breach or attack in the previous 12 months. Among micro and small businesses that experienced an attack with a negative outcome, the typical perceived cost was £560. For the most expensive 10% of these incidents, the cost reached £10,000.

Many organisations still have unresolved weaknesses across cloud services, identity platforms, endpoints and externally accessible systems. Unpatched software, exposed services, reused passwords and poorly configured permissions remain common routes into a business. AI can help attackers identify and act on these gaps more quickly, giving IT teams less time to respond.

A clear view of your current position is therefore essential. A Security Posture Assessment can help identify the weaknesses that matter most, prioritise remediation and create a stronger security baseline before attackers find the same gaps first.

Where everyday AI use creates data risk

Shadow AI is one of the most practical AI cybersecurity risks facing busy organisations. It happens when employees use AI tools without approval, oversight or clear rules. In most cases, the intention is positive. Someone may upload a document to generate a summary, use an AI assistant to rewrite meeting notes or ask it to analyse a spreadsheet so they can complete a task more quickly.

The risk lies in the information being shared and the lack of visibility around what happens next. Customer data, financial details, legal documents, source code, security logs, regulatory drafts and commercial plans should not be entered into AI tools that have not been approved for business use.

Traditional data protection controls are often designed to detect files being downloaded, emailed or copied to external storage. AI creates a different challenge. A tool may be able to review a large volume of information, identify patterns, relationships and decision-making logic, then condense that knowledge into a new output. The original files may never move, but valuable business information can still leave the organisation in another form.

This makes AI data leakage harder to identify through conventional monitoring alone. An employee could upload documents gradually over several weeks, with each individual action appearing routine or low risk. AI agents can create a further challenge by accessing data using the permissions of a legitimate user, making automated activity look similar to normal work unless security teams are monitoring behaviour, access patterns and unusual application activity.

Even where a provider states that business data will not be used to train a public model, organisations still need to understand:

• How long prompts and uploaded files are retained
• Where the data is processed and stored
• Who can access conversations and outputs
• What audit and administrative controls are available
• Whether the tool meets internal, contractual and regulatory requirements

These considerations are covered in more detail in our Claude Business Security guide, including how admin controls, audit logging, data residency and supplier governance affect the safe use of AI with business information.

This is where data governance and cybersecurity need to work together. Teams need clear guidance on which tools are approved, what information can be entered and when human review is required. Organisations also need visibility over where sensitive data sits, who can access it and whether existing permissions remain appropriate.

A Microsoft Purview Health Check can help assess current data protection settings, review sensitivity labels and access controls, and identify practical steps to reduce the risk of accidental leakage.

For a closer look at how everyday AI use can lead to both accidental data exposure and deliberate knowledge extraction, watch our video on accidental leaks and intentional distillation.

How AI systems can be manipulated

AI prompt hacking is an emerging risk for organisations building or adopting AI systems. It occurs when a user, document, webpage or other source instructs an AI tool to override what the system was meant to do.

The risk increases when AI systems can access documents, emails, applications, workflows or customer data. For business use, the concern is not only the AI output. It is what the AI has permission to see and do. If an AI assistant has access to too much information, a prompt injection attack could expose sensitive content or trigger actions that should have required human review.

Practical controls include limiting permissions, separating trusted and untrusted content, logging AI interactions and adding approval steps for high-risk actions. AI systems should be treated like any other business technology. They need testing, monitoring and clear ownership.

Why AI cybersecurity needs practical governance

AI cybersecurity adoption works best when governance is clear, practical and easy for teams to follow. Policies, access controls and monitoring need to keep pace with how quickly AI tools are changing.

Create rules teams can use

Start with clear policies that explain which AI tools are approved, what data can be used, and which use cases need extra review. Keep the guidance short, practical and easy to apply. If the policy is too vague or too restrictive, people will find their own route.

Give every decision an owner

Security, IT, legal, compliance and business teams all have a role, but accountability needs to be clear. Your organisation should know who approves new AI tools, who reviews data risk, who monitors usage and who responds if something goes wrong.

Keep senior leaders close to the risk

AI risk should be visible to senior leaders, not buried in technical detail. Boards and leadership teams need a clear view of business impact, data exposure, compliance risk and operational readiness.

For organisations introducing Microsoft Copilot or other AI tools, CloudGuard’s Securing AI service can help review governance, permissions, data exposure and safe usage controls before risk builds up. We work with small and medium-sized businesses across the UK and Europe to assess current AI exposure, including shadow AI, then provide a prioritised action plan. This can include AI use policy development, practical staff awareness training and ongoing governance support, with monthly monitoring, vendor reassessment and policy updates as AI usage changes.

For senior support, CISO Advisory Services can help align AI adoption with cyber risk, board reporting and practical decision-making.

What good monitoring should cover

Once AI tools are in use, visibility becomes essential. Your team needs to understand which applications are being accessed, which identities are involved, what information is moving and where activity falls outside normal working patterns.

Monitoring should bring together signals from users, cloud environments, endpoints, email, identity platforms and data stores. This is particularly important when AI tools or agents are connected to business systems. A compromised account may be able to access sensitive information, trigger automated workflows or search across large volumes of data while appearing to operate through legitimate permissions.

CloudGuard’s Managed XDR Service provides 24/7 monitoring, detection and response across your environment. By connecting security data in one platform, our team can identify genuine threats, reduce unnecessary alert noise and act quickly when AI-enabled or traditional attacks are detected.

ANSEL, CloudGuard’s virtual AI security analyst, supports this process by enriching and prioritising alerts, automating routine investigation tasks and escalating incidents that need human judgement. This helps analysts focus on genuine risks rather than spending valuable time reviewing repetitive or low-value alerts.

Across CloudGuard’s security operations, ANSEL has helped save an average of 18 days per quarter on ticket resolution and reduce resolution times by up to 90%. In one customer example, Amazon Filters had 98% of its alerts automatically enriched or resolved over a 90-day period, saving the equivalent of 52 days of manual work.

Monitoring must also be supported by clear procedures for handling AI-related data exposure. If sensitive information is pasted into an unapproved tool, teams need to know how to record the event, assess the data involved, establish whether it was retained or shared, and reduce the chance of it happening again. This may involve removing access, contacting the AI provider, reviewing permissions, updating data controls and providing targeted guidance to the employee involved.

Where monitoring identifies malicious access, account compromise or deliberate data extraction, the situation should move into the organisation’s formal incident response process. The distinction matters: not every accidental AI disclosure is a full cyber incident, but every event should be assessed consistently so that a more serious breach is not overlooked.

How to build AI readiness without slowing the business

I now plays a role in everyday work across modern organisations. Strong governance helps your team use these tools with clear visibility, controlled access and reduced security risk.

For IT teams, the priority should be practical readiness. Start with the areas that reduce risk quickly and give your organisation a clearer view of AI use.

A strong AI cybersecurity plan should include:

• A list of approved AI tools and clear rules for sensitive data
• A review of current shadow AI use across the organisation
• Identity and access controls for AI-connected systems
• Data classification and protection settings
• Monitoring across users, endpoints, email and cloud services
• A process for reviewing new AI tools before adoption
• Staff guidance that is easy to follow
• Incident response plans for AI-related security events
• Regular reporting to senior leaders

Organisations need a practical way to use AI confidently while keeping risk under control. Businesses need the speed and productivity that AI can offer, but not at the cost of data protection, compliance or resilience.

If your team is reviewing how AI is being used across the business, CloudGuard can help you understand the risks, set practical guardrails and build a clearer path to safer adoption.