The role of AI in cybersecurity: How organisations are fighting back smarter

Cyber threats are becoming more frequent and sophisticated, creating growing challenges for UK organisations. According to the Government's Cyber Security Breaches Survey 2025, 43% of UK organisations experienced a cyber breach or attack in the past year, with phishing remaining the most common threat.

On top of this, artificial intelligence in cybersecurity is shaping both sides of security landscape. While cybercriminals are using AI to scale and refine attacks, organisations are leveraging AI powered cybersecurity to detect threats faster, automate responses and strengthen resilience. Therefore, as AI capabilities advance, organisations must understand both the opportunities and risks they present.

This article explores how AI in cyber security is reshaping the threat landscape, the challenges organisations face, and the best practices for staying secure in an evolving environment.

What is AI in cybersecurity?

AI in cybersecurity refers to the use of AI technologies to detect, prevent, and respond to cyber threats more quickly and accurately than traditional rule-based security tools. Unlike systems that rely on fixed signatures or static rules, AI cybersecurity tools analyse patterns in data to identify emerging threats, reduce false positives, and improve security at scale.

The main AI subsets used in cybersecurity include:

• Machine learning (ML): Machine learning in cybersecurity Identifies patterns across large volumes of network and user activity data to flag anomalies.
• Deep learning: Powers more advanced malware classification and image-based AI threat detection, such as identifying malicious attachments.
• Generative AI in cybersecurity: Used defensively for simulating attack scenarios and threat-hunting but also exploited offensively to generate convincing phishing content.
• Natural language processing (NLP): Analyses email content and sender behaviour to detect phishing and social engineering attempts.

The UK cyber threat landscape in 2025–2026

UK organisations face increasingly complex AI cyber threats. With a cyberattack occurring roughly every 44 seconds, businesses are under constant pressure from evolving threats. Phishing remains the most common attack vector and continues to account for the majority of reported breaches.

Adding to this, artificial intelligence is accelerating this challenge. In its 2025 assessment, the National Cyber Security Centre (NCSC) warned that AI is making cyberattacks faster, more effective and easier to scale. Rather than creating entirely new attack methods, AI is enhancing existing ones by speeding up vulnerability discovery and exploit development, leaving organisations with less time to identify and patch weaknesses.

The NCSC also warns of a growing “digital divide”. Organisations that adopt AI security solutions are likely to strengthen their resilience, while those that lag behind may become increasingly exposed as AI-driven attack tools become more accessible.

The business case for AI for cybersecurity is already clear. According to IBM's 2025 Cost of a Data Breach Report: UK Edition, organisations that use AI and automation extensively experience average breach costs of £3.11 million, compared with £3.78 million for those that do not. Despite these benefits, fewer than one-third of UK organisations have adopted AI at scale, and only 31% have implemented formal governance policies to manage AI use and reduce risks such as shadow AI.

Key applications of AI in cybersecurity defence

AI has moved from an experimental add-on to a core part of how modern security teams operate. Here's where it is making the biggest difference.

AI threat detection and real-time monitoring

AI continuously analyses vast amounts of network, endpoint and user activity, flagging anomalies or suspicious patterns that point to a potential attack long before a human analyst could spot them manually. This includes monitoring employee and contractor behaviour for unusual activity that might suggest an account compromise or insider threat.

Behavioural analysis and zero-trust identity

By learning normal patterns of users, devices and network behaviour, AI can quickly sense malicious activity and trigger automated alerts. This underpins zero-trust security approaches, where every access request is continuously verified.

Behavioural analysis is particularly effective in phishing detection, where AI evaluates email content and sender behaviour, and in network security, where it spots the unusual traffic patterns associated with Distributed Denial of Service (DDoS) attacks and other intrusions.

Automated incident response

Automation is one of the strongest contributions to AI-powered cybersecurity. AI-powered security tools can prioritise threats, investigate incidents, and initiate containment actions within seconds, reducing the window of opportunity for an attacker. They can also automate routine tasks such as software updates and security patching, helping organisations address vulnerabilities before they are exploited.

AI-powered malware detection

Traditional antivirus tools rely on known malware signatures, limiting their ability to identify new threats. AI enhances detection by analysing behavioural and structural characteristics, enabling it to identify previously unseen and polymorphic malware that continually changes its code to evade detection.

Vulnerability management and risk prioritisation

AI helps security teams identify, assess and prioritise vulnerabilities based on their potential impact and likelihood of exploitation. As attackers move more quickly to exploit newly disclosed vulnerabilities, this intelligence enables organisations to focus resources on the risks that matter most.

User authentication and identity threat detection

AI strengthens user authentication by monitoring biometric data, keystroke patterns and broader behavioural signals to verify identity and flag potential imposters or compromised accounts. This provides an additional layer of protection beyond passwords and helps detect compromised accounts, credential theft and unauthorised access attempts.

See how managed AI-driven security works in practice OneAdvanced's Managed Cybersecurity Services combine 24/7 UK-based SOC monitoring with MDR, delivering AI-enhanced threat detection and automated triage without in-house build. Explore Managed Cybersecurity Services

How AI is being used against you – The offensive side

AI is just as available to attackers as it is to defenders, and criminals have moved quickly to exploit it. Research says, roughly 1 in 6 breaches now involve attackers using Artificial Intelligence in cybersecurity, most commonly to power phishing campaigns and deepfake impersonation.

• AI-powered phishing: Generative AI allows attackers to craft highly convincing, personalised phishing messages in minutes rather than hours, removing the spelling and grammar errors that once made phishing easier to spot.
• Polymorphic and adaptive malware: Malware that uses AI to continuously alter its own code, evading signature-based detection.
• Deepfake fraud and social engineering: Synthetic audio and video used to impersonate executives or trusted contacts is one of the fastest-growing forms of AI-enabled fraud.
• AI-assisted vulnerability research: The NCSC's central 2027 warning: AI is shrinking the gap between a vulnerability being disclosed and being actively exploited.

Shadow AI compounds this risk. Breaches involving high levels of unsanctioned, unapproved AI tool use cost UK and global organisations hundreds of thousands of pounds more on average than standard incidents.

Read our related piece on Human vs AI: Who Is Responsible for AI Mistakes? for a closer look at where accountability sits when things go wrong.

Benefits of AI for cybersecurity for UK organisations

Used well, AI for cybersecurity delivers measurable advantages for security teams, not just theoretical ones. Here's what the evidence shows across five areas where AI is changing outcomes for UK organisations.

Faster detection and containment

Speed is everything in a breach. The longer attackers go undetected, the more data they can access, and the more expensive incident becomes, and AI is measurably closing that window. Research shows that UK organisations that made extensive use of AI cybersecurity tools and automation achieved a mean time to identify a breach of 148 days and a mean time to contain it of 42 days. Organisations not using these technologies took considerably longer: 168 days to identify and 64 days to contain – a combined 42-day improvement in breach response for AI-enabled teams.

Lower breach costs

Faster detection and containment reduce the financial impact of a cyber incident. Organisations that make extensive use of AI and automation in their security operations report average breach costs of £3.11 million, compared with £3.78 million for those with limited adoption – a difference of more than £600,000 per breach. Despite these potential savings, fewer than a third of UK organisations have implemented AI and automation extensively across their security functions.

Reduced analyst fatigue

Security teams are drowning in alerts, and most of those alerts turn out to be noise. The 2025 SANS Detection and Response Survey found that 73% of security teams name false positives as their single biggest detection challenge, while Microsoft and Omdia's State of the SOC research found that 46% of all alerts handled by a typical SOC turn out to be false positives, which means nearly half of an analyst's workload generates no security value at all.

Scalability without headcount growth

For many organisations, expanding cybersecurity teams is challenging due to skills shortages, budget constraints and increasing demand for specialist expertise. AI security solutions help bridge this gap by automating repetitive tasks, accelerating investigations and handling routine security monitoring at scale. This enables existing teams to manage a growing volume of threats more effectively, improving productivity and allowing security professionals to focus on higher-value activities that require human judgement and expertise.

Proactive rather than reactive defence

Traditional security operates largely in response mode: an alert fires, an analyst investigates, and the organisation reacts to whatever has already happened. AI in cybersecurity shifts more of this work upstream. Predictive vulnerability scanning identifies and ranks weaknesses by likely impact before they're exploited, while behavioural baselining flags the early indicators of compromise (such as unusual login patterns, anomalous data transfers, and irregular privilege use) well before they develop into a full incident.

Challenges of AI in cybersecurity

Though AI integration into cybersecurity systems brings multiple benefits, it also introduces new categories of risk that organisations need to manage deliberately.

• Data privacy and sovereignty: Safeguarding sensitive data during AI analysis is critical, given UK GDPR obligations and the risk of data exposure, particularly where AI tools process data outside UK jurisdiction.
• Shadow AI: Employees using unapproved AI tools without IT oversight creates data leakage vulnerabilities that are often invisible to security teams until a breach occurs.
• Algorithmic bias and false positives: AI systems can develop biases that lead to unfair or discriminatory outcomes or generate enough false positives to erode trust in the system.
• Accountability and governance: As AI makes more autonomous decisions, establishing clear accountability for those actions becomes more difficult, and at the same time, more important.
• AI as a new attack surface: The AI models and systems organisations deploy can themselves become targets, particularly within critical national infrastructure.
• Skills gap: UK cybersecurity workforce shortage persist, with ISC2’s 2025 research finding a meaningful share of organisations still reporting significant staffing shortfalls, making AI-fluent security talent harder to find.

It’s therefore essential to establish clear ethical guidelines for AI deployment in cybersecurity, alongside robust transparency and bias-mitigation strategies.

For a deeper look at how organisations are approaching this, wee ‘What is AI security’?  

The UK regulatory and compliance landscape

The UK regulatory environment around AI and cybersecurity has moved quickly, and organisations need to be across several frameworks at once.

• NCSC AI Cyber Threat Assessments (January 2024 and May 2025): Set out the UK's official, intelligence-informed view of how AI is reshaping the threat landscape to 2027.
• UK AI Cyber Security Code of Practice (January 2025): A voluntary code setting out 13 principles covering the secure design, development, deployment and end-of-life management of AI systems, adopted as a global standard by ETSI in May 2025.
• Cyber Security and Resilience Bill: Ongoing legislation aimed at strengthening statutory cybersecurity duties for organisations operating essential services and digital infrastructure.
• ISO 42001: The international AI governance management standard, providing a structured framework for responsible AI deployment.
• UK GDPR: Continues to apply in full where personal data is processed through AI systems, regardless of where the underlying model is hosted.

OneAdvanced became one of fewer than 100 organisations globally, alongside companies including Anthropic, AWS and Google, to achieve ISO 42001 certification in March 2026, reflecting a structured approach to AI governance.

Read more in our ISO 42001 certification announcement.

How to evaluate AI cybersecurity solutions?

With a growing number of AI security solutions UK providers in the market, choosing the right provider can be challenging. Use the following questions to assess whether a solution meets your organisation's security, compliance and operational requirements:

• Does it support UK data sovereignty, with data hosted and processed within the UK?
• Is it aligned with recognised standards and guidance, including NCSC recommendations, ISO 27001 and ISO 42001?
• Does it provide 24/7 monitoring and managed detection and response (MDR), rather than simply generating automated alerts?
• Can it integrate seamlessly with your existing security tools and wider technology environment?
• Does it offer sector-specific capabilities for regulated industries such as healthcare, legal services or the public sector?
• Does the provider guarantee that your data will not be used to train its AI models?

How OneAdvanced helps?

OneAdvanced supports UK organisations across the full spectrum of AI-enabled cyber defence. And we structure that support around the three principles that define OneAdvanced IQ, our intelligent system of work: Connected, Trusted and Intelligent. Cybersecurity isn't a separate workstream bolted onto IQ; it's woven through all three pillars.

• Connected: Unifying workflows, teams and data into a single system that carries business and sector context across every interaction, so security visibility doesn't stop at departmental or platform boundaries.
• Trusted: A secure, sovereign and resilient system backed by expert services providing 24/7 protection, enterprise-grade cyber security and sector-aligned compliance.
• Intelligent: AI-driven insight and automation embedded directly into the flow of work, delivering real-time detection and response rather than insight that arrives after the fact.

In practice, this is delivered through:

• Managed Cybersecurity Services: A 24/7 UK-based Security Operations Centre (SOC) delivering Managed Detection & Response (MDR), built on AI-enhanced detection, behavioural analysis and automated triage.
• OneAdvanced AI: A private, sovereign large language model with UK data hosting, fully encrypted and built with role- and sector-specific safeguards, launched in April 2025.
• ISO 42001 and ISO 27001-aligned governance: A risk management framework compliant with NIST and NCSC guidance, giving customers confidence in how AI is governed across our platform.
• Sector expertise: Deep experience supporting NHS, legal, public sector and logistics organisations with the specific compliance and threat profiles each sector faces.
• AI & Data Services: Helping organisations adopt AI securely across the business, not just within the security function.

Custom privacy controls also mean customer data is never used to train OneAdvanced's AI models, which is an increasingly important differentiator as more organisations weigh up the data sovereignty implications of the AI tools they adopt.

Ready to strengthen your AI-powered cyber defences? Talk to OneAdvanced's cybersecurity team about a UK-based, ISO 42001-certified approach to managed detection, response and AI governance. Book a Demo

Frequently Asked Questions (FAQs)

What is the role of AI in cybersecurity?

AI helps organisations detect, analyse and respond to cyber threats faster and more accurately than traditional rules-based tools, by learning normal patterns of behaviour and flagging anomalies that may indicate an attack, including threats that have never been seen before.

What is the difference between AI-powered and traditional cybersecurity?

Traditional, rules-based cybersecurity can only detect threats it has been explicitly programmed to recognise, such as known malware signatures. AI powered cybersecurity learns patterns of normal behaviour and can identify new, previously unseen threats based on anomalies and deviations from that baseline.

What does the UK government's AI Cyber Security Code of Practice mean for my organisation?

Published in January 2025, the voluntary code sets out 13 principles for the secure design, development, deployment and end-of-life management of AI systems. While voluntary, it has since been adopted as a global standard by ETSI and signals the direction AI cybersecurity UK regulation is heading.

What does the NCSC say about AI in cybersecurity?

The NCSC's May 2025 assessment warns that AI in cybersecurity will almost certainly make cyber intrusions more effective and efficient by 2027, primarily by enhancing existing attack techniques, and cautions that a growing 'digital divide' is emerging between organisations that adopt AI-enabled defences and those that don't.

Is AI use in cybersecurity covered by UK GDPR?

Yes. UK GDPR obligations apply in full whenever personal data is processed through an AI system, regardless of where the underlying model is hosted, making data sovereignty an important consideration when selecting AI cybersecurity tools.

What is shadow AI and why is it a cybersecurity risk?

Shadow AI refers to employees using AI tools that haven't been approved or reviewed by IT or security teams. Because these tools sit outside formal oversight, they can create data leakage vulnerabilities that are invisible to security teams until a breach occurs.

How does OneAdvanced use AI in its cybersecurity services?

OneAdvanced's Managed Cybersecurity Services combine a 24/7 UK-based SOC with Managed Detection & Response, using AI threat detection, behavioural analysis and automated triage to identify and contain threats faster.

Does OneAdvanced's AI cybersecurity solution comply with UK regulations?

Yes. OneAdvanced's approach is aligned with ISO 27001 and ISO 42001, NIST and NCSC guidance, and built around UK data sovereignty, with customer data never used to train OneAdvanced's AI models.

What cybersecurity certifications does OneAdvanced hold?

OneAdvanced holds ISO 27001-aligned risk management certification and achieved ISO 42001 AI cybersecurity governance certification in March 2026, placing it among fewer than 100 organisations globally to hold this standard, alongside companies including Anthropic, AWS and Google.