What is AI security? Threats, frameworks & best practices for UK organisations

With 43% of UK organisations suffering a cyber breach in the past 12 months, the threat landscape is becoming increasingly complex. At the same time, AI is accelerating both the scale and sophistication of cyberattacks, creating new risks for organisations that rely on AI-powered systems and data.

Securing AI systems is not just an IT concern, but a broad level priority. This guide explores what AI security is, why it matters, the key threats organisations face, and the best practices to protect systems, data, and people while meeting evolving regulatory requirements.

What is AI security?

AI security refers to the practices, processes, and technologies designed to protect AI systems, models, and the data they process from unauthorised access, misuse, manipulation, and attack. It encompasses both securing AI tools against cyberthreats and using AI to strengthen an organisation’s broader security posture.

There are three distinct dimensions to AI security that organisations should understand:

• Securing AI systems: Protecting AI models, training data, and pipelines from external attack and internal misuse.
• Using AI to secure other systems: Deploying AI security tools for threat detection, anomaly monitoring, and automated incident response.
• AI as an attack vector: Understanding how threat actors exploit AI tools to launch more sophisticated, faster, and harder-to-detect attacks.

As organisations increasingly depend on AI to automate workflows, process sensitive data, and drive decisions, the security of those systems becomes inseparable from the security of the business itself.

To learn more about AI and machine learning trends, see our dedicated guide.

Why does AI security matter for UK organisations right now?

The cyberthreat landscape facing UK organisations in 2026 has changed fundamentally. AI is no longer just a productivity tool; it also enables cybercriminals to automate attacks, generate convincing phishing campaigns at scale, and identify vulnerabilities faster than any human can respond.

As organisations rush to adopt AI, many are taking on new risks without the governance, skills, or security controls needed to manage them effectively.

These key statistics highlight the urgency of the issue:   69% of large UK organisation experienced a cyber breach or attack. 31% of UK businesses are rushing to adopt AI, yet 76% admit to having no AI security practices in place. Cyber resilience ranks as the 5th business priority for UK organisations in 2026, with 70% planning to increase cybersecurity spending. AI is both the first priority and a top emerging security risk, yet training and development to manage AI safely ranks 10th. Approximately 7 million people in the UK are now using GenAI in the workplace 56% of UK organisations believe GenAI increases cybersecurity risks; 34% feel unprepared

With these being said, AI security is no longer a future consideration. It is a business-critical capability that underpins resilience, regulatory compliance, customer trust, and the safe adoption of AI at scale.

How AI security differs from traditional cybersecurity

Traditional cybersecurity focuses on protecting fixed infrastructure, such as networks, endpoints, and applications against known attack patterns. AI security, on the other side, operates in a fundamentally different and more complex environment, where organisations must protect not only systems and data, but also AI models, training datasets, and decision-making processes.

To understand AI risks more fully, consider how the two disciplines compare:

Dimension	Traditional cybersecurity	AI security
Attack surface	Networks, endpoints, applications	Models, training data, APIs, inference pipelines
Threat vectors	Malware, phishing, DDoS, insider threats	Data poisoning, prompt injection, model inversion, adversarial inputs
Detection methods	Signature-based, rule-based monitoring	Behavioural analysis, output auditing, adversarial testing
Data risk	Data theft or destruction	Data manipulation corrupting model outputs silently
Governance needs	GDPR, ISO 27001, Cyber Essentials	Above plus NIST AI RMF, ISO/IEC 42001, ICO AI guidance
Speed of change	Stable threat taxonomy	Rapidly evolving attack techniques tied to AI model development

Top AI security threats in 2026

Understanding the specific threat vectors that AI systems face is the foundation of any effective security strategy. These are the threats UK security leaders must address.

1. Prompt injection attacks

Prompt injection exploits the flexibility of large language models (LLMs) by embedding malicious instructions within legitimate-looking inputs. An attacker might seed a public forum with a prompt that instructs an LLM-powered tool to redirect users to a phishing site, leak confidential data, or execute unauthorised actions.

For a deeper understanding of how these systems work, see our guide on understanding LLMs.

2. Data poisoning

Data poisoning involves deliberately corrupting the training datasets used to build AI and machine learning models. The goal is to manipulate the model’s behaviour, reduce accuracy, or introduce systematic bias: all without detection. For example, a poisoned facial recognition model may misidentify individuals from certain demographic groups, leading to discriminatory outcomes with serious legal and reputational consequences.

3. Hallucination abuse

AI hallucinations, fabricated or incorrect outputs generated by AI models, can be deliberately exploited. Threat actors can design queries that trigger hallucinations, causing AI systems to produce false information, fabricated legal citations, or misleading outputs. In regulated sectors, such as legal, healthcare, and public services, hallucination abuse presents a particularly acute responsible AI challenge.

4. Vulnerable development pipelines

AI model development involves multiple stages, such as data collection, preprocessing, training, evaluation, and deployment. Each of which introduces potential vulnerabilities. Common pipeline weaknesses include unvalidated training datasets, weak API security, and insecure Mops practices. The rise of Shadow AI, where employees using unvetted AI tools without IT oversight, creates additional pipeline exposure that UK organisations are only beginning to address.

5. Model theft and reverse engineering

Model theft involves the unauthorised extraction of a trained model’s parameters or architecture. Stolen models can be replicated, used to bypass intellectual property protections, or leveraged to extract sensitive data from the original training set. For organisations in competitive industries, model theft represents both a financial and strategic risk.

6. Bias and discrimination as a security risk

AI bias is not just an ethical issue; it’s an AI governance and security risk. When models trained on imbalanced or manipulated datasets produce discriminatory outputs, organisations face regulatory scrutiny, legal challenge, and reputational damage. Under UK GDPR and the Equality Act 2010, biased AI outputs can directly constitute compliance failures.

A taxonomy of AI security threats

Breaking down AI security threats by the layer of the technology stack they target helps security teams prioritise defence effectively. These threats can be broadly grouped into three main areas:

1. Data-level threats

Data forms the foundation of AI systems, but it also presents significant security vulnerabilities. Common examples of data-level threats include:

• Data poisoning attacks: Malicious actors may inject false or misleading data into training datasets, twisting model performance or causing flawed outputs.
• Data breaches: Sensitive datasets used for training AI models may be targeted and stolen, leading to privacy violations and regulatory risks.

2. Model-level threats

AI models themselves can become a target, as exploiting their design or outputs can have wide-ranging implications. Some examples are:

• Model inversion attacks: Model inversion is a machine learning security threat where the output of a model is queried to infer its parameters or architecture.
• Adversarial inputs: Attackers use manipulated input to deceive AI models, causing them to produce incorrect predictions or decisions, ultimately eroding trust and compromising outcomes.

3. Infrastructure-level threats

The systems and environments that host and operate AI technologies are equally vulnerable to attack. For instance:

• API exploitation– Poorly secured AI APIs can allow attackers to interfere with systems, exfiltrate data, or inject harmful operations directly into workflows.
• Cloud service vulnerabilities– Misconfigurations or weaknesses in cloud infrastructure used to deploy AI systems may expose the entire ecosystem to denial-of-service attacks or unauthorised access.

UK regulatory landscape for AI security

UK organisations deploying AI face a rapidly evolving set of legal and regulatory obligations. Unlike EU-based counterparts operating under the EU AI Act, UK organisations are governed by a principles-based framework drawing on data protection law, ICO guidance, and NCSC recommendations, set to tighten significantly through new legislation in 2026 and beyond.

UK GDPR Article 32 – Technical and organisational measures

Article 32 of UK GDPR requires organisations to implement ‘appropriate technical and organisational measures to ensure a level of security appropriate to the risk of processing personal data. For AI systems processing personal data, this means encryption, access controls, data minimisation, and regular security testing are not optional.

Data (Use and Access) Act 2025

The Data (Use and Access) Act 2025 is the most significant UK data protection development since Brexit. It updates automated decision-making obligations, strengthens data subject rights, and imposes new requirements on organisations using AI for automated processing. Security leaders should review their AI governance frameworks to ensure compliance before new provisions come into force.

ICO AI & Biometrics Strategy (June 2025, Updated March 2026)

The ICO’s AI and Biometrics Strategy sets out how the regulator will enforce data protection law in the context of AI. Key focuses include transparency obligations for AI decision-making, lawful basis requirements for AI training data, and expectations around testing for bias and accuracy.

NCSC Guidance on Securing AI Systems

The National Cyber Security Centre has published specific guidance on securely implementing AI tools, covering procurement, deployment, and ongoing monitoring. The NCSC warns that AI will ‘almost certainly increase the volume and impact of cyberattacks’ in the near term, particularly ransomware, placing additional urgency on AI security investment.

AI security frameworks and standards

Adopting a recognised AI security framework UK organisations can reply on is the most effective way to build a systematic, auditable approach to AI security. Here are the four most relevant frameworks:

NIST AI Risk Management Framework (AI RMF)

Developed by the US National Institute of Standards and Technology, the NIST AI RMF provides a structured approach to identifying, assessing, and mitigating AI risks across four core functions: Govern, Map, Measure, and Manage. Widely adopted by UK organisations as a foundation for AI governance and security, it is particularly strong on incorporating trustworthiness into the full AI lifecycle.

ISO/IEC 42001 – AI Management Systems

ISO/IEC 42001 is the first international standard specifically for AI management systems. It provides a structured framework for establishing, implementing, maintaining, and continually improving an AI management system, including security, governance, and responsible use requirements. For organisations that already hold ISO 27001 certification, 42001 is a natural extension.

OWASP AI Security & Privacy Guide

The OWASP AI Security and Privacy Guide provides actionable guidance for designing, creating, testing, and procuring secure AI systems. It covers both AI security and privacy dimensions, making it a useful practical resource for development and security teams implementing AI in the workplace.

Essential AI security best practices for UK organisations

Effective AI security requires action across six domains. Each represents a layer of defence that, combined, significantly reduces an organisation’s exposure to the threats described above.

1. Data security and encryption

All data used in AI training, testing, and operation must be securely stored, transmitted, and processed. Implement end-to-end encryption, secure data repositories, and strict data governance protocols for strong AI data security. Adopt privacy-preserving techniques, such as anonymisation and differential privacy to protect Personally Identifiable Information (PII).

2. Model integrity and adversarial testing

Maintain the integrity of AI models through secure development pipelines with version control and tamper-proof mechanisms. Implement periodic adversarial testing such as red-teaming exercises to identify vulnerabilities before attackers do. For LLM deployments, specifically test for prompt injection, jailbreak attempts, and data leakage scenarios.

3. Access control and identity management

Implement role-based access controls at every stage of the AI lifecycle. Enforce multi-factor authentication (MFA) for access to AI environments, training data, and model parameters. Identity and access management should be organisation-controlled, not delegated to third-party AI vendors.

4. Continuous monitoring and anomaly detection

AI systems require real-time monitoring for anomalous outputs, unexpected model behaviour, and infrastructure-level threats. Implement automated alert for unusual patterns in datasets, model performance deviations, or API access anomalies. Static, periodic audits are insufficient; continuous monitoring is the baseline expectation for 2026.

5. Employee training and AI security awareness

The human element remains the most exploited attack vector, with phishing accounting for 85% of cyber breaches experienced by UK businesses in 2025/2026. Security teams need specific training in AI-related threats, such as prompt injection, data poisoning, hallucination abuse, not just general cybersecurity awareness. All employees using AI tools need clear guidance on acceptable use, data handling, and how to identify and report suspicious AI behaviour.

6. Vendor and supply chain security

Many AI vulnerabilities enter through third-party components, including pre-trained models, APIs, data providers, and Mops tooling. Before deploying any third-party AI capability, evaluate it against your security requirements: certifications held, data residency, whether customer data trains the provider’s models, and integration with your identity and access management controls.

How to choose a secure AI provider in the UK

When evaluating AI vendors, security and compliance teams should assess providers against the following criteria. The checklist below provides a structured framework for these assessments.

• Data residency: Does your data stay within the UK? Can the provider confirm this contractually?
• Encryption: Is the environment fully encrypted at rest and in transit, by default?
• UK GDPR compliance: Is the provider registered with the ICO and able to demonstrate Article 32 compliance?
• Customer data isolation: Is your data processed in isolation? Is it used to train the provider’s models?
• Identity and access management: Does your organisation control identity and access, or is it delegated to the vendor?
• Certifications: Does the provider hold ISO 27001, Cyber Essentials Plus, or equivalent?
• Model transparency: Can the provider explain how the AI makes decisions? Is explainability supported?
• Incident response: Does the provider have a documented, tested incident response plan covering AI-specific threats?
• Sector experience: Has the provider deployed AI securely in your sector like healthcare, legal, public sector, retail?
• Ongoing monitoring: Does the provider offer 24/7 security monitoring and proactive threat notification?

How One Advanced delivers secure AI for UK organisations?

As UK organisations accelerate AI adoption, concerns about data security, sovereignty, and compliance remain major barriers to confident deployment. OneAdvanced AI is designed to address these challenges, with a platform built for the UK’s regulatory environment and the needs of regulated sectors.

At the core is One Advanced IQ, the intelligent system of work. It provides a secure, sovereign, and resilient foundation for AI-driven operations. Three core capabilities make it especially relevant to AI security:

• Connected: Unified workflows and data within a single, governed system, reducing the fragmented, ungoverned AI deployments that create security exposure.
• Trusted: A secure, sovereign, resilient system with enterprise-grade cybersecurity, 24/7 protection, and sector-aligned compliance built in.
• Intelligent: AI-driven insight and automation embedded directly into the flow of work, with guardrails, not as an ungoverned bolt-on.

Ready to secure your AI deployment? Speak to a OneAdvanced expert about how our Trusted AI platform keeps your data sovereign, encrypted, and compliant. Book a Demo    |    Visit the Agent Marketplace

Frequently Asked Questions (FAQs)

What are the main types of AI security threats?

The main categories are: data-level threats (data poisoning, training data breaches); model-level threats (prompt injection, adversarial inputs, model inversion, hallucination abuse); and infrastructure-level threats (API exploitation, cloud misconfigurations, supply chain attacks).

What are UK organisations' legal obligations for AI security under UK GDPR?

Article 32 of UK GDPR requires organisations to implement ‘appropriate technical and organisational measures’ for processing personal data securely. For AI systems, this includes encryption, access controls, data minimisation, and regular security testing.

How does the ICO’s guidance on AI affect how we secure AI systems?

The ICO’s AI and Biometrics Strategy (updated March 2026) sets out regulatory expectations for transparency, lawfulness, and fairness in AI systems that process personal data. Organisations must be able to explain AI decisions, ensure training data is lawfully sourced, and test systems for bias and accuracy.

How does OneAdvanced AI keep our data secure and within the UK?

OneAdvanced AI is a private, sovereign AI platform built for secure UK deployment. Customer data stays in the UK, remains fully encrypted, and is never used to train the underlying model. Identity and access management stays under the customer’s control. The platform is designed to support UK GDPR, ICO, and sector-specific compliance requirements.

Can OneAdvanced AI be used safely in healthcare, legal, or public sector environments?

Yes. OneAdvanced AI is purpose-built for regulated sectors with specific compliance requirements. Its private, sovereign architecture, with UK data residency, full encryption, and no model training on customer data, directly addresses the security and compliance requirements of NHS, legal, and public sector organisations.

See how OneAdvanced AI for UK organisations supports your sector.