AI security refers to the practices designed to protect AI tools, data, and applications from unauthorised access, misuse, disclosure, and alteration. It involves safeguarding against cyber threats, data breaches, and other malicious activities that could compromise the integrity, confidentiality, and availability of AI services.
As AI becomes deeply integrated into software, critical infrastructure, and our daily workflows, safeguarding these systems is priority for organisations. A single data lapse can lead to far-reaching consequences, including financial losses, regulatory penalties, and irreparable damage to reputation. For instance, Clearview AI, a facial recognition company, fined more than £7.5m by the Information Commissioner's Office (ICO) for unlawfully storing facial images. This incident not only resulted in financial consequences for the company but also raised concerns about privacy and misuse of personal data.
To mitigate these risks and ensure trust in AI and related technologies, organisations must prioritise their security. In this article, we’ll explore the fundamentals of AI security, it’s importance, and how organisations can safeguard their AI infrastructure in today’s high-stakes digital landscape.
Top AI security threats
As AI continues to advance and becomes integrated into our business processes, it also brings about potential risks and threats. Unlike conventional security challenges, such as network security breaches, or data theft, AI security threats are often more complex, dynamic, and difficult to detect. These include prompt injection attack, data poisoning, hallucination abuse, and vulnerable development pipelines.
Prompt injection attacks
Prompt injection attacks exploit the inherent flexibility of LLMs by inserting malicious inputs masked as legitimate prompts. By doing so, attackers can manipulate generative AI systems (GenAI) to leak sensitive information, spread misinformation, or execute other harmful actions. For instance, an attacker could seed a forum with a malicious prompt instructing LLMs to redirect users to a phishing website. If someone uses an LLM to summarise the forum discussion, the generated summary could unknowingly guide the user to the attacker’s site, leading to potentially serious consequences.
Data poisoning
In data poisoning, attackers deliberately corrupt the training data used to build AI and machine learning (ML) models. The objective is to manipulate the model’s behaviour, reduce its accuracy, and generate flawed outputs – all without detection. For example, facial recognition systems trained on biased or poisoned data may misidentify individuals from certain groups, resulting in discriminatory outcomes. Such attacks undermine both the fairness and accuracy of ML models, impacting a wide range of applications, from hiring practices to law enforcement surveillance.
Hallucination abuse
AI hallucinations are incorrect, misleading, or entirely fabricated results produced by AI models. These errors can arise due to insufficient or biased training data, flawed model assumptions, or issues in how the AI processes and interprets information. Simply put, when a user asks a generative AI tool for an answer, they expect a response based on accurate training data. However, AI may sometimes produce outputs that aren't based on data, are wrongly decoded, or lack any clear pattern “hallucinating” the response.
Vulnerable development pipelines
Development pipelines for AI models consist of multiple stages, including data collection, pre-processing, model training and evaluation, and deployment. Each stage requires collaboration between various teams and processes to create a functional and reliable AI system. However, vulnerabilities can emerge at every step of this pipeline often due to the following issues:
- Unvalidated datasets: Using unvetted datasets can introduce bias or malicious behaviour into AI models, compromising reliability and increasing vulnerability.
- Weak APIs: Weak API security can allow unauthorised access, enabling tampering, data breaches, or operational disruptions.
- Insecure MLOps practices: Without strong MLOps tools and methods, organisations risk governance issues, compliance failures, and system vulnerabilities.
How AI threats differ from traditional security threats
AI threats differ from traditional security risks due to the unique nature of how AI systems operate. Unlike rigid software that follows fixed rules and predefined inputs, AI tools rely on dynamic data and constantly adapt their behaviour based on incoming information. This adaptability, though powerful, creates unique vulnerability that traditional security measures often not equipped to address. So, what makes AI risks so distinct? Let’s break it down.
One prominent factor is the use of synthetic data. This data is often generated to enhance training datasets or fill in gaps where real-world data may be limited. While useful, synthetic data can introduce vulnerabilities, especially if it is manipulated or poorly constructed, leading to inaccurate outcomes or exploitation by malicious actors. Additionally, the internal logic of AI models –the "thinking process" that drives their decision-making – is intricate and can be exploited if not well-protected. Attackers can use techniques like adversarial inputs to confuse or trick the system into making incorrect predictions.
What really sets AI threats apart is the ability to target multiple layers of the system simultaneously, from data integrity and model logic to deployment pipelines. This expansive and interconnected attack surface makes it critical for your organisations to adopt rigorous security protocols tailored specifically for AI systems. Furthermore, a proactive approach is crucial, as the dynamic nature of AI means that vulnerabilities can evolve just as quickly as the technologies themselves. Strong governance, continuous monitoring, and agile defences are key to staying ahead of these increasingly sophisticated threats.
A taxonomy of AI security threats
Breaking down AI security threats into distinct categories can help your organisation identify and address risks effectively across different layers of technology stack. 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 notable examples include:
- 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 – Here, 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.
Top AI security risks to avoid in 2025
The adoption of generative AI (GenAI) is surging among individuals and organisations across the UK. According to Deloitte’s 2024 Digital Consumer Trends report, approximately 7 million people in the UK are now leveraging GenAI in the workplace. While this technology promises to enhance productivity and improve decision-making, it also poses significant risks to data privacy and security. Here are some key AI security risks that you should watch out for in 2025.
Data breaches and leaks
AI systems, particularly LLMs, pose a substantial risk when it comes to data breaches and leaks as they can unintentionally memorise sensitive information during training and inference phases. During AI model training, companies often use vast datasets, some of which may inadvertently contain confidential or Personally Identifiable Information (PII). This data can then be encoded into model’s parameters and, under specific circumstances, extracted by malicious actors through crafted queries.
For instance, a 2022 study published in "Proceedings on Privacy Enhancing Technologies" showcased how attackers retrieved private user information embedded in popular AI models like GPT-3. Addressing these risks requires businesses to adopt stringent data filtering, secure model deployment practices, and increased transparency around how AI models handle sensitive information.
Model theft and reverse engineering
Model theft poses a significant security threat in machine learning, involving the unauthorised extraction of a trained model's parameters or architecture. This is typically achieved by querying the model and analysing its outputs to infer its underlying parameters. The stolen model can then be replicated or used to extract sensitive data from the original training set.
Similarly, reverse engineering involves dissecting a model’s outputs and behaviours to reconstruct its functionality without proper authorisation. This process often aims to replicate the original model, bypassing intellectual property rights. Whatever is the case, both the processes can jeopardize proprietary technologies, leading to lost market advantages and substantial financial repercussions for organisations.
Bias and discrimination
Bias within AI models isn’t just a technical issue; it is a reputational, regulatory, and ethical hazard. When models trained on incomplete or imbalanced datasets make decisions, there is a risk of reinforcing existing inequalities – whether through discriminatory hiring practices, uneven credit approvals, or biased product recommendations.
To prevent this, organisations should prioritise the use of diverse and representative training data, implement regular monitoring and audits, and maintain clear, transparent documentation. This approach fosters fairness and inclusivity, addresses potential errors and mitigates bias, making a responsible AI solution.
Lack of transparency and explainability
Understanding how AI models make decisions is crucial, especially in high-stakes sectors such as finance, healthcare, or legal systems. When AI systems operate as “black boxes”, they limit the ability of businesses to explain their outcomes to customers, auditors, or regulators. This lack of transparency can erode trust, hinder compliance efforts, and amplify risks during critical decision-making processes.
Delivering explainability requires organisations to provide detailed model documentation, such as the rationale behind algorithmic choices, data sources, and the scope of operational limitations. By prioritising transparency, organisations can use AI responsibly while cultivating trust amongst stakeholders and ensuring smoother adoption of cutting-edge technologies in businesses.
AI security frameworks and standards
NIST AI Risk Management Framework
The National Institute of Standards and Technology (NIST) has developed an AI Risk Management Framework (AI RMF) to guide organisations in identifying, assessing, and mitigating risks associated with AI systems. This framework emphasises to incorporate trust into the design, development, use, and evaluation of AI products, services, and systems.
SAIF (SAIF AI Framework)
SAIF, Google's Secure AI Framework, provides a comprehensive guide for organisations aiming to secure their AI systems. This framework offers a structured approach to develop and deploy AI technologies responsibly, ensuring they align with ethical principles and best practices.
OWASP guidelines for AI
OWASP guidelines for AI is a working document to provide clear and actionable insights on designing, creating, testing, and procuring secure and privacy-preserving AI systems. It consists of two parts: how to address AI security and how add AI privacy. By following OWASP guidelines, organisations can structure robust risk governance policies while reinforcing the resilience of their AI systems in dynamic threat landscapes.
Essential AI security best practices
With a clear understanding of AI security, the various types of threats, and the standards designed to address these risks, it's now time to learn some best practices that can ensure robust AI security.
Data security
Protecting sensitive data within AI systems helps you maintain trust and compliance. You must ensure all data used for AI training, testing, and operations is securely stored, transmitted, and processed. Encryption technologies, secure data repositories, and strict data management protocols should be implemented to prevent breaches. Additionally, your business should adopt privacy-preserving techniques, such as anonymisation and differential privacy, to safeguard personally identifiable information (PII).
Model integrity
Maintaining the integrity of AI models ensures their reliability and prevent malicious interference. Organisations should utilise secure model development pipelines that incorporate version control and tamper-proof mechanisms. Periodic testing and validation of AI models are crucial to detect vulnerabilities or degradation in performance. Furthermore, implementing adversarial training strategies can strengthen models against external attacks, ensuring they perform consistently under various conditions.
Access control
Access control is a key mechanism that determines who can access what data, resources, or functions within a system. It protects sensitive data and model parameters from unauthorised access or tampering. You can implement access control measures in different stages of the AI development lifecycle, such as data collection, model training and deployment. This helps to prevent internal or external actors from exploiting vulnerabilities in the system.
Furthermore, you can also implement multi-factor authentication (MFA), which requires users to provide multiple forms of identification, such as a password and biometric verification, before granting access to sensitive data or AI models. This added layer of security can significantly reduce the chances of unauthorised access.
Monitoring
Continuous monitoring of AI systems ensures they operate securely and align with organisational and regulatory requirements. Organisations should implement real-time monitoring tools for early detection of anomalies, performance deviations, or potential security threats. Additionally, they should establish alert mechanisms for unusual patterns within datasets, models, or operational environments for improving security frameworks over time.
Training
Just as machine learning models are trained on diverse, high-quality datasets to reduce biases and improve robustness, employees must also undergo comprehensive training. Your organisation should ensure that employees receive thorough training in securing AI systems, understanding compliance requirements, and identifying potential risks. Establishing a culture of continuous learning equips teams to manage evolving threats, adopt best practices, and uphold ethical standards in AI deployment. However, training is not a one-time process but an ongoing effort in developing resilient AI operations.
Safe, secure, and trusted AI security tool for UK organisations
As UK organisations embrace AI to boost productivity, data security is their biggest concern -they are worried about putting company and personal data into public AI models, and are concerned about data sovereignty, privacy, and security.
OneAdvanced AI is built with this in mind. Designed specifically for UK organisations, it offers a private, secure AI solution. Here’s how our innovative platform helps companies to transform their workflows while prioritising security:
- Protected data using AI: Our private AI model ensures that your data remains in the UK, under your control and in compliance with data protection legislation (e.g. UK GDPR).
- Fully encrypted: Being secure by design, the OneAdvanced AI environment is fully encrypted.
- Identity management controlled by the organisation: providing security and convenience for the end user.
- Customer data is not used to train AI models: Aligning to trust, privacy and safety, prompts, data is not stored or used to train our model.
With its secure, intuitive, and reliable features, OneAdvanced AI redefines the way UK organisations use AI to streamline operations and drive success. Register today for access to OneAdvanced AI.