Artificial intelligence is no longer a futuristic concept; it's a powerful tool being rapidly integrated into businesses of all sizes. From automating customer service with chatbots to optimise supply chains and analyse vast datasets, AI promises to boost efficiency, innovation, and profitability. But in the rush to adopt these transformative technologies, many organisations are overlooking a critical element: security.
The prevailing mindset often treats AI as just another software application, to be plugged in and used without a second thought for the unique vulnerabilities it introduces. This "move fast and break things" approach, when applied to a technology with such a deep and complex dependency on data, is creating a perfect storm for cybersecurity risks.
AI systems are not just vulnerable to traditional cyber threats like malware or phishing. They present a whole new attack surface with specific, and often more subtle, weaknesses.
• Data Poisoning: An AI model is only as good as the data it’s trained on. Malicious actors can introduce corrupted or biased data into the training pipeline, causing the AI to learn faulty or harmful behaviours. For a financial institution's fraud detection AI, this could mean an attacker "poisoning" the data to make their fraudulent transactions appear legitimate, effectively creating a backdoor for crime.
• Model Stealing: The intellectual property embedded in an AI model is often a company's most valuable asset. Attackers can repeatedly query a model to reverse-engineer it, creating a replica of the original. This "model stealing" not only compromises a company's competitive advantage but can also be used to launch further attacks.
• Adversarial Attacks: These are deliberate, subtle manipulations of input data designed to trick an AI system into making an incorrect decision. For example, an attacker could add a few carefully placed, imperceptible pixels to an image that would cause an object recognition AI to misidentify a stop sign as a "speed limit 40" sign. In a business context, this could mean an AI-powered system misclassifying critical data or allowing a fraudulent transaction to pass through.
• Prompt Injection: With the rise of generative AI, a new type of threat has emerged. Prompt injection involves crafting specific inputs that bypass the AI's safety guardrails, causing it to reveal sensitive information, generate harmful content, or perform unintended actions. A simple, well-crafted prompt could trick an internal chatbot into disclosing confidential company information or private customer data.
The security risks of AI are compounded by a phenomenon known as "Shadow AI." This is the use of unapproved, consumer-grade AI tools by employees who, in their desire for efficiency, bypass corporate IT protocols. An employee might use a public chatbot to summarise a sensitive internal document or a third-party image generator to create marketing material using proprietary logos. This not only exposes the business to data privacy violations but also creates unknown entry points for attackers.
Failing to consider security from the start can lead to a cascade of negative consequences.
• Data Breaches and Privacy Violations: AI systems are data-hungry. Without proper controls and encryption, they become a prime target for data theft. This can result in significant financial penalties under regulations like GDPR and lead to catastrophic reputational damage.
• Operational Disruption: A compromised AI system can lead to operational failures, from inaccurate decision-making to complete system shutdowns. The downtime and recovery costs can be substantial.
• Liability and Ethical Concerns: Businesses can be held liable for the actions of their AI systems, especially if they are found to be biased or discriminatory. The lack of transparency in how many AI models arrive at their decisions (the "black box" problem) makes it difficult to establish accountability and manage legal risk.
The good news is that these risks are manageable, but only if security is considered an integral part of the AI journey, not an afterthought. Businesses must adopt a proactive, security-by-design approach that includes:
• Clear AI Governance: Establish clear policies on which AI tools are approved for use, how data can be handled, and who is responsible for AI-related risks.
• Secure Development Lifecycle: Integrate security from the initial stages of AI development, including robust data integrity checks, model testing, and vulnerability assessments.
• Employee Training: Educate employees on the dangers of "Shadow AI" and the responsible use of all AI tools.
• Continuous Monitoring: Implement continuous monitoring to detect unusual AI behaviour, data anomalies, or signs of adversarial attacks.
The rise of AI presents an unprecedented opportunity for business. However, without a foundational commitment to security, that opportunity can quickly turn into a liability. The time to think about AI security is not after an attack has occurred, but before the first line of code is written or the first employee uses an unapproved tool. The future of a secure, successful business depends on it.
Whether you are just embarking on your business AI journey or have an AI solution in place but have concerns about how secure it is, feel free to reach out to ETT and one of our specialists would be delighted to explore how we can implement a robust and secure environment for you.
Richard Manser
Automation Lead
August 2025
