The hidden risks of AI: How business innovation can become a liability

AI is everywhere and moving fast. It feels like barely a year ago that AI chatbots were a novelty. Today, they are embedded in customer service portals, internal HR tools, financial advisory platforms, healthcare triage systems, and e-commerce checkout flows.

Organisations across the Netherlands and broader Europe are moving quickly to integrate AI agents and large language models into their operations, driven by competitive pressure, boardroom enthusiasm, or vendor promises of productivity gains. That pace of adoption is a remarkable achievement. But it has also created a dangerous gap: between the speed at which organisations are deploying AI, and the speed at which they are thinking critically about what that deployment exposes them to.

The false sense of security

A common assumption among business leaders and IT teams is that if you are using a well-known AI product built by a reputable company, then it must be secure. After all, these are sophisticated tools built by knowledge teams with significant resources.

The truth is that even the most capable AI systems, including those from leading vendors like Anthropic, OpenAI, and Google, carry inherent vulnerabilities that exist not because the vendor was negligent, but because of the fundamental nature of how large language models work. These models are trained to be helpful, conversational, and context-aware. And those very qualities create attack surfaces that traditional security tools simply were not designed to address.

There is also a structural problem. When AI tools are deployed across an organisation, it is often driven by business teams, operations leads, or product managers—not security professionals. IT and security teams frequently find out about new AI deployments after the fact, if at all. By then, the tool is already in production, handling real customer data, and potentially exposed to the public internet.

The threat landscape specific to AI

To understand why AI security requires dedicated attention, it helps to understand what makes these threats different from conventional cybersecurity risks.

Prompt injection is the new phishing

Prompt injection is perhaps the most widely discussed AI-specific threat, and for good reason. In a prompt injection attack, a malicious actor crafts inputs designed to override or manipulate an AI model’s instructions, effectively hijacking its behaviour. Just as phishing exploits human psychology to trick people into handing over credentials, prompt injection exploits the AI’s natural language processing to trick it into ignoring its own rules.

The results can range from embarrassing to catastrophic: an AI customer service agent revealing confidential pricing logic, an internal HR bot disclosing employee data, or a financial assistant generating advice that violates regulatory requirements.

Social engineering at scale

AI agents can also be targeted through social engineering, techniques that manipulate the agent’s reasoning through persuasive language, false context, or emotional framing. Where a human employee might eventually become suspicious of a persistent caller trying to extract information, an AI agent will continue engaging indefinitely, following its programmed helpfulness without the intuitive alarm bells a person might feel.

Attackers have demonstrated the ability to convince AI agents to issue refunds they should not issue, reveal backend workflows, expose system prompts, and even take actions on connected systems that go well beyond their intended scope.

The difference from traditional cybersecurity threats

Traditional cybersecurity focuses on protecting infrastructure: patching software vulnerabilities, securing network perimeters, monitoring for unusual traffic patterns. And these tools are important, even in AI defence. However, new AI threats that can easily bypass these often operate at a completely different layer—the semantic layer. The attack is not a piece of malware or a network intrusion that can be identified by traditional security tools; it is a sentence. The new battlefield is a carefully constructed question or sequence of words designed to make the model behave in ways its designers did not intend.

This is why many traditional security tools such as antivirus software and intrusion detection systems provide little to no protection against AI-specific attacks. You need solutions that understand AI behaviour, not just network packets.

Who is at risk?

The short answer: almost everyone deploying AI in a business context may carry a significant margin of risk. However, certain profiles carry elevated risk:

• Businesses of all sizes using AI chatbots on public-facing websites, where any visitor can interact with the system and probe for weaknesses
• Companies using AI for internal workflows that involve sensitive business data such as HR systems, finance tools, and customer relationship management (CRM) platforms
• Highly regulated industries including financial services, healthcare, travel, and retail, where a data breach or compliance failure carries significant legal and financial consequences
• Any organisation that has deployed AI agents with access to backend systems, APIs, or sensitive databases where a compromised agent could trigger actions far beyond a single conversation

The real-world consequences are serious if such a breach happens. There are regulatory fines under GDPR and the EU AI Act. And while there is the idea that there’s no such thing as bad press, few businesses want the reputational damage if a misbehaving AI agent goes viral. AI vulnerabilities can also lead to data breaches that trigger notification obligations and erode customer trust. If AI receives inputs of confidential information, your organisation becomes responsible for its security and potentially liable if it is stored, reshared, or further used. And lastly, there is the operational cost of pulling a flawed AI deployment back offline and retraining it.

Why awareness is the first step

Almost all cybersecurity frameworks start with a foundational step of identifying where risks reside. The inherent assumption that may be flawed with AI is that organisations know where to look.

Many organisations have no visibility into how their AI systems behave in practice. They know what the system is supposed to do, and most have tested it in controlled conditions. However, they have never subjected their AI implementation to adversarial conditions or explored what happens when someone is actively trying to make their AI agent misbehave.

Building that awareness requires more than a conversation with the AI vendor. Organisations need internal alignment between IT, security, legal, and business stakeholders, with a shared understanding of what AI the organisation is running, what data it has access to, and what would happen in a worst-case scenario. Teams should also complete an honest assessment of where the gaps are so they identify vulnerabilities before attackers do.

How Nomios and F5 can help you start the conversation

Nomios has been helping organisations across the Netherlands and the broader European market navigate complex cybersecurity challenges for years. As AI security becomes one of the most pressing new frontiers in the field, Nomios is positioned as a trusted security and innovation advisor that helps customers understand their AI risk exposure before it becomes a problem.

The technology underpinning that work is F5’s AI security portfolio: a purpose-built suite of solutions designed to test, protect, and continuously monitor AI systems against the specific threats they face. From adversarial red teaming that stress-tests your AI against thousands of real-world attack scenarios, to translating those results into runtime guardrails that intercept and block malicious inputs before they reach your model, F5 provides the technical backbone for a complete AI security strategy.

But technology alone is not enough. Understanding what to deploy, how to configure it for your specific environment, and how to interpret the findings it surfaces—that is where Nomios’ expertise matters. Navigating a new and rapidly evolving risk area is not something most organisations should attempt alone, and it is not something you should have to. We can help you understand your AI risks, deploy active testing and detection, and implement run-time protections in record time.

Ready to understand where your AI deployments stand from a security perspective?

Speak to Nomios today. Our team will help you map your AI exposure, identify the risks you may not be aware of, and chart a practical path toward securing the AI systems your business depends on.