Artificial intelligence and security: the LLM challenges

Artificial intelligence and security are two sides of the same coin: adopting large language models (LLMs) reliably in a company means knowing their risks and governing them with method. While generative AI redefines automation, data analysis and customer interaction, its spread introduces new vulnerabilities, from prompt injection to data leakage and hallucinations. This evergreen guide explains the concrete threats, how to protect data, why governance and human oversight are decisive, and how to reach safe AI adoption, starting from the key questions every organization asks.

The topic is not theoretical. LLMs are now embedded in chatbots, AI agents and workflows that touch customer data, confidential documents and internal systems. Understanding where the risks hide, and which countermeasures address them, is the prerequisite for innovating without exposure. In the sections that follow we move from the map of vulnerabilities to governance practices, all the way to a concrete adoption path.

What are the main security risks of LLMs?

The main security risks of LLMs concern how the model receives instructions, handles data and generates answers. Unlike traditional software, a large language model interprets natural language: this flexibility is also its attack surface. Organizations such as OWASP, which publishes the Top 10 for LLM Applications, and NIST with its AI Risk Management Framework exist precisely to systematize these threats and provide a common language to address them.

The first family of risks is prompt injection. These are malicious instructions inserted into user inputs or into external content the model reads (a web page, a document, an email), designed to hijack its behavior. A successful attack can make the system ignore its rules, reveal information it should protect, or perform unauthorized actions. When the LLM is connected to tools that act on the real world, such as a CRM or an ERP, the risk grows because the manipulated instruction can translate into a concrete operation.

The second critical area is data leakage, the unintended exposure of sensitive data. It can happen in several ways: confidential data entered into prompts that ends up in logs or unprotected histories, information the model "remembers" from previous interactions, or answers that mix data from different users. This is why managing privacy and the data perimeter is not a technical detail but an architectural choice.

Then there is the matter of hallucinations: LLMs can produce plausible but false answers, presented with the same confidence as correct information. In business contexts this is particularly insidious, because a wrong answer can influence decisions, generate incorrect content or undermine user trust.

In summary, the vulnerabilities to watch most closely are:

• Prompt injection: manipulation of the model's behavior through hidden instructions in inputs.
• Data leakage: leakage of sensitive data through prompts, logs or system memory.
• Hallucinations: incorrect but convincing outputs, not grounded in verifiable sources.
• Excessive autonomy: actions performed on external systems without adequate controls or limits.
• Lack of traceability: difficulty in reconstructing what the system did and why.

How to protect data and ensure the reliability of answers?

Protecting data means deciding, even before choosing the model, where information lives, who can access it and what is sent to the LLM. A secure architecture limits exposed data to the minimum necessary, separates environments, encrypts information in transit and at rest, and clearly defines the retention of prompts and answers. Privacy, in other words, must be designed in, not added afterward.

On the reliability front, the most effective technique to reduce hallucinations is RAG (Retrieval Augmented Generation). Instead of relying solely on the model's "internal" knowledge, the system first retrieves the relevant documents from the company's knowledge base and then generates the answer, anchoring it to those sources. This makes the output more accurate, verifiable and traceable to the user's real data. We explored how this approach works in our article on the RAG system, where we explain how information retrieval improves conversation quality.

Reliability, however, does not depend on technology alone. The quality of AI conversations matters: natural, context-aware exchanges that recognize when a request falls outside the system's scope. A good assistant also knows how to say "I don't know" or hand over to a person, instead of improvising an answer. This design honesty is an integral part of both perceived and real security.

Among the most effective measures to ensure trustworthy answers:

• Grounding in sources with RAG and citation of the documents used.
• Clear boundaries on the topics and actions allowed for the system.
• Filtering and validation of inputs and outputs before they are shown or executed.
• Continuous monitoring of conversations to detect anomalies and drift.

Governance and human-in-the-loop: control stays human

Governance is what turns a set of good intentions into a controllable, accountable system. It means defining roles, policies and limits: who can configure the AI, on which data it can operate, which actions it can take autonomously and which require approval. Frameworks such as the NIST AI RMF and the guidance from ENISA, the European cybersecurity agency, help structure this work across the entire lifecycle of the solution, from design to monitoring.

The cornerstone principle is human-in-the-loop: keeping a person in the decision loop for the highest-impact choices. This is not about slowing automation down, but about placing control where it is needed. Low-risk operations can proceed autonomously, while sensitive ones, for example an action that modifies critical data or communicates with a customer in delicate situations, go through human validation. This balance protects the organization and increases the trust of those who use the system. The same principle guides the design of AI agents for business, where the ability to act must always be paired with clear boundaries and oversight.

Security, moreover, inevitably intertwines with ethics: transparency toward users, absence of bias, accountability for automated decisions. We dedicated a deep dive to the relationship between ethics and artificial intelligence, because a secure system is also a system you can trust.

How to adopt AI safely in your company

Safe adoption starts from a concrete use case and a risk assessment, not from enthusiasm for the technology. It pays to choose a well-defined process, map the data involved, define what the system can and cannot do, and start with a monitored pilot. From there the solution is refined based on real usage, expanding the perimeter only when controls prove effective.

On the operational side, often-underrated factors also matter: simplicity of implementation, use and maintenance reduces the risk of errors and insecure configurations; cost predictability avoids surprises and enables transparent investment management; freedom from vendor lock-in ensures you can change technology or move your data elsewhere over time, maintaining flexibility and independence. These seemingly managerial aspects have a direct impact on the security and sustainability of the solution. The same applies to high-volume scenarios, such as AI for IT help desk services in the Bestway case study, where reliability and control make the difference in daily operations.

A safe adoption path typically goes through these steps:

• Defining the use case and analyzing the associated risks.
• Mapping and protecting the data involved, with clear access and perimeters.
• Designing with RAG and human-in-the-loop for reliability and control.
• Monitored pilot, with quality and security metrics defined in advance.
• Going to production with traceability of actions and continuous monitoring.

The Athics perspective: the webinar on LLM security

Athics dedicated a webinar to these topics, designed for companies that want to integrate AI strategically, with a specific focus on Large Language Models. The event, featuring Stefano Somenzi, CTO of Athics, addressed the key questions about implementing artificial intelligence: from protecting data privacy with LLMs to methodologies for ensuring the reliability of answers, from the issue of hallucinations to the use of RAG technology to obtain accurate, verifiable outputs.

The session also covered the quality of AI conversations, performance optimization, simplicity of implementation and maintenance, cost predictability and strategies to avoid vendor lock-in. Ample time was dedicated to a question and answer session on the most frequent security concerns, with particular attention to chatbots and AI agents, tools that are becoming increasingly pervasive in the business world.

Conclusion

Talking about artificial intelligence and security does not mean holding innovation back, but making it sustainable. The risks of LLMs, prompt injection, data leakage and hallucinations, are known and addressable with solid architectures, techniques such as RAG, clear governance and human control at the right points. Companies that build security in from the design stage adopt AI with confidence and reap its value without exposure.

If you want to understand how to bring generative AI into your processes safely and reliably, talk to our team: we will help you define the right use case, protect your data and bring the solution to production with the proper controls.