Ringr inyecta 1.2M€ para consolidar su liderazgo en España

When we think of voice conversational assistants, it is easy to imagine an infallible artificial intelligence: a system that never hesitates, always has the information at hand, and responds with scientific precision to every query, no matter what. The reality, however, is much more nuanced —and, at times, unsettling— especially when the conversation takes place in a complex environment like a phone call.

Beyond the Illusion of Certainty

Generative AI, and specifically language models (LLMs), have revolutionized customer service automation. But there is an uncomfortable truth: these systems have been trained to have answers for everything, not necessarily to always tell the truth. This translates into what experts call "hallucinations": the model responds with information that seems plausible, but is fabricated, incorrect, or simply incoherent. It’s not that the AI is trying to deceive; its only mission is to continue the conversation in the most natural way possible, even if that means "filling in" gaps with data that never existed. Consider an AI phone agent assisting a customer asking about the requirements to cancel a banking service. If the model does not have updated information, it will try to deduce or invent an answer to avoid leaving the user without a response. The result can range from a minor inaccuracy to an error that causes a complaint or, worse, a violation of internal regulations.

Why Isn’t It Enough to Ask the AI Not to “Hallucinate”?

One of the most intuitive —and widespread— approaches to mitigate the risks of hallucinations is to introduce explicit rules into prompts: "Do not respond if you do not have the information", "Do not invent data", "Acknowledge when you do not know the answer". However, in practice, these generic instructions are only a patch, not a definitive solution.

The reason is technical and fundamental:

• Language models do not "know" what they know. They do not have an explicit understanding of their limits; they only predict the next word or phrase based on statistical patterns present in the data on which they were trained.

• Even if the instruction is clear, the model may interpret it ambiguously or partially, especially if it detects contradictory signals in the context or if the previous prompt suggests that it should "try to help" at all costs.

• Additionally, LLMs do not have access to real-time external verification sources (unless explicitly endowed with that capability), so they cannot verify whether a piece of data they generate is real or fabricated. Most often, they will "fill" the gap with what they consider most probable or useful based on their training.

Even the most advanced versions of AI can "hallucinate" in simple tasks, such as recognizing not knowing something, because that behavior does not stem from a genuine understanding of ignorance, but rather from previous examples of phrases like "I don’t know" learned from the corpus.

Thus, generic rules help but do not guarantee anything. The only robust way to avoid dangerous hallucinations is to combine AI with external controls: response verification, strict limits on the information available, and validation systems outside the model itself.

The Power of AI Turned into Vulnerability

However, one of the greatest dangers of conversational artificial intelligence is not so much its capacity for error, but the sense of security and competence it projects. The fluency and naturalness with which LLMs respond can make us forget that, in reality, they do not understand either the context or the consequences of their words. This illusion of intelligence leads us to make the increasingly common mistake of delegating sensitive tasks to AI, such as verifying interlocutors before authorizing sensitive operations or revealing confidential information. In practice, this is malpractice that opens the door to significant risks:

• LLMs lack solid mechanisms to distinguish legitimate interlocutors from potential attackers. If the AI receives ambiguous or incomplete signals, it may provide information to those it should not, or skip crucial validation steps.

• An attacker may exploit overconfidence in AI, using social engineering or manipulating the flow of conversation (prompt injection) to obtain sensitive data that should never have been accessible.

• Furthermore, if passwords, access keys, or personal data are introduced into the prompts —with the expectation that the AI will process, validate, or filter them— there is no guarantee that this information will not be accidentally exposed in future responses or through unauthorized access.

The false sense of control and privacy that AI offers can be costly: no model guarantees perfect compartmentalization of information, and in many cases, it is not even possible to audit with precision how sensitive data has been internally managed. Therefore, delegating security or the management of critical data to a language model is not only risky, but it can also be directly exploited against us if a malicious user knows how to pressure the system's weak points.

Phone Assistance: The Challenge of Applying AI with Confidence

The context of phone assistance is particularly conducive for the problems of conversational AIs to manifest more severely. The immediacy and pressure to give quick responses on a call compel language models to constantly improvise, amplifying the risk of hallucinations and unintentional leaks of sensitive information. In a phone conversation, the interaction is usually direct, personal, and full of expectations: users expect quick solutions and often share private data or request critical actions in real time. This dynamic turns any mistake —no matter how small— into a potentially serious risk. A fabricated response, insufficient validation, or a misinterpretation of a request can lead to the delivery of confidential information to the wrong person or the authorization of unwanted operations.

Moreover, the oral nature of phone assistance makes users tend to trust more in the “authority” of the professional voice and limits or eliminates subsequent human supervision. Unlike written channels, where responses are recorded and can be reviewed, in voice, everything happens in real time, making it more difficult to detect, audit, and correct errors afterward.

In this context, the combination of hallucinations and overconfidence in AI is not just a technical problem, but also an operational and security challenge. Delegating the management of sensitive tasks to AI without the proper controls and limitations turns automated phone assistance into an especially vulnerable scenario, where a failure can have immediate and direct consequences for users and companies.

How to Mitigate These Risks?

Recognizing the risks of conversational AI in phone assistance is the first step. The next, equally important, is to design and implement concrete measures to mitigate those risks and build robust and reliable systems. Here are the main keys:

1. Do not expose sensitive information to the LLM

Avoid, as much as possible, introducing personal data, passwords, access keys, or confidential information into the prompts sent to the language model. Use anonymous identifiers and limit the data that the model can see and process. If necessary, separate sensitive information and manage it exclusively in traditional systems, out of the LLM's reach.

2. Role Separation: AI as Support, Never as the Only Barrier

AI should be a complement, not a replacement, of critical validation processes. All sensitive decisions (authentication, authorizations, account changes, etc.) must pass through independent and auditable systems that have explicit rules and human controls if needed.

3. Validation and Double-Checking of Responses

Implement additional layers of verification, especially for responses that may have a direct impact on the user or the security of information. This may include automatic confirmations, random human reviews, or the requirement for explicit evidence for certain responses before executing them.

4. Auditing, Logging, and Continuous Monitoring

Ensure that all relevant interactions are recorded so they can be audited in case of an incident or quality review. Establish periodic review policies to detect patterns of error, possible information leaks, or anomalous behaviors in the AI.

5. Use of AI in Controlled Environments

Whenever the level of risk justifies it, deploy the models in controlled environments directly managed by the organization, minimizing exposure to third parties and limiting external traffic. On-premise solutions or those with total control over data storage and processing add an extra layer of security in the most critical use cases.

6. Training and Awareness of Teams and Users

The human factor remains key: both developers and end-users must understand the limitations of AI. Provide training on how to interact safely with conversational assistants and about the risks of overconfidence. Always inform transparently about the real role of AI and where its competencies end.

7. Design of Restrictive Prompts and Systems

Use prompt engineering that limits what the model can generate and clarifies its limitations. Reinforce with standard messages when the AI does not have sufficient information ("I do not have that data", "I cannot help you with that request"), instead of allowing improvised responses. Explicitly specifying in the prompt what data the model does not have can be a powerful tool for mitigating hallucinations.

8. Continuous Update and Improvement

Conversational AI is a rapidly evolving field. Regularly review and improve policies, controls, and system configurations to adapt them to new threats, vulnerabilities, or technical advancements. Keeping constant updates as a basic principle of security.

Intelligent Responses Require Intelligent Controls

The promise of a conversational AI that never goes blank is powerful, but it also harbors subtle dangers. Hallucinations and the possible leakage of information are not isolated failures, but symptoms of a technology that, while amazing, remains deeply imperfect. In the age of automation, caution and secure design are not optional: they are the only guarantee that AI is an ally and not a hidden risk. Because, ultimately, artificial intelligence is only as reliable as the limits and controls we are capable of imposing on it.