Securing AI applications: building resilience beyond infrastructure

Every week, we speak with business leaders who are exploring how AI can transform their organizations. They see the potential to automate complex processes, deliver better customer experiences, and make faster, smarter decisions. But almost immediately, the conversation turns to an equally important question: how do we keep it safe?

It's a question we hear more often now than ever before. Today, 78% of organizations are using AI in at least one business function, according to McKinsey. What was once experimental is now essential, running core business operations. And the systems that make AI powerful are also the ones that introduce news risks.

AI, once experimental, has now become essential.

Why AI security is different

AI applications aren't built like traditional software. They are intricate ecosystems that combine data pipelines, machine learning models, APIs, and cloud services. Each component can become a point of vulnerability if not properly protected.

When the data feeding a model is compromised, or when an API providing access to a model lacks proper authentication, the risk becomes real. These aren't hypothetical risks. They are scenarios we see organizations facing as they scale their AI initiatives.

AI security isn’t just about defending infrastructure; it’s about protecting intelligence itself.

The emergence of large language models has brought entirely new security challenges. The OWASP Top 10 for LLM Applications highlights vulnerabilities already appearing in real-world deployments, including:

• Prompt injection, where hidden instructions in a prompt are used to alter model behavior or produce harmful outputs. These attacks can be used to bypass security restrictions, manipulate responses, or expose internal logic if prompts are not properly validated.
• Sensitive information disclosure, where systems may inadvertently reveal sensitive or private data, or allow attackers to extract information about training datasets through techniques such as data reconstruction, membership inference, model extraction, or prompt and context stealing.

These threats sit alongside a more familiar challenge: API security. In AI systems, APIs are the gateways through which models are accessed, trained, and managed. Recent research shows that 84% of enterprises experienced an API security incident in the past 12 months, according to Akamai.

Without proper controls like authentication, rate limiting, and input validation, these APIs become easy targets. Weak configurations can expose model parameters or enable unauthorized changes to how models operate.

The challenge of visibility in hybrid environments

Most organizations today aren't running AI in a single, centralized environment. IDC reports that 88% of organizations are now deploying or operating hybrid cloud infrastructure, which often leads to fragmented visibility and control across multiple platforms.

This fragmentation makes it harder to monitor what's happening across AI systems. Data flows between on-premises infrastructure, multiple cloud providers, and edge locations. Maintaining awareness of where data resides, how it moves, and who can access it becomes a significant operational challenge.

Maintaining awareness of where data resides, how it moves, and who can access it becomes a significant operational challenge.

And unlike traditional software, AI models aren't static. They evolve based on the data they process. This makes it harder to know if a model has been subtly manipulated or degraded over time. Continuous monitoring becomes essential, not just to detect potential attacks but to maintain trust in the decisions these systems deliver.

Building security into AI from day one

Effective AI security starts with embedding security from the design phase rather than treating it as an afterthought. This includes rigorous data validation at every step, strict access controls, and keeping development environments isolated from production systems. It means cryptographically signing model artifacts to ensure their authenticity throughout the AI lifecycle.

But technical controls are only part of the equation. Continuous observability and monitoring of model behavior are critical for identifying unusual patterns, tracking how data drifts over time, and detecting deviations in outputs that might signal a problem.

The goal is resilience: building AI systems that can withstand threats and recover quickly without compromising either functionality or trust. This requires automated response capabilities, proactive detection mechanisms, and the ability to contain incidents efficiently across complex, distributed environments.

Building resilience means creating AI that withstands threats and earns trust.

How we help organizations secure AI at scale

At Telefónica Tech, we offer a comprehensive approach to protecting artificial intelligence environments by safeguarding infrastructure, applications, and data. We ensure end-to-end protection of AI systems, including APIs, cloud workloads, and data flows, applying controls such as data exposure mitigation, environment isolation, and model monitoring to detect deviations or manipulations, among others. Our goal is to ensure resilience and trust in AI systems, enabling organizations to innovate safely and comply with the most demanding regulatory frameworks.

This work is never one-size-fits-all. We see strong adoption across generative AI, virtual assistants, predictive analytics, and automated decision platforms. Each comes with its own security considerations.

Our goal is to ensure resilience and trust in AI systems, enabling organizations to innovate safely.

That's where our partnership with F5 becomes particularly valuable. Together, we're helping customers solve some of the hardest problems in AI security. F5 brings deep expertise in Web Application and API Protection, or WAAP, along with sophisticated traffic management capabilities. These technologies integrate seamlessly with our cybersecurity services to deliver comprehensive protection for AI workloads, even in complex multi-cloud environments.

F5's advanced traffic inspection helps us detect and block malicious patterns in real time, from abnormal query behavior to attempted data extraction. Their solutions enable us to validate both the prompts entering AI systems and the responses coming out, preventing data leakage before it reaches users.

We can also dynamically route requests to optimize for cost, compliance, and performance while maintaining consistent security controls across hybrid infrastructure. It's this combination of our cybersecurity expertise and F5's traffic management and API protection that creates a true defense-in-depth strategy.

■ The result: organizations innovate faster, with security embedded into every stage of their AI lifecycle. They gain the visibility to understand what's happening across their AI systems, the controls to prevent unauthorized access, and the resilience to respond quickly when threats emerge.

Three principles for secure AI

When defining an AI security strategy, three principles should guide the approach:

1. AI and security must go hand in hand. One cannot exist without the other. The organizations that treat security as integral to their AI initiatives from the start are the ones that move fastest and with the most confidence.
2. Protecting AI means protecting the data, models, and interfaces that power it, not just the infrastructure they run on. Each layer needs its own security considerations and controls.
3. Building resilient AI systems is what ultimately builds trust. When stakeholders know that AI operates securely, transparently, and in compliance with regulations, it becomes a source of competitive advantage rather than risk.

AI and security must go hand in hand. Only secure AI gives organizations a competitive advantage.

AI systems are no longer peripheral tools. They are the foundation of digital innovation for organizations across every industry. When we integrate security, resilience, and governance from the start, we transform potential vulnerabilities into strengths.