Training Security Experts in AI and LLM Application Security

Artificial intelligence leads in the technology trend, with 2025 Top Strategic Technology Trends from Gartner detailing a number of areas in the advancement of AI that are key focus areas [1]. Of these, Agentic AI and AI governance platforms are featured as the essential parts of technology landscapes of the future as organizations scale out the use of AI technology for decision-making. As great as this rapid adoption may be, it also means a lot of security challenges come along with it. With AI-driven tools becoming key to enterprise operations, understanding and mitigation of AI-related risks are very important among security experts.

Because of these new challenges, the future generation of security professionals needs to be duly equipped to understand the unique risks brought in by AI and LLM. This paper discusses some of the key strategies on how to train future security professionals in the security of AI and LLM applications, building from foundational AI knowledge, identifying risks specific to GenAI and LLM, and navigating regulatory requirements laid out by the likes of the EU AI Act.

Laying the Foundations

First, grounding in the fundamental concepts of AI gives the security professional the ability to manage risks associated with AI. Differently stated, one enables professionals to identify typical vulnerabilities that could be expected in an AI system; therefore, the first step in training necessarily deals with core concepts that include a broad overview of how AI works, including machine learning and neural networks, and the differences between traditional models of AI and GenAI.

The important element of basic training should cover differences between supervised, unsupervised, and reinforcement learning – variants currently in common usage in the development of AI. Understanding which variant is in use is critical because each variant has very different security implications. For instance, models developed through supervised learning are susceptible to a kind of attack called data poisoning, whereby manipulated data biases model outputs. Equipped with this explicit understanding, the security professional will be better prepared to watch for and defend against identified vulnerabilities.

The training also has to involve basic concepts of model training, data quality, and the role of feedback loops since those directly tie into model integrity and security. Also, it is important that security people understand how GenAI produces its output and in what respect it differs from classical AI. This would help in identifying vulnerabilities in systems that, with the use of AI, automatically create texts, images, or other kinds of output.

As responsible AI practices continued to soar, ethics and governance also turned out to be fundamental knowledge today. In inculcating the feeling for ethical considerations such as mitigation of bias, privacy, and accountability, security professionals are able to make contributions in helping organizations build AI systems in keeping with standards of safety and ethics so that even much safer and more responsible applications are provided.

Understanding Generative AI and LLM Risks

Large language models and generative AI have just started getting baked into modern enterprise applications, and with those come an increasing need by security professionals to manage new kinds of security vulnerabilities that no traditional AI-structured rules possess. While such kind of risks are already being addressed by security professionals using different kinds of frameworks, the OWASP Top 10 for LLMs [2] is among those that come to the fore. These highlighted areas comprise the list of critical vulnerabilities in LLMs and give very actionable guidance for defense.

The following is an overview of each of the OWASP Top 10 risks for LLMs:

1. LLM01: Prompt Injection is a vulnerability whereby an attacker manipulates input prompts either for unexpected outputs or leakage of sensitive information. Prompt injection requires strict input validation and sanitization practices.
2. LLM02: Insecure Output Handling: Many a time, LLM could emit unsafe code or sensitive information within the generated outputs for lack of validation. Professionals in this domain should implement verification whether such generated content does not open up any vulnerabilities.
3. LLM03: Training Data Poisoning– While data training is the mainstay of performance in LLMs, it also has the potential to distort model behaviors or produce harmful outputs if any poisoned data is present. Protection of integrity in these training datasets therefore calls for frequent audits and monitoring of such data.
4. LLM04: Model Denial of Service (DoS) – Attackers could leverage different types of interactions with LLMs that are load-intensive to the system and result in system downtime or degraded performance. This risk can be diminished through rate limiting and anomaly detection.
5. LLM05: Supply Chain Vulnerabilities – Most of the LLMs are dependent on third-party components; hence, they might be very vulnerable to supply chain attacks. It is very important to be regularly auditing and verifying the dependencies with a view to minimizing such risks.
6. LLM06 Sensitive Information Disclosure: LLMs will disclose sensitive information if proprietary or private information exists in training data. Recommended strategies for mitigation include data anonymization and strict access controls.
7. LLM07 Insecure Plugin Design: Plugins to LLM can extend LLM functionalities, but if poorly handled or designed, it may also introduce security vulnerabilities. Ensuring plugins are secure and isolated from core functionality reduces exposure.
8. LLM08: Excessive Agency – LLMs might be given too much autonomy and freedom in executing decisions without having sufficient oversight from humans. The boundaries have to be strictly defined concerning the work of the model as an effective way to avoid not foreseen actions.
9. LLM09: Over-reliance-Where very strong confidence in the data output by LLMs is seen, flawed decisions might result. The user training regarding critical evaluation of AI-generated output is an important human control to perform in trying to avoid reliance upon data that may be wrong.
10. LLM10: Model Theft—Exfiltration of LLM model information and its intellectual property themselves can be a result of unauthorized access. Protection against such exfiltration and unauthorized access shall include credential protection, monitoring usage, and implementing encryption.

Apart from the understanding of such risks, security professionals should get acquainted with the mitigation strategies specific to each of these vulnerabilities. The inclusion of OWASP Top 10 into the training programs will give structure to find, understand, and address LLM vulnerabilities in order to prepare for securing AI-powered applications against future threats.

Understanding the Implications of AI Risks

This rise has consequently brought regulatory initiatives all over the world on the proper and responsible use of this new technology in AI. Many therefore try to put boundaries on the use and deployment of this artificial intelligence, or AI. Probably the broadest ranging framework for governance of AI could be the EU AI Act [3], introducing a structured risk-based approach toward the management of the AI applications. Understanding this regulatory landscape is important, given that compliance with international standards – as one might expect – is going to play a significant role in securing AI systems.

It categorizes AI systems, in relation to their use, as minimal, limited, high, and unacceptable risks, considering the consequences an individual and society will face due to those systems. Examples of applications of AI in the high-risk domains: AI applied to health, law enforcement, and finance, where severe damage can be the consequence of a simple error or misapplication. Transparency, Security, and Accountability High-risk AI systems should be subject to strict requirements. Because of this, it becomes important that security professionals understand categories and can assess the level of required scrutiny for different uses of AI; such will determine the required security controls to put in place.

With that in mind, the understanding of key regulatory principles that are foundational to the EU AI Act-transparency, data privacy, and fairness- are indispensable since the security professional will integrate these into foundational risk management and compliance. One would add, transparency allows users and stakeholders to have an idea about how a decision is made by an AI system. It comes into effect in areas where the AI system is affecting aspects that touch on finance or personal well-being. They manage to do this through techniques such as explainable AI, where the process of arriving at a certain decision is documented and can be accessed through the AI model. This makes the auditing of the AI systems easier and gives insight to stakeholders on how a particular outcome came about.

Another important principle that could well be considered in the EU AI Act is data privacy. Large volumes of data are fed to the AI models, and these usually contain sensitive information protected by protection laws such as the General Data Protection Regulation (GDPR). Security professionals are compelled to apply different methods of preserving privacy: anonymization of data, encryption, and differential privacy. This would go a long way in ensuring the minimization of regulatory risks and protection of user privacy if only proper management of personal information could be made certain.

Adding regulatory compliance to this security training in AI will ensure that professionals derive comprehensive insights into the management of AI risks. In tune with understanding the implications of the EU AI Act and other similar regulations, security experts can build appropriate safeguards that meet both the requirements of legality and the need for public-trusted AI systems. This will balance security with ethics and regulations in the foreground, making the process of deploying AI responsibly by organizations without lagging behind globally enacted regulations.

Hands-On Training for AI and LLM Security

Usually, theoretical aspects in cybersecurity goes only so far. Without hands-on training, it would not be possible to prepare security professionals effectively against such sophisticated threats imposed by AI and large language models. In such practical and hands-on learning, the professional knows how to understand and respond to real-world threats, thereby helping them in working for securing an AI-driven system.

The most effective way of preparing professionals for security incidents related to AI is simulation-based learning. In this respect, laboratories can be set up where professionals will be able to have firsthand attack effects of scenario simulations including but not limited to: prompt injection, model theft, and training data poisoning. Generate a simulated scenario, for example, of a malicious actor trying to carry out a model inversion attack against an LLM to get sensitive training data. The lab goes through developing an attack and seeing its consequences, and the students develop countermeasures against this unfolding attack. Hands-on training will also provide students with an understanding of how the attack is carried out and how they can act as fast as possible in a real-world environment.

Practical Lab Environments are also helpful for learning how to secure AI systems. Environments can also be set up to emulate real-world deployments of AI and LLM systems, where professionals can have a playground on safe grounds for the detection of vulnerabilities and application of security controls. Input validation, with a view to reducing the risk of possible prompt injection attacks, or model access control, to avoid unauthorized use with possible theft of the models, are some lab-set exercises that may be performed. Professionals learn technical skills through hands-on labs in which they engender confidence in applying security measures that protect AI systems directly.

Moreover, integration of the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Framework [4] into these labs adds depth to how adversaries approach an attack on AI systems. ATLAS provides structure to the tactics and techniques utilized against AI by attackers, enabling professionals to analyze actual threat behaviors. Incorporating ATLAS into the lab exercises would, therefore, allow such learners to identify an attack and predict adversarial moves with a view to enhancing their capabilities of defense. For example, a simple exercise will be studying ATLAS for tactics such as model weakness exploitation or injecting adversarial inputs with a view to developing methods of countering such threats.

Continuing this line of thought, hands-on learning can also be supplemented through Industry Certifications and Specialization Paths that would systematically put a professional on track to specialize in AI security. Certifications in AI Ethics and Governance, and GenAI Security, among others, give insight into both the theoretical framework of knowledge and the practical ability with which to approach the protection of AI systems. These certifications also include some coursework and hands-on labs that give professionals more depth in their expertise, along with the ability to stay up to date with constantly evolving security best practices.

Building professional capacity for the security challenges of AI and LLM can include simulation-based learning, lab environments, and resources like the MITRE ATLAS framework as part of the security training programs. Indeed, such hands-on and technical proficiency will inculcate proactive habits to keep the systems secure within today’s dynamic threat landscape.

The Way Forward

The more that AI and large language models continue to emerge and be integrated into practically all aspects of modern technology, the more the need is growing for professional security experts who may provide protection for these systems. Training the security experts of tomorrow in the security of AI and LLM applications is not only an upskilling technically but also crucial to ensure safety, privacy, and ethical responsibility on a worldwide basis in the implementation of AI.

What should the organizations do? The organizations shall adopt thorough AI security training programs that strike a balance between theoretical learning and practical competencies and regulatory compliance. The modern security practitioner should embrace lifelong learning, pursuing specialization opportunities in AI/LLM security, to remain relevant as industries continue to transform in record time. These favors are returned by the practitioners who have experience through mentoring and passing knowledge on to the next generations, hence building an environment of learning and sharing knowledge that would make the world of AI resilient in security aspects.

Securing AI is everyone’s responsibility. To us, this means training, mentorship, and security practices that will make sure users use AI technologies safely, responsibly, and with ethics. As we set the course for the next wave of security professionals, we prepare for a resilient, secure, and trusted AI-driven future.

References:

1. Gartner. 2025 Top Strategic Technology Trends. https://www.gartner.com/en/articles/top-technology-trends-2025
2. OWASP. OWASP Top 10 for LLMs and Generative AI Apps. https://genai.owasp.org/llm-top-10/
3. European Parliament. EU AI Act: First Regulation on Artificial Intelligence. https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence
4. MITRE. MITRE ATLAS Framework. https://atlas.mitre.org/.

Parth Shah