For decades, rule-based systems have been the backbone of many industries, providing predictable, structured outputs based on predefined logic. But things have changed with the rise of GenAI. Unlike rule-based systems, GenAI operates in complex, non-deterministic ways, unlocking new possibilities but also introducing new risks.
To effectively identify and mitigate these risks while ensuring AI remains a valuable asset, it’s crucial to understand the fundamental differences between rule-based systems and GenAI.
Rule-Based Systems
Introduced in the 1970s, rule-based systems were among the earliest forms of artificial intelligence, designed to replicate human decision-making through predefined rules and logical statements.
These systems follow an “if-then” logic structure, where each rule specifies a condition (the “if” part) and an action or conclusion (the “then” part). When an input is received, the system evaluates the rules and executes the corresponding action accordingly.
Because they rely on explicitly defined human expertise, rule-based systems work well in areas where knowledge can be clearly structured. However, their rigid nature makes them limited in adaptability and unsuitable for handling complex, dynamic scenarios.
As organizations seek more advanced AI solutions that can process vast amounts of information and respond flexibly, GenAI has emerged as the next evolution.
The Unique Risks of Generative AI
GenAI is a Black Box
GenAI models are trained on massive and diverse datasets to learn grammar, context, semantics, and relationships within data. Whilst they generate human-like responses, they do not “understand” content the way humans do. Instead, they rely on highly complex statistical algorithms to predict the most likely next word or phrase based on input.
During training, controlled randomness is introduced to produce varied and natural-sounding outputs. However, because these models involve intricate layers of interconnected mechanisms, even researchers struggle to fully explain why a specific input generates a particular output. This lack of transparency, often referred to as the “black box” problem, introduces vulnerabilities related to undesired outputs including data leakage, misinformation, and inappropriate or harmful content.
Complicated Layers & Convergence of Logic, Data, User Access and Output
Building a GenAI from scratch demands enormous computational power, vast datasets (Meta’s Llama 3 pretrained on over 15 trillion tokens), and significant financial investment, making it feasible only for a few tech giants such as OpenAI, Google, and Meta. Most organizations instead rely on these as foundation models and customize them using techniques such as prompt engineering, fine-tuning, and retrieval-augmented generation (RAG). This approach is far more accessible and cost-effective, allowing organizations to adapt LLMs for particular use cases without the immense effort and resources required to develop a new model from the ground up for each application.
However, this means organizations have limited visibility into the foundation model’s training, making it challenging to identify and address underlying vulnerabilities. Fine-tuning and customization also introduce interdependencies between the foundation model and added components, where changes in one layer can unintentionally impact others, causing unpredictable behaviors or new vulnerabilities.
And unlike traditional systems where logic, data, user access, and output presentation are managed as separate domains with clear boundaries, GenAI intertwines these elements as a single, dynamic ecosystem, making risk management more complex.
GenAI has a Dynamic Risk Profile
Beyond inherent vulnerabilities, GenAI’s usage scenarios also create unique security challenges.
Inbound threats
Malicious user inputs, such as prompt injection or jailbreaking, which manipulate the model into generating harmful or unintended outputs.
Outbound threats
Risks arising from the model’s responses, such as data leakage, hallucinations, or inappropriate content that could damage an organization’s reputation and compliance posture.
To have a better overview of GenAI threats/vulnerabilities, here are some of the major ones in OWASP Top 10 for LLM apps 2025:
(Inbound) Problematic Training Data
Data poisoning or comprimised supply chain can introduce hidden vulnerabilities, biases, or backdoors into GenAI systems. (Modified model spread misinformation on Hugging Face)
(Outbound) Prompt-Based Attacks
Malicious techniques, such as prompt injection or jailbreaking, manipulate the model to bypass safeguards or generate undesired outputs. (Revealing Bing Chat’s internal rules)
(Outbound) Data Leakage
Sensitive information embedded in training data can appear in responses. (Amazon’s internal documents possibly leaked)
(Outbound) Inappropriate Outputs
Harmful, discriminatory, illegal, or otherwise inappropriate content risks reputational and legal issues. (Humans please die)
(Outbound) Hallucinations
Models fabricate plausible but false information, misleading users. (Google’s AI suggests gluing cheese to pizza)
GenAI-Specific Solutions Are Essential
Understanding how GenAI differs from rule-based systems is just the first step in building a robust security strategy.
To effectively safeguard GenAI, organizations can’t rely on traditional security approaches designed for threats targeting rule-based systems. Instead, they need purpose-built solutions to address these new risks.
Vulcan Attack helps organizations simulate real-world adversarial threats, uncover vulnerabilities, and strengthen their GenAI models before attackers can exploit them.
Vulcan Protect provides continuous monitoring and real-time defense mechanisms to detect and mitigate risks, ensuring GenAI remain secure, compliant, and reliable.
