Get Ahead of the Game – LLM Compliance and Mocking

As LLMs (Large Language Models), the technology underpinning modern AI, grow ever more complex and ubiquitous, organizations are increasingly being asked to demonstrate that their use of these technologies is reasonable and responsible. While the regulatory frameworks are still in flux, corporate compliance teams have seen the writing on the wall and are expecting clearer and stricter expectations in the coming years.

Simply put, now is the time to start laying the groundwork for LLM compliance – before you’re required to.

In this piece, we’ll look at the current state of LLM compliance, as well as what it might look like in the context of the coming regulatory revolution. We’ll dive into why mocking and traffic replay play a critical role in proactive compliance, and dig into how tools like Speedscale can help compliance professionals and risk teams build and test systems that are audit-ready from day one. Additionally, we’ll explore how processing and summarizing large text files, such as documents, can be automated using technologies like Vertex AI and Cloud Vision OCR, facilitating the creation of searchable summaries that are stored in databases.

Introduction to Large Language Models

Large language models (LLMs) are a groundbreaking type of machine learning model designed to handle natural language processing tasks with remarkable proficiency. These models, which include well-known examples like generative pretrained transformers (GPTs), are trained on vast amounts of text data, enabling them to understand and generate human language with impressive accuracy. The training process involves self-supervised learning, where the model learns to predict the next word in a sentence, gradually acquiring a deep understanding of syntax, semantics, and the intricate relationships within language.

One of the key strengths of LLMs is their ability to be fine-tuned for specific tasks. Through a process known as prompt engineering, developers can guide these models to perform a wide range of functions, from generating coherent text to answering complex questions. This adaptability makes LLMs a crucial component of modern artificial intelligence (AI) systems, capable of enhancing various applications with their advanced language capabilities.

However, it’s important to note that LLMs also inherit the biases and inaccuracies present in their training data. This means that while they can generate text and perform tasks with high accuracy, they can also propagate existing biases if not carefully managed. Despite these challenges, the potential of LLMs to transform industries and improve human-technology interactions is immense, making them a focal point of AI research and development.

How Large Language Models Work

Large language models work by leveraging a massive amount of text data to train a sophisticated neural network. This neural network, typically based on a transformer architecture, is designed to handle vast amounts of data and generate human-like text. The transformer architecture is particularly effective because it uses an attention mechanism, which allows the model to focus on specific parts of the input data, thereby generating more accurate and contextually relevant responses.

The training process of LLMs involves feeding the model with a vast amount of text data, enabling it to learn the patterns and structures of human language. This process requires a large number of parameters and significant computational resources. The more data and computational power used, the better the model becomes at understanding and generating text.

Once trained, LLMs can be fine-tuned for specific tasks using prompt engineering. This involves providing the model with carefully crafted prompts that guide it to perform particular functions, such as text generation, translation, or question answering. The attention mechanism plays a crucial role in this process, allowing the model to focus on relevant parts of the input data and generate precise and accurate responses.

Overall, the ability of LLMs to process and generate natural language with high accuracy makes them invaluable tools in the field of artificial intelligence. Their advanced capabilities are a result of the intricate training processes and the sophisticated architecture that underpins them.

Applications of LLMs

The applications of large language models are as diverse as they are transformative. These models have the ability to generate human-like text, making them invaluable for a wide range of natural language processing tasks. One of the most prominent applications is text generation, where LLMs can create coherent and contextually relevant text for various purposes, from writing articles to generating responses in chatbots.

Language translation is another significant application of LLMs. By understanding the nuances of different languages, these models can accurately translate text, breaking down language barriers and facilitating global communication. In addition, LLMs excel in question answering, providing precise and informative responses to user queries, which is particularly useful in customer service and virtual assistant applications.

LLMs are also making waves in the field of code generation. By understanding programming languages and coding patterns, these models can assist developers in writing code, debugging, and even generating entire programs. This capability not only speeds up the development process but also reduces the likelihood of errors.

Content creation is another area where LLMs shine. From generating marketing copy to creating engaging social media posts, these models can produce high-quality content that resonates with audiences. Additionally, LLMs are used in retrieval augmented generation, where they leverage large databases of text to generate accurate and contextually relevant responses to user queries.

Overall, the potential of LLMs to revolutionize industries and improve human-technology interactions is immense. Their ability to understand and generate human language opens up new possibilities for innovation and efficiency across various fields, making them a focal point of research and development in artificial intelligence.

What Is Large Language Models Compliance?

LLM compliance is a broad term referring to a set of practices, controls, systems, and documentation that help ensure an organization aligns its use of generative AI models with emerging and existing regulations and governance frameworks. While the legal system is still playing catch-up with LLM compliance, this discipline has a wealth of knowledge and expertise to draw from in the realm of traditional enterprise risk management, corporate governance, and administrative law, enhancing conversational AI capabilities to closely mimic interactions typically performed by human agents.

Whether you’re a compliance officer at a tech company or part of a legal team supporting product development, the goals are familiar: reduce regulatory risk, ensure ethical use, and demonstrate controls through repeatable, inspectable processes.

Just like corporate compliance efforts in healthcare, financial services, or anti-money laundering programs, LLM governance will demand demonstrable safeguards. This is especially true for LLM-adopting orgs in the European Union, where privacy and business regulations are much more significant – and more commonly enforced – than in the United States.

Potential Compliance and Regulatory Frameworks

To give you a sense of where this problem originates and where it’s headed, let’s take a brief look at the types of regulatory frameworks an LLM-augmented Application might have to consider.

GDPR (General Data Protection Regulation) – EU

This regulatory framework applies to any organisation handling data from EU citizens, regardless of where this handling occurs or where the company is located. It requires explicit consent before processing personal data, and grants users rights such as data access, deletion, and portability. LLMs must avoid generating responses that contain or leak personally identifiable information (PII) without consent, and must handle unstructured data in compliance with GDPR to ensure data protection. Taking this a step further, automated decision-making using LLMs may fall under Article 22, which gives users the right to object to such processing. Failure to adhere to the GDPR may carry significant financial and legal consequences.

CCPA / CPRA (California Consumer Privacy Act / California Privacy Rights Act) – USA

California passed this framework to give Californian citizens a significant boost in addressing the lack of US privacy regulations. In essence, this framework governs businesses that collect data sets from any California resident. It requires disclosure of data collection and sharing practices, and allows users to request an opt-out from data sales. Additionally, users can choose to delete their information, and any organisation adopting an LLM that interfaces with California citizen data must respect opt-out signals and user deletion requests.

HIPAA (Health Insurance Portability and Accountability Act) – USA

This regulatory framework is quite famous in the United States and applies to healthcare providers, insurers, and any service handling protected health information (PHI). LLMs processing documents containing PHI must comply with privacy, security, and breach notification rules, and must operate within the context of Business Associate Agreements (BAAs). Usage of LLMs in diagnostic tools, chatbots, or patient-facing services must avoid unauthorized data exposure, and such exposure may carry hefty fines or punitive legal measures.

PCI DSS (Payment Card Industry Data Security Standard)

The PCI DSS framework applies to all organisations that handle credit card information – for many LLM-enabled orgs, this is a framework that might not seem directly related to the LLM integration, but is nonetheless triggered by any sort of payment vending. LLMs must not store or expose sensitive authentication data such as card numbers, CVVs, or any character of this information, and the use of LLMs in customer support for financial institutions must be tightly controlled and audited.

China’s Interim Measures for the Management of Generative AI Services

This is an interim measure implemented by China ahead of a more complete regulatory effort governing generative AI. This regulation is for any AI LLM system operating in China or with Chinese data – it prohibits the AI systems from generating content that is considered a subversion of state authority, but it also governs any data or generative action which violates – either in whole or in part – any of the myriad data protection laws within Chinese territory. This mandates significant data source transparency, as well as the labelling of AI content and systems, or model auditing. Reinforcement learning, particularly reinforcement learning with human feedback (RLHF), plays a crucial role in enhancing model performance to comply with these regulations by reducing biases and improving the accuracy of generated content.

AI Act (proposed) – European Union

This act is a proposed regulatory framework to regulate AI by risk category – that is, whether the AI application is high or low risk to data from European Union citizens. LLMs used in recruitment, finance, or law, for instance, may be designated high-risk, requiring impact assessments and explainability. Fine tuning these general-purpose LLMs (like GPT-style models) for specific tasks is crucial to comply with the AI Act, as it helps mitigate biases and inaccuracies, ensuring more accurate and contextually relevant outputs. General-purpose LLMs may be required to register and disclose training data summaries.

NIST AI Risk Management Framework (USA, voluntary)

The NIST AI Risk Management framework is a – at least for now – voluntary framework for AI development and deployment. It encourages responsible AI development that is aligned with privacy, fairness, transparency, and security by incorporating human feedback to improve LLM performance. While it is not currently legally binding, it’s quickly becoming a de facto standard for federal contractors and AI vendors.

Why Mocking Matters for Compliance

One of the most effective ways teams can prepare for future LLM regulations is by building robust mocking systems. Mocking allows teams to simulate LLM traffic, test edge cases, validate data handling workflows, and document how systems respond under different inputs – all without hitting production or leaking sensitive data. For example, this capability illustrates how LLMs can be utilized to ensure compliance by providing concrete instances of how systems manage various scenarios.

For compliance professionals, this kind of mocking isn’t just QA hygiene – it’s a compliance infrastructure that supports risk assessment efforts, helping to satisfy audit requirements at scale.

Speedscale excels at this kind of mocking, providing a traffic capture and replay solution that can help deliver usable data and deterministic mocks based on real-world traffic patterns.

How Speedscale Supports Regulatory-Compliant LLM Mocks

Speedscale can be effectively used to build LLM mocks that support regulatory compliance by leveraging its core strengths in traffic replay, synthetic test generation, and data masking. By incorporating advanced machine learning models, Speedscale enhances compliance efforts through more accurate and powerful mocks. Let’s take a look at how Speedscale can unlock more powerful mocks and align more accurately with compliance and regulatory frameworks.

Replay of Real API Traffic with Sensitive Data Masking

Speedscale captures real API traffic and can replay it deterministically, which allows LLM developers to simulate production-like conditions without risking exposure of real user and production data. This is critical for regulatory frameworks like GDPR, HIPAA, or CCPA, which mandate that sensitive data and data sets must not be used inappropriately or without consent.

Through this process, you can feed replayed traffic into a mocked LLM environment to do everything from response validation to contract assurance, testing the overall behaviour of the mock and ensuring no regulated data is used improperly. Speedscale includes automatic data masking, which helps to scrub PII or PHI from traffic during capture. This can help identify potential issues with compliance in your data efforts and ensure no breach or misuse occurs during the testing and mocking phases.

Validation and Regression Testing

One of the huge benefits of Speedscale is the fact that you can use traffic snapshots to automatically create test assertions. You can compare actual behaviour against the expected behaviour under a variety of different compliance scenarios, as well as validate that functionality during network failover states, third-party failures, and more. In essence, Speedscale lets you test from end to end, and then work backwards to potential fail states and risks.

Regulatory bodies often require auditability and consistency. Speedscale can help deliver this and align with frameworks like SOC 2, PCI DSS, and ISO 27001, where predictable and auditable behavior of AI/ML systems is mandatory. By testing each task that LLMs are fine-tuned to handle, such as interpreting questions or generating responses, Speedscale ensures compliance and reliability.

Zero Trust and Agentic Access Simulation

In the age of regulatory compliance, zero-trust architectures have stood out as a great solution to ensure privacy first. While many testing solutions struggle with zero trust architecture, Speedscale is built to model based on traffic, not privileged backend access. Accordingly, more complex solutions such as agentic workload-identity-based access controls (e.g., SPIFFE/SPIRE) can be mirrored through observed traffic within Speedscale itself.

This is hugely important, as LLM agents are increasingly granted machine-to-machine privileges. Speedscale can help you ensure these mocks conform to role-based or behavior-based access control rules as required under various laws, ensuring accuracy both in transit and against the contract of the system itself. By closely mimicking the interactions typically performed by human agents, Speedscale ensures that privacy and compliance are maintained within zero trust architectures.

Summary of Compliance Benefits

If you’re deploying LLMs in highly regulated environments (like fintech, healthcare, or EU cloud), Speedscale can be a key tool to build compliant, controlled test environments that reflect production behavior without violating legal frameworks. Foundation models, which are advanced AI systems trained on extensive datasets, play a crucial role in understanding and generating natural language, making them essential in these sectors.

Compliance, Before You’re Forced To

As software development is more and more focused on the ethics and regulatory compliance concerns of AI, LLM compliance will only grow in importance. Enhancing model performance through reinforcement learning with human feedback (RLHF) is crucial for reducing biases and improving the accuracy of generated content, addressing issues such as hallucinations that can emerge from training on extensive unstructured data. Getting this right now is not just the right thing to do – in many cases, it is business critical. LLM compliance is set to be a massive concern over the next decade, with ethics professionals and regulatory compliance assurance programs growing in equal measure. The reality is that you will have to ensure compliance and regulatory alignment, and right now, it is easier and cheaper to do so than to wait until your hand is forced.

Speedscale is well-positioned to support this work. Whether you’re focused on application process compliance, data privacy, or broader business law concerns, mocking LLM systems now will pay dividends later. It lets you complete your compliance posture before the rules are written – and be ready when they are.

In the world of LLM risk management, the best time to start is now. You can get started with Speedscale with a free trial and jump on your journey to compliance today!