The audit industry has long been driven by data: financial records, transactional details, compliance reports, and more.
As the volume of data explodes and regulatory requirements become more complex, new technologies are being employed to enhance the efficiency and effectiveness of audits.
One such technology making waves is the use of Large Language Models (LLMs), such as OpenAI’s GPT or similar models.
While LLMs have the potential to revolutionize audit processes, they also raise critical concerns around privacy, security, and data quality—concerns that audit professionals must address proactively[1].
This blog will explore these challenges and provide practical solutions, ensuring that even the most technophobic auditor can sleep soundly at night.
Understanding LLMs and Their Role in Auditing
LLMs are artificial intelligence (AI) systems trained on vast amounts of data, enabling them to understand, generate, and manipulate language with impressive accuracy. In the context of auditing, these models can be deployed to analyze large datasets, review contracts, interpret regulatory requirements, and assist in generating audit reports.
The speed and scalability of LLMs make them particularly useful for handling repetitive tasks, identifying anomalies, and providing insights that would take a human auditor much longer to uncover.
In fact, recent studies, including one from Harvard, have shown that junior team members partnering with AI can often outperform their more experienced colleagues who aren’t using AI. It’s like giving your 22-year-old auditors a six-year experience boost overnight [2].
However, while LLMs promise to level up your team and processes, Auditors must grapple with several key challenges.
1. Privacy: Handling Sensitive Data with Care
One of the most significant concerns for auditors is privacy, especially given the sensitive nature of the data they handle. Audits often involve accessing confidential financial information, personal client details, and proprietary business records. When leveraging LLMs, particularly those hosted on cloud-based platforms, there is an inherent risk of exposing this sensitive data.
LLMs like GPT are typically trained on vast datasets, including publicly available text from the internet, but they may also require fine-tuning on specific audit-related data to deliver relevant results. This can lead to several privacy-related challenges:
- Data Input and Output: When auditors input sensitive data into an LLM, there is the risk that the data could be stored or used for model improvement, depending on how the system is configured. Furthermore, outputs generated by LLMs may unintentionally include confidential information or be influenced by biases present in the training data.
- Third-party Hosting: Many LLMs are hosted by third-party service providers, and using these platforms means auditors are entrusting client data to external entities. If data privacy protocols are not rigorously enforced, there is the potential for breaches or misuse.
Solutions:
- Data Encryption and Anonymization: Encrypting data before feeding it into an LLM and using anonymized datasets can help mitigate privacy risks. This ensures that any sensitive information is obfuscated and cannot be traced back to specific individuals or entities.
- Supplier Due Diligence: Most firms are likely to use a third party to host their LLM. Rigorous due diligence on the third party must be undertaken to ensure that privacy and security controls are in place.
2. Security: Guarding Against Cyber Threats
Closely tied to privacy concerns is the issue of security. The integration of LLMs into audit workflows introduces potential vulnerabilities, including cyberattacks, data breaches, and unauthorized access to proprietary information.
Some of the specific security concerns associated with using LLMs in auditing include:
- Model Vulnerabilities: AI models can be susceptible to adversarial attacks, where malicious actors manipulate inputs to force the model to produce incorrect or biased outputs. In an audit setting, this could result in flawed audit conclusions or misidentification of risk factors.
- Data Storage: If the data used to train or interact with the LLM is stored insecurely, it could be exposed to hackers or other unauthorized parties. Given that audits often involve financial data, the consequences of such breaches can be severe, leading to reputational damage, regulatory penalties, and loss of client trust.
Solutions:
- Robust Cybersecurity Protocols: Audit firms should adopt stringent cybersecurity measures to protect the data being processed by LLMs. This includes regular security audits, ensuring compliance with data protection regulations (such as GDPR or CCPA), and deploying advanced firewalls and encryption technologies.
- Access Control and Monitoring: Limiting access to the LLM system and regularly monitoring usage can help mitigate the risk of unauthorized access. Audit teams should ensure that only authorized personnel can input and extract data from the model, and all interactions should be logged for traceability.
3. Data Quality: Ensuring Reliable and Accurate Results
The effectiveness of an LLM is only as good as the data it is trained on. In the context of auditing, where accuracy and reliability are paramount, data quality is a critical concern. Poor-quality data can lead to inaccurate results, flawed risk assessments, and incorrect audit conclusions.
Some of the challenges related to data quality in LLMs include:
- Biased or Incomplete Data: LLMs are trained on existing data, which can sometimes contain biases or be incomplete. In an audit scenario, this could result in the model overlooking critical risk factors or placing undue emphasis on irrelevant information.
- Contextual Misunderstanding: LLMs may struggle to understand the context in which audit-specific terms or financial data are presented. For example, they may misinterpret industry-specific jargon or fail to account for the nuances of regional regulations, leading to suboptimal audit outcomes.
Solutions:
- Curating High-Quality Datasets: To improve the quality of results produced by LLMs, audit firms must ensure that the data used to train the model is accurate, complete, and relevant to the audit industry. This may involve curating specialized datasets that focus on regulatory frameworks, financial transactions, and audit standards.
- Human Oversight: While LLMs can assist with many tasks, human auditors must retain control over the final output. It is essential to have auditors review and validate the conclusions drawn by the model to ensure that they align with the facts and the audit’s objectives.
Conclusion
Large Language Models have the potential to transform the audit industry by automating repetitive tasks, analyzing vast datasets, and identifying risks with unprecedented speed and accuracy. However, these benefits come with significant responsibilities. Privacy, security, and data quality are crucial considerations that auditors must address to ensure that LLMs are used ethically and effectively.
These three principles underpin how Validis builds and runs our platform, not just in our use of LLMs. Security, Privacy and Data Quality are first and foremost in our culture and are the first areas considered when we come to design software and infrastructure.