The growing adoption of artificial intelligence (AI) in governance, risk, and compliance (GRC) programs is reshaping how organisations manage regulatory requirements. Specialised language models (SLMs) are emerging as a more secure and efficient alternative to large language models (LLMs) like ChatGPT and Gemini Pro, addressing critical concerns related to data privacy, accuracy, and control.
4CRisk, which offers AI-powered tools to support compliance, recently delved into the world of SLMs and their role in compliance.
Over the past year, organisations have faced challenges with LLMs, particularly regarding data security. The potential risk of sensitive data exposure has led many enterprises to limit their reliance on these models. Additionally, LLMs are known for generating biased or inaccurate results, often referred to as “hallucinations,” raising concerns about their reliability. Other challenges include issues surrounding intellectual property, environmental costs, and a lack of transparency and control.
SLMs have emerged as an enterprise-friendly alternative, offering domain-specific, private, and highly accurate AI capabilities. Unlike general-purpose LLMs, SLMs are designed to meet the stringent requirements of regulatory and compliance functions, making them an ideal choice for automation within these critical areas.
One of the key advantages of SLMs is their robust approach to data privacy and security. 4CRisk’s platform ensures that sensitive data remains within the organisation’s infrastructure, mitigating the risks associated with external LLMs. By deploying SLMs within a private cloud environment, companies can prevent data from being shared with third-party providers, enhancing compliance with regulations such as GDPR. 4CRisk’s zero-trust security approach, penetration testing, and SOC II certifications further strengthen security measures.
Accuracy and relevance are other significant advantages of SLMs. 4CRisk’s models are trained on curated regulatory content and GRC-related data, offering a deeper understanding of compliance language and processes. By reducing the likelihood of incorrect or nonsensical outputs, SLMs provide more reliable and trustworthy insights compared to traditional LLMs.
Efficiency and cost-effectiveness are also key benefits of SLMs. With their smaller, more efficient structure, they require fewer computational resources, leading to lower operational costs and a reduced environmental footprint. Since AI inferences are performed locally, organisations benefit from reduced latency, allowing real-time or near-real-time decision-making.
Moreover, SLMs provide greater control and customisation opportunities. 4CRisk’s models can be fine-tuned to align with specific organisational needs, internal policies, and evolving regulatory frameworks. The transparency and explainability offered by SLMs make them a preferred solution for GRC professionals who must justify AI-driven decisions to regulators and stakeholders.
The adoption of 4CRisk’s SLMs is transforming regulatory, risk, and compliance program automation. These models enable automated regulatory change management by quickly analysing new regulations and updating policies accordingly. They also support compliance monitoring, risk assessments, and report generation, streamlining traditionally labour-intensive tasks.
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