AI Game Changer: How are Specialized Language Models the Safest Alternative to LLMs for Regulatory, Risk, and Compliance Programs?

Introducing Our Author

Venky Yerrapotu​, Co-Founder and CEO at 4CRisk.ai is a hands-on leader with over 20 years of experience in building and onboarding hundreds of customers to high performance GRC technology platforms, His expertise and experience over the last 5 years is in AI-powered software solutions for regulatory, compliance and risk professionals. He joins us in the blog to explore how specialized language models (SLMs) offer secure and private interactions and analyses with significant advantages for governance, risk, and compliance program teams seeking AI-powered automation.  

What are Large Language Models?

Over the past year, you’ve heard a great deal about Large Language Models (LLMs), as many organizations have had to limit the use of LLMs such as ChatGPT, Gemini Pro, and others when they find their organization’s sensitive data might get compromised. In addition, because of the breadth of the LLM content, LLMs have been found to contain biases and produce incorrect or nonsensical outputs, known as "hallucinations’, not to mention concerns over copyrighted IP being used, the environmental cost of sustaining these models and the lack of control and explainability in LLMs.

What are Small Language Models?

For the enterprise, an alternative has emerged: the Specialized Language Model (SLM). Unlike LLMs, SLMs are domain-specific, private, secure, highly accurate, faster, and more cost-effective.  For risk, compliance, and regulatory intelligence domains, SLMs are the only approach that addresses these key concerns, making AI-powered automation more accessible and effective. With SLMs, teams can transform how they manage their work.

Let’s dive deeper into HOW this is done with the 4CRisk platform, AI Agents, and products.

1. How do Specialized Language Models Provide Stronger Data Privacy and Security?

• 4CRisk’s Platform keeps sensitive data within the virtual walls of the Enterprise: Large, general-purpose language models (LLMs) often require sending data to external servers. An example is information shared with an LLM, disclosing how an internal control works, or asking questions on how to mitigate weak controls; this information could be shared, and put the enterprise in a vulnerable position. Unlike LLMs, SLMs can be deployed within a company's infrastructure or a private cloud environment. This keeps sensitive GRC data within the organization's control, stopping data from flowing out to public LLMs. ‍
• 4CRisk’s private cloud deployments employ zero-trust security principles, such as penetration testing, SOCII certifications, and other security measures, to minimize the risk of data breaches and unauthorized access.
• ‍^‍4CRisk’s Private SMLs can provide audit trails and role-based access, ensuring that the right people have access to the right level of information at the right time.
• ‍4CRisk’s Private SLMs restrict data sharing through configurations and integrations: With SLMs, there's no need to share sensitive data with third-party AI providers, addressing concerns about data ownership, compliance with data privacy regulations (like GDPR), and potential competitive disadvantages.

2. How Do Specialized Language Models Improve Accuracy and Relevance?

• 4CRisk’s SLMs leverage Domain-specific training: 4CRisk's SLMs are trained on carefully curated regulatory content and GRC-related data. This focused training allows our models to understand the nuances of regulatory language and GRC processes more accurately than general-purpose LLMs. ‍
• Reduced "hallucinations": General LLMs can sometimes generate incorrect or nonsensical outputs, known as "hallucinations." 4CRisk’s SLMs, with their narrower focus, are less prone to these errors, leading to more reliable and trustworthy results.

3. How Do Specialized Language Models Increase Efficiency and Cost-Effectiveness?

• Faster processing: SLMs are typically smaller and more efficient than LLMs on tasks they have been fine-tuned to automate, requiring less computational power and resources. This translates to faster processing times, lower infrastructure costs, and an eco-friendly footprint. ‍ ‍
• Reduced latency: Because AI inferences are performed locally on 4CRIsk’s SLMs within a private environment, there's less latency than sending data to external servers. This enables real-time or near-real-time analysis and decision-making.

4. How Do Specialized Language Models Provide Greater Control and Customization?

• Fine-tuning and adaptation: 4CRisk’s SLMs are more easily fine-tuned and adapted to specific organizational needs and GRC frameworks. This allows for greater customization and alignment with internal policies and procedures, continual improvement, and learning from regulations, rules, laws, and standards safely leveraged from the public domain.
• ‍Explainability and transparency: 4CRisk’s SLMs are more transparent and easier to understand than complex LLMs. This can be crucial for GRC professionals who must explain AI-driven decisions and ensure compliance with regulatory requirements. 4CRisk products provide for Human-in-the-Loop reviews, voting, and collaboration with other team members.   

Why are SMLs a Game-Changer for Regulatory, Risk and Compliance Program Automation?

By offering secure, private, and domain-specific AI capabilities, 4CRisk's SLMs can significantly enhance Regulatory, Risk and Compliance program automation in several ways.

• Automated regulatory change management: Quickly analyze and interpret new regulations, assess their impact on the organization, and update policies and procedures accordingly.
• ‍Automated compliance monitoring: Continuously monitor compliance with relevant regulations, identify potential risks and gaps, and generate alerts for timely action.
• ‍Automated controls rationalization: Monitor controls coverage, merging similar requirements and highlighting duplicate or overlapping controls.
• ‍Automated risk assessments: Streamline risk assessments by automatically analyzing data, identifying potential threats, and prioritizing mitigation efforts.
• ‍Automated report generation: Generate accurate and comprehensive reports for regulatory reporting, internal audits, and management oversight.
• In conclusion, 4CRisk's approach with SLMs addresses key concerns around data privacy, accuracy, efficiency, and control, making AI-powered automation more accessible and effective for Regulatory, Risk, and Compliance programs.This represents a significant advancement in the Regulatory, Risk, and Compliance domain and can potentially transform how organizations manage their work.    

Check out these related blogs and resources  

• https://www.4crisk.ai/post/challenges-for-businesses-choosing-large-language-models-llm-to-automate-business-processes ‍
• http://4crisk.ai/post/eliminating-bias-in-ai-products
• ‍https://www.4crisk.ai/whitepapers/a-practical-guide-to-ai-agents-and-co-pilots