Imagine our global workforce, constantly navigating an ever-shifting maze of compliance rules across personally identifiable information (PII), intellectual property (IP), export controlled information (ECI), and covered defense information (CDI), each varying by jurisdiction. No amount of training can realistically equip every team member to flawlessly handle these complexities.

Our compliance landscape is vast and dynamic. Even with the best policies and training, expecting our staff to stay abreast of every international compliance change is unrealistic. Engineers and creators are spending valuable time deciphering regulations instead of innovating. This isn’t just about individual knowledge; it’s about the massive, growing debt of managing and classifying our existing and new data in line with these regulations. This process debt is where our real risk lies.

We need to leapfrog this challenge. Let’s integrate AI directly into our content creation tools. Imagine AI that automatically classifies data the moment it’s created, tailored to the specific compliance needs of each jurisdiction we operate in. This isn’t just about reducing risk; it’s about liberating our workforce from the impossible burden of compliance minutiae, allowing them to focus on what they do best.

This AI-driven approach isn’t just an incremental improvement; it’s a transformative step towards a truly compliant culture. It’s about making compliance seamless and intuitive, embedded in the very fabric of our daily operations. This is how we mitigate risk, reduce process debt, and empower our teams to innovate without the looming shadow of non-compliance.

Navigating the process debt maze as a new hire – without and with an AI compliance co-pilot.

Without AI compliance co-pilot:

For a new hire stepping into the intricate labyrinth of corporate compliance, the journey can be daunting. There’s a complex web of constantly evolving rules and regulations spanning multiple domains like PII, IP, ECI, and CDI, each with its nuances and jurisdictional variations. This landscape forms a “process debt maze” where each turn presents a new compliance challenge, requiring extensive training and constant vigilance. The new hire must navigate this maze relying heavily on memory, frequent consultations of policy manuals, and continuous learning of new regulations – a time-consuming endeavor that can often lead to errors, confusion and frustration. This environment not only burdens the individual with overwhelming compliance responsibilities but also detracts from their primary job functions, stifling productivity and innovation.

With AI compliance co-pilot:

Now envision the transformative impact of an AI compliance co-pilot in this scenario. This AI tool acts as a personalized navigator, guiding the new hire through the same maze with ease and efficiency. As the new hire creates or handles data, the AI instantly classifies it according to the latest compliance policies and regulations, tailored to specific jurisdictions and domains. It’s like having an expert whispering the right course of action at every turn, ensuring compliance without the exhaustive effort of manual classification. The maze that once seemed intimidating is now a well-lit path, allowing the new hire to focus on their core responsibilities while the AI handles the complexities of compliance. This seamless integration of AI into the workflow not only empowers the employee but also significantly reduces the risk of compliance errors, transforming the daunting maze into an intuitive, navigable landscape reducing process debt by orders of magnitude at scale.

What is compliance co-pilot?

A truly supportive role for AI and a force enabler for company culture is one where we leverage contextual awareness with dynamic compliance-based large language models (LLM). It is AI, powered by an LLM and dynamic corpus associated in real-time to relevant compliance domains, adept at understanding the context of the data being processed.

When a user works on a spreadsheet containing financial data, the LLM can recognize the content and context as financial and ‘know’ how to determine which compliance domain rules apply for an accurate and timely classification decision.

Domain-specific corpus and analysis: The AI, leveraging the capabilities of LLM, uses a corpus specifically tailored to the industry, country and company policies is called a compliance co-pilot. The LLM’s advanced natural language processing abilities allow it to analyze text and data more deeply and contextually, identifying relevant compliance and classification parameters. This approach ensures that the classification is aligned with the specific nuances of the data’s domain, such as financial regulations and internal compliance policies.

Dynamic learning and corpus expansion: The compliance co-pilot continuously learns from new data input, user interaction and updates of regulations or company policies. Dynamically expanding its knowledge base (corpus) and refining understanding, improving classification accuracy and adaptability over time with increasingly simplified human validation of classification data by domain taxonomy.

Enforced classification with compliance co-pilot insights: The system ensures that no data, like a spreadsheet, can be saved or shared without an AI-determined compliance classification. Leveraging LLM, the AI can make more nuanced and informed decisions about classification, considering a wider array of factors and potential implications.

An example: As a user inputs financial data into a spreadsheet, the dynamic LLM-based AI understands the financial context and content through sophisticated language analysis. The AI then consults its specialized corpus, which includes comprehensive data on financial regulations, organizational policies and potentially country-specific legal requirements.  It then assigns an appropriate compliance classification to the spreadsheet, such as “confidential – financial,” based on its deep and contextual understanding.

Key differentiators and advantages with dynamic LLM/corpus

• LLMs bring a high level of accuracy in understanding and classifying text, leading to more precise compliance classifications.
• The AI operates unobtrusively, providing compliance assurance without disrupting the user’s workflow.
• The LLM’s ability to learn and incorporate new information ensures the system remains current with the latest regulatory and policy changes.
• The sophistication of LLMs in analyzing and classifying data reduces the potential for human error and inconsistency in compliance adherence.
• The versatility of LLMs makes the system effective for different types of data and across various departments within an organization.

With dynamic elasticity when integrating LLMs into the AI system for data classification, the process becomes more intelligent, nuanced and adaptable, significantly enhancing compliance management in complex business environments while maturing and improving all aspects through continued use.

How might this work? Incorporating an LLM (Large Language Model) into a compliance management system with a dynamic, real-time updating corpus based on compliance domain taxonomies offers a sophisticated approach to handling complex regulatory environments.

Here’s a more granular look at how this is achieved –

Dynamic corpus development:

The LLM’s corpus is continuously fed and updated with information from various compliance domain taxonomies. These taxonomies categorize and organize compliance-related information into structured formats, covering areas like finance, healthcare, data privacy, international trade, etc. The corpus includes regulatory texts, legal rulings, industry-specific guidelines and even internal corporate policies.

Real-time updating sourced from compliance domain taxonomies:

The corpus is not static; it’s designed to integrate new information as soon as it becomes available. This includes the latest regulatory changes, amendments to international laws, or updates to internal company policies. Advanced data scraping and processing tools are employed to automatically gather and integrate this new information into the LLM’s corpus through the compliance data map.

Tailored to specific compliance needs:

The LLM can be tailored to the specific compliance requirements of an organization or industry. This involves training or fine-tuning the model on the dynamically updated corpus so that it becomes highly proficient in understanding and interpreting the specific nuances and language of each compliance domain.

Contextual analysis and classification:

When a user inputs data (like in a spreadsheet or document), the LLM analyzes this content in real-time, utilizing its tailored corpus to understand the context and potential compliance implications.  The model classifies the data based on its understanding of the relevant compliance domain or domains.

Feedback loop for continuous improvement:

User interactions, corrections and feedback can be used to further refine the LLM’s understanding and classification accuracy. This feedback loop ensures that the model stays relevant and accurate over time, adapting to the evolving landscape of compliance.

Integration with authoring and data management tools:

The LLM should be as close to seamlessly integrated into the software tools used as possible, for data creation and management, ensuring that compliance checks and classifications are part of the natural workflow.

User interface and experience:

The interface through which users interact with the LLM should be intuitive, providing clear guidance on compliance classifications and any required actions without overwhelming the user.

Privacy and security:

Given that the compliance co-pilot processes potentially sensitive information, ensuring data privacy and security is paramount. This involves implementing robust encryption, access controls and data handling protocols; in many cases it may require on-prem or internal AI services. While at the outset ingestion of internal AI servers may seem daunting from a financial perspective, when used at extended scale mounting transactional costs of on-line solutions add up. It should be noted online AI solutions may not be usable for some of the most important AI use-cases, invention harvesting and critical technology, who wants to share their most innovative ideas with an AI provider?

To pull it all together, by leveraging compliance co-pilot with a dynamic, real-time updating corpus derived from compliance domain taxonomies, organizations can significantly enhance their compliance culture. This approach not only helps in maintaining regulatory compliance but also reduces the workload on employees, allowing them to focus on their core activities while the LLM handles the complexities of compliance classification and management. The process debt reduction to average scale defense industrial base companies using this approach constitutes a key technological differentiator and competitive advantage to those who adopt to it versus those who do not.

David Harris is a partner at TC Engine. 

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