Artificial intelligence and machine learning are steadily transforming the way businesses approach risk, regulation, and operational oversight. With an ever-evolving cyber ecosystem, compliance is regularly playing catch-up.

Revelations regarding zero-day attacks, where no prior knowledge of the exploitation exists, promote additions and amendments to what defines a “secure environment.” New software and breakthroughs in industry advancement force entities like the Cybersecurity and Infrastructure Security Agency (CISA) and National Institute of Standards and Technology (NIST) to issue new policies year after year, proliferating the complexity and time it takes to perform compliance in an area long associated with paperwork heavy audits, complex standards, and rapidly evolving expectations. This backlog of paperwork creates systemic drawbacks in efficiency and adds cost to the companies forced to comply.

For many organizations, the challenge is not whether to use artificial intelligence (AI), but how to use it. This is where systems like LORE (Linguistic Operations for Relevant Extraction), are turning the tide on workplace inefficiencies escalated by policy assurance. Built using privately hosted open-source large language models (LLM) and designed around retrieval augmented generation, LORE demonstrates how assistive intelligence alongside human-in-the-loop automation can reduce compliance overhead while maintaining clarity, precision, and control.

The business case for smart compliance

Business owners understand that regulatory compliance is not only non-negotiable, but also time-consuming and resource intensive. Automation offers clear benefits here, such as faster response times, improved document tracking, and the ability to surface relevant content on demand. Not all automation is created equal, and consequently blindly trusting AI-generated answers without validation can lead to errors, missed standards, or worse, regulatory action due to inaccurate claims.

LORE’s architecture bridges the gap between efficiency and trust, as it automates early-stage review and classification of compliance content but keeps expert humans in the loop for oversight and final decision-making. This structure ensures that AI is a partner in the process and not a substitute.

Reducing hallucinations & enhancing trust

At the heart of LORE is an increasingly adopted AI and ML (machine learning) approach known as retrieval augmented generation, or RAG. This architecture addresses one of the biggest risks of language models, hallucination, the generation of text that may sound plausible but is factually incorrect or unsupported.

Rather than relying solely on data from model training, RAG enhances outputs with real-world references. When a user inputs a question or answer for review, LORE transforms that input into a vector, a mathematical representation of meaning, and searches within a database of pre-encoded compliance documents. These may include standards like NIST Special Publication 800-53 Rev.5, NIST Interagency Report 8228, or other authoritative frameworks that specify specific security controls that should be in place. The top matching sources are retrieved in real-time and passed to the AI model, which uses those sources to generate responses that are grounded in actual policy text.

This structure operates as a safeguard by drawing on a vetted source library at the moment of inference. The RAG architecture helps mitigate hallucination risks and reinforce trust in AI outputs, which is a compelling reason for business leaders to pursue AI-assisted compliance tools.

Designed for growth & oversight

While Quanterion Solutions Inc. currently uses LORE for cybersecurity compliance, its architecture serves as a template for compliance in all industries and topics. Given the nature of compliance rollouts occurring annually and sometimes even faster, a critical component of LORE is its design with scale in mind. As new frameworks emerge or organizational needs evolve, additional documents can be encoded and added to the system’s retrieval library, or old ones may be replaced. This makes LORE adaptable across industries, whether in healthcare, defense contracting, or environmental regulation.

Just as importantly, LORE supports transparency, as each compliance classification it produces is paired with reasoning statements, numerical-confidence scores, and direct references to the relevant sections of the regulatory texts. This makes it easy for human reviewers to validate the AI’s conclusions and for organizations to create a clear audit trail. It is a practical way to increase confidence in automated decisions without increasing manual-review time.

Key considerations for business leaders

Business leaders looking to adopt AI and ML tools in regulatory settings should carefully evaluate their current compliance workflows and AI-system reliability. Key considerations include:

• Scalability of workflows: Determine if audits or internal reviews are straining resources and could benefit from targeted AI automation to improve efficiency early on.

• Grounding in real documentation: Ensure the AI system uses retrieval augmented generation, so answers are based on verified source material rather than speculation.

• Trustworthiness of outputs: Verify if the AI’s outputs are reliable enough to act upon, favoring tools that offer transparency, citations, and grounded reasoning for reassurance.

A responsible future for compliance automation

As regulatory environments grow more complex and digital operations continue to scale, AI-enabled compliance tools will become less of a luxury and more of a necessity. The tools that succeed will not try to automate everything but rather understand the value of precision, traceability, and human judgment.

LORE is one example of how that future might appear, as it combines smart automation with trusted documentation and AI-generated classifications with human-led decisions. It does not eliminate the work of compliance; it accelerates it.

For business leaders preparing to modernize their compliance functions, now is the time to explore how architectures like RAG, when paired with human oversight, can become powerful allies in managing risk and staying ahead of regulation.      

Nicholas Hartnett is a software engineer at Quanterion Solutions Inc. Contact him at nhartnett@quanterion.com.