In Governance, Risk, and Compliance (GRC), accuracy is paramount. Whether conducting control tests, assessing policy designs, or responding to questionnaires, we require a high degree of confidence in the information guiding our decisions. This makes the phenomenon of AI “hallucination” – the generation of false or nonsensical information presented with conviction – a critical concern. While AI offers immense potential to revolutionize GRC, its integrity and veracity must be ensured.
What exactly is hallucination? It occurs when an AI confidently fabricates information, including false statements and even invented references. This isn’t merely an annoyance; in the context of expert systems like AI GRC, it’s a serious risk. Unlike general AI interactions, where inaccuracies might be flagged easily, fabricated information from a specialized GRC AI could have significant consequences. Importantly, while humans also exhibit similar behaviors, the goal isn’t to eliminate AI hallucination entirely, but to reduce it to a level lower than that of a human.
So, why does AI hallucinate? Several factors contribute to this issue. Insufficient or incomplete data is a primary culprit. Even with advanced models, a lack of information leads the AI to “fill in the gaps”, potentially introducing inaccuracies. Conversely, heavily biased or inaccurate data—the classic “garbage in, garbage out” scenario—can also lead to flawed outputs. Overfitting, where the model has learned its training data too thoroughly can struggle to generalize to new or unseen information, leading to irrelevant or incorrect responses. Sometimes, misinterpretation of reality due to the AI’s reliance on a modeled world rather than the real one, can also contribute
At Trustero, we address this challenge with a comprehensive strategy we call “The Quad.” This framework encompasses four key pillars:
- Contextualization: Providing the AI with a strong foundation of relevant, detailed data is crucial. This can be achieved through Retrieval Augmented Generation (RAG) and the patented Trust Graph, which helps the model focus on the correct information. Utilizing indexed, categorized data, or even incorporating web search (with careful attention to source trustworthiness) can further enhance contextual understanding.
- Prompting: The way a question is posed dramatically impacts the response. Clear, exhaustive prompts establish context, explain the task, and provide relevant information, minimizing ambiguity and the potential for misinterpretation. Techniques like establishing rewards or punishments within the prompt can also shape the AI’s response.
- Guardrails: Setting boundaries for the AI is vital. This can be done through prompt instructions, pre-defined parameters, or model adjustments. For example, instructing the AI to admit uncertainty when confidence is low, or restricting its access to potentially unreliable data sources, can limit inaccurate outputs.
- Reasoning: Requiring the AI to explain its reasoning and cite its data sources increases transparency and allows for human verification of logic, evidence, and conclusions. This step is critical for establishing trust and mirroring the way we build confidence in human experts.
By consistently applying these principles, organizations can significantly reduce AI hallucination in GRC systems. The combination of a well-contextualized, carefully prompted AI, guided by robust guardrails and transparent reasoning, paired with human oversight, offers a powerful and reliable solution.