Risk management frameworks, and their associated controls, are complex systems that need to process many dynamic risks. To accelerate and enhance that work, risk management teams can use AI — applying AI analysis, ranking, natural language processing, machine learning and other capabilities across vast volumes of data.
Generative AI (GenAI) is a powerful way to harness AI capabilities — and a powerful way to enhance your risk management framework. Risk advisors can use GenAI as another advisor, to help with a range of risk management tasks:
- gap analysis can identify potential risk and control gaps in a Risk & Control Matrix (RCM) by comparing the RCM across an extensive library of other RCMs
- control creation can analyze a user’s risk description to provide multiple options of in-depth control descriptions tailored to address the risk
- control formalization can re-word controls to be more technically sound and comprehensive, if they are written in too brief or casual a manner
- walkthrough question generation can turn a risk and control description into 20 risk-specific and control-specific walkthrough questions, to maximize your understanding of the risk environment in less time
- testing procedure generation can use a control to generate step-by-step testing procedures, along with the evidence required for each step’s completion
- research by inquiry allows users to ask plain-language questions about uploaded documents to easily analyze scenarios, like whether an existing policy satisfies a specific requirement
GenAI capabilities can augment a team's existing subject matter knowledge, empowering better and more efficient risk and compliance management.
However, GenAI comes with its own unique risks.
That’s why it’s essential to apply GenAI with a strategic approach that harmonizes AI capabilities with data security, contextual precision and tailored output. To achieve this harmony, you need to consider five key factors:
- Control system and user inputs
- Refine prompts with corporate data
- Ensure data security
- Refine GenAI outputs
- Share success stories
These factors can help risk teams successfully implement, maintain and optimize the power of GenAI in risk management.
1. Control system and user inputs
“You need to develop tools or processes that limit system and user inputs to specific, well-defined pieces of information.”
Controlled integration begins with controlled inputs. “You need to develop tools or processes that limit system and user inputs to specific, well-defined pieces of information,” said Grant Thornton Risk Advisory Managing Director Greg Haberer.
By requesting discrete data points, like the purpose of a SOX internal control, you can engineer your system’s GenAI responses to be contextually precise, consistent and directly aligned with organizational objectives. This helps align the GenAI capabilities with targeted problem-solving and informed decision-making.
2. Refine prompts with corporate data
“This is the art of crafting backend prompts that seamlessly integrate inputs and corporate data, and it’s a cornerstone of effective GenAI interaction.”
Controlled inputs can help ensure comprehensive and consistent context, but then your system needs to facilitate the synergy of context and data. “This is the art of crafting backend prompts that seamlessly integrate inputs and corporate data, and it’s a cornerstone of effective GenAI interaction,” said
By formulating precise prompts that amalgamate user-defined queries with financial records, compliance documents, and historical data, AI-generated content gains depth and relevance. This synergy improves your GenAI solution's understanding of contextual intricacies, leading to insightful outputs.
3. Ensure data security
You must not allow any system to use your corporate data unless it protects your corporate data. Cybersecurity and data privacy stand as paramount concerns in GenAI integration.
GenAI uses structures called large language models (LLMs) to process language. The LLM for your GenAI solution must be hosted in a dedicated cloud environment, to safeguard sensitive corporate information from potential breaches. By understanding unique AI risks and prioritizing data security, your organization can harness GenAI capabilities without compromising the confidentiality of its data.
4. Refine GenAI outputs
“Incorporate an iterative process of expert review, to refine AI-generated content so that it aligns seamlessly with your corporate messaging and guidelines.”
Tuning your system is important, and tuning your GenAI solutions with your secured corporate data will yield the best results. However, there are other ways to help those results improve over time.
“Incorporate an iterative process of expert review, to refine AI-generated content, prompts and tuning data, so that it aligns seamlessly with your corporate messaging and guidelines,” Haberer said. This process can foster a harmonious blend of GenAI capabilities and human expertise. Expert-reviewed GenAI content can becomes an asset that enhances the risk program’s communication standards and adds a new level of value.
5. Share success stories
Real-world instances have already demonstrated the tangible benefits of controlled GenAI integration into risk management frameworks. “We’ve embraced this approach and achieved improved operational efficiency and decision making,” Rojhani said.
Share your success stories within your organization, to help build on the understanding, support and ultimate value achieved from your risk management team’s GenAI solution. Your solution can continue to improve over time, but its transformative power within your organization could be limited unless you foster an understanding that can drive broader adoption and advancement.
Advance with confidence
GenAI technology is still an emerging landscape but, when you integrate GenAI into your risk management framework in a careful and controlled way, you can navigate that landscape with confidence.
Form a structured approach that prioritizes privacy, precision and expertise. Recognize the value of integrating GenAI capabilities with specialists who provide analysis and review.
The integration of controlled GenAI technology within risk ecosystems is a pivotal juncture in the evolution of modern risk and compliance programs. With the right approach, these programs can harness the full potential of GenAI while safeguarding information and maximizing business benefits.
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