Artificial intelligence (AI) is here to stay. Every day, businesses find more activities that can be optimized thanks to the efficiency and effectiveness of this evolving technology. Within marketing, customer service, and even security, the power of information management and AI has advanced the standards for companies in a highly competitive environment.

AI tools have their own set of operational and functional advantages, and their own risks, too. That’s why companies should carefully evaluate the use of AI within their operations, and understand the risks and rewards you assume when adopting this technology.

Society still doesn’t have a consensus on exactly what we mean by the phrase “AI,” so we cannot refer to a single phenomenon. Broadly speaking, however, we can say that artificial intelligence adapts to meet users’ needs by analyzing usage patterns among various data sources or general guidelines within a sea of information.

Platforms such as Trello have already begun to include AI features based on machine learning to predict the recurring activities of their users. Other applications have adapted their business model based on AI technologies such as QuillBot, a rephrasing service powered by natural language processing.

Enterprise risk management can also use these technologies to simplify business processes and use resources effectively.

Is Artificial Intelligence a Game Changer in Risk Management?

It’s more accurate to say that in the fullness of time, AI will be a game-changer — but perhaps not quite yet. AI is changing the game one step at a time.

For example, banks and financial technology (fintech) companies are implementing risk management systems with AI solutions to facilitate decision-making processes, reduce credit risks, and provide financial services tailored to their users through automation and machine learning algorithms. AI’s ability to analyze large amounts of information substantially improves the identification of data relevant for cybersecurity risk management, risk assessment, and accurate business decision-making.

Some specific use cases that have benefited from AI integrated with risk management systems include:

Threats Analysis and Management

Machine learning engines can analyze large amounts of data from various sources. This information generates real-time prediction models that allow risk managers and security teams to address risks quickly. The models are fundamental to develop early warning systems that assure the uninterrupted operation of the organization and the protection of its stakeholders.

Risk Reduction

AI also provides the ability to evaluate unstructured data about risky behaviors or activities in the organization’s operations. AI algorithms can identify patterns of behavior related to past incidents and transpose them as risk predictors.

Fraud Detection

Fraud detection traditionally requires intense analysis processes for financial institutions and insurers. AI systems can substantially decrease the workload of these processes and reduce fraud threats by using machine learning models that focus on text mining, social media analysis, and database searches.

Data Classification

AI tools can also process and classify all available information according to previously defined patterns and categories and monitor access to these data sets.

How to Use Artificial Intelligence in Your Risk Management Plan

Unfortunately, these benefits do not come without risks. When implementing AI technologies, companies must pay special attention to their associated challenges, such as protecting the data collected and used, along with the costs of implementation.

The following procedure can be used to implement AI models within your company, both to reduce “AI risk” and to take advantage of the benefits that these tools can bring to your organization:


The first step to implementing a risk management system supported by AI is to identify the organization’s regulatory and reputational risks. Conduct a risk assessment, based on current frameworks and your company’s organizational values. Use it to determine the data you need to collect and how you want to process that information.

Data Sourcing

Based on previous risk assessments, it is possible to define which data sets are suitable for AI model processing and which ones aren’t. So think carefully about what data to use and where you can source that information. Even at the operational level, choosing the right data sets influences the quality of the results, so data sourcing becomes a crucial step for the implementation of the ecosystem.

Model Development

Once you have useful data, build a useful model. Consider the level of transparency you want in AI operations, since some AI tools aren’t recommended for high-risk activities. Review any regulatory limits on how AI can be used for certain business processes, and how the AI will further business objectives your organization has.


Like other risk management tools, the use of AI must be constantly evaluated and adjusted. It’s critical to consider the changing needs of the organization and the possible drawbacks that this technology may present.

ZenGRC Has Risk Management Solutions

ZenGRC is a governance, risk, and compliance platform that assists you in implementing, managing, and monitoring your risk management framework and remedial assignments.

Its workflow-tagging feature empowers risk management strategies by allowing you to assign tasks for risk assessment, analysis, and mitigation. Duties and tasks can be prioritized so everyone understands what to accomplish and when to do it. Its user-friendly interface simplifies the evaluation of pending and finished activities.

The ServiceNow connection enables communication with ZenGRC in both directions. ZenGRC can assist you in streamlining the administration of all your crucial regulatory compliance frameworks, including PCI, ISO, HIPAA, and others.

Get started on the path to worry-free risk and compliance management. Contact us for a free demo.

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