Governance, risk, and compliance (GRC) professionals are as eager as anyone else to use the increasing capabilities of generative artificial intelligence to make their work as efficient and smooth as possible. But while generative AI offers attractive benefits for risk managers, using AI-augmented GRC platforms can also end up introducing new potential for data privacy breaches.
In this guide, we’ll break down exactly which aspects of GRC can be maintained or even improved with help from large language models and other types of AI. We’ll also cover which risk management tasks are better left to humans who can exercise judgment, understand context and more readily adapt to changing situations– especially as organizations navigate regulatory changes.
How GRC Leaders Can Benefit from AI’s Strengths
So what can an AI-augmented GRC platform offer your organization? You can make the most of AI by focusing your implementation on data collection, risk assessment, data analytics and pattern recognition.
AI software is especially useful for maintaining data quality standards, identifying certain risks, supporting early-stage risk mitigation and spotlighting compliance gaps. But even when it comes to these tasks, you’ll need to ensure continuous monitoring of AI output by humans to avoid unnecessary risk.
Read: The Future of GRC: AI Enabled, Human Led
Identifying Risks and Fraud
Cyber risk professionals will be pleased to hear that AI risk identification programs excel at not only gathering and sorting diverse types of data, but also combing through that data to find anomalies that can indicate fraud or other issues.
Because AI can identify patterns in information and use them to predict future output, AI-augmented GRC software can pinpoint even small deviations from your usual processes to highlight potential fraud, spot signs of criminal activity and forecast developing risks. Moreover, it can perform these tasks at a much greater speed than humans are capable of.
Many AI-augmented risk assessment tools can also produce reports illustrating their findings, making them great for providing polished risk assessment reports to vendors.
Read: Artificial Intelligence and Cybersecurity: A Federal Perspective
Performing Audits and Ensuring Compliance
Of course, those same pattern analysis skills can benefit your organization internally as well as externally. You can easily use AI tools to collect relevant data from across your organization to perform regular checks for any deviations from compliance requirements and regulations.
Not only can AI rapidly perform and package that kind of analysis into a presentation-ready document complete with visualizations, but it can also avoid the kinds of bias that might lead a human employee to overlook a particular risk factor or potential aberration. For example, people tend to come into any situation expecting a specific result, and confirmation bias might lead your employees to assume any data that challenges their assumptions must be inaccurate or irrelevant.
Read: The Practical Applications of Artificial Intelligence in Government Programs
When To Rely on Human Expertise Instead of AI
Don’t take all of this to mean that it’s time to fire your staff and move your entire operation over to artificial intelligence. While AI can make specific tasks like risk modeling significantly simpler and more efficient, this technology can become a risk in and of itself when mishandled.
Choosing the wrong third-party AI tool can expose your organization to potential data breaches, regulatory violations or ethical concerns. Apart from thoroughly vetting any tools you’re considering for data security and regulatory compliance, the best way to avoid incurring these risks is understanding when to rely on the unique talents of your human team members.
Read: Beyond Cybersecurity: What Leaders Overlook About AI Risk
Drawing Conclusions from Data
With specific instructions, AI excels at gathering data and identifying anomalies. But that doesn’t mean these programs comprehend what those numbers and factors mean in context.
One of the biggest weaknesses of even advanced generative AI programs is their relative inability to contextualize data and use it to draw sound and actionable insights. While progressing all the time, modern AI simply isn’t ready to replace the expertise and judgment provided by a team of seasoned human analysts who understand your organization.
When it comes to your clients, this weakness means you can’t rely on AI alone to properly analyze data to pinpoint vendor risks, nor should you blindly trust any AI-generated risk reports.
Read: 4 Best Practices for Managing Generative AI Risk
Communicating Insights
Another weakness of AI in its current incarnation is its failure to emulate so-called soft skills essential to GRC work, such as communicating clearly and accurately, coordinating teams and recognizing situations that require empathy, patience or understanding.
For example, when preparing vendor risk assessments, you might be able to entrust AI with your initial data gathering and organizing efforts. But you won’t be able to delegate responsibilities such as interviewing relevant experts and team members, refining policy drafts or updating stakeholders and clients at key moments.
You’ll certainly want to call in human experts not only to review any AI-prepped reports for accuracy and eliminate potential hallucinations, but also to clearly communicate the most relevant insights for your audience. AI struggles to draw realistic conclusions from data patterns, and it likewise falls short when it comes to conveying data insights with appropriate tone and context.
Tolerating Acceptable Risks
Although every GRC professional’s goal is to monitor and reduce risk, the complex realities of operating any data or cloud service often require tolerating a certain level of acceptable risk. But because AI only understands data through clear and straightforward rubrics, these programs tend to demonstrate little to no risk appetite.
It takes a human mind to truly comprehend the gap between realistic and ideal conditions, the nuances of a particular client’s values and goals, and the difference between an acceptable or unavoidable third-party risk and a genuine and immediate threat to data security.
For example, AI can’t weigh out the pros of working with a certain vendor or tool against the cons of incurring any additional risks they might expose you to. And it can’t incorporate factors that data doesn’t easily reflect, such as a strong working relationship.
This means that until much greater strides are made in the development of artificial intelligence, human expertise is still absolutely essential to third-party risk management specifically, and governance, risk and compliance work in general.
How To Balance the Best of Artificial and Human Intelligence
Like humanity itself, artificial intelligence is in a constant state of growth, learning, and development. To strike the right balance between innovation and responsibility, you and your team will need to not only diligently vet the AI tools you plan to use, but also implement continuous control monitoring to ensure AI data hasn’t been compromised by hallucinations or other errors.
Now that you know exactly which types of tasks can be entrusted to AI and which are better left to your human colleagues, you’re one step closer to ethical AI-supported GRC. If you’re ready to take the next step, download our ebook From Blank Page to GRC Ready: How AI is Accelerating Documentation, Standardization and Compliance Review.
