HHow to Manage Generative AI Risk Against its Benefits

The opportunities and benefits of harnessing artificial intelligence for your business are undeniable. However, when considering the use of generative AI for your business, AI risk management, both known and unknown, need to be understood and assessed to be managed proactively. This article will help you learn how governance, risk and compliance software, also known as GRC software, can manage GenAI risk.

Like any powerful new technology, GenAI is being used across departments and industries. Some functions, like marketing chatbots, have already deployed GenAI in ways we can see. But the reality is that a wide range of departments, from customer service to IT to engineering, are also experimenting with it. To deploy GenAI safely and grant access across your organization, GRC software operates as a critical tool in your toolbox. GRC tools are already made to monitor and manage potential risks, especially when it comes to GenAI risks associated with:

• Cybersecurity
• Privacy
• Compliance
• Customer and partner relationships
• Ethical and legal obligations

Tools like Open Source’s ChatGPT and Midjourney make GenAI accessible everywhere, but there isn’t a “one size-fits-all” solution for managing AI risk. Each industry has its own unique set of requirements, and each specific use case has its own risks. These concerns reinforce the need for AI governance, strong human oversight and secure controls.This is precisely when a reliable GRC software solution is essential to scale and adapt to shifting business needs.

Managing GenAI Risk in Your GRC Program

Regulatory compliance already plays a critical role in policies and controls related to cybersecurity, fair use, transparency, ethical adherence, data privacy, IP infringement and others. Formalized guidance can come from risk management frameworks, too, like NIST AI RMF Core. Auditors also contribute significantly to the launch of GenAI risk management programs. That said, it’s up to your broader AI risk management framework to:

• Develop and implement guidelines that provide greater transparency
• Assess risk for a range of use cases
• Ensure data privacy
• Adhere to current security requirements

Let’s look at those four key areas of GenAI risk management and how organizations can address them to manage risk as part of a greater GRC management program.age risk as part of a greater GRC management program.

1. Transparency

Most commercial GenAI products are not transparent, meaning that many GenAI companies aim to protect their AI algorithms as intellectual property. Thus, GenAI tools often function as “black boxes,” making it difficult for customers to audit them. Users likely don’t know what foundational data was used to “train” the system or how it’s processed. The output from GenAI tools and AI-powered products needs to be judged for data integrity against standards for fairness, bias and regulatory compliance.

Some organizations might use black box techniques to audit these AI systems. However, in black box audits, testing methodologies like equivalence partitioning, boundary value analysis and other forms of functional testing are used to infer behaviors without knowing the underlying processes. In contrast, audits of transparent systems can use white box testing techniques, such as static code analysis and unit testing, which rely on access to the internal code and structures.

Essentially, a black box audit is more about evaluating what the system does rather than how it does it, which can limit the depth of a full audit.

2. Risk

We already know that there are external and internal risks associated with the rise of GenAI that must be assessed and addressed. According to a PwC survey, executive respondents cite data breaches and cybersecurity attacks as the top two areas of AI concern. Threat actors are already using GenAI in phishing attacks to make them more effective, and this will continue to evolve. This trend will likely accelerate the convergence of GRC with cybersecurity.

Because of this uptick in malicious actors, AI technologies and machine learning systems are subject to increasing regulation. This means that organizations must ensure that their AI systems comply with relevant laws and industry standards to avoid legal penalties and reputational damage.

Reputational damage can also come as a result of using AI content that is prone to hallucinations and AI biases. AI models can inadvertently perpetuate or amplify algorithmic biases or inaccuracies present in biased training data. There is still room for error with AI using predictive analytics models. That’s why it’s important to continuously monitor and test AI outputs for bias or unexpected behaviors and take steps to mitigate any unfair or false outcomes.

There are also ethical standards for AI safety considerations to keep in mind. Whether intentional or inadvertent, issues such as invasion of privacy, intellectual property issues, copyright violations, IP infringement or other unethical outcomes are part and parcel with GenAI.

Internally, organizations need to control GenAI risks and ascertain trustworthy AI systems by using their GRC software to set up governance policies, assess risks, understand the data being used and protect it.

3. Data Privacy

You’ve already experienced the power of personal digitized data if you’ve ever visited a website and suddenly see its advertising at every turn. GenAI promises to turbocharge this phenomenon. Generative AI systems often require large amounts of data from which to learn and regurgitate. That data can include sensitive or personal information that was never intended to be used in this way.

Additionally, advanced AI systems can use “scraping” AI algorithms to capture photos and other visual content and use it for unintended purposes. This adds fire to mounting privacy concerns. Organizations must ensure that their GenAI is not accessing personal data without authorization. Otherwise, they will run afoul of regulations and anyone impacted by its abuse, including employees, customers and partners, particularly when it comes to GDPR or HIPAA. Many also look to Service Organization Control 2 (SOC 2) to establish robust information security practices against data theft. It’s another way to build customer credibility and public trust as you shield against adversarial attacks, unauthorized access and vulnerabilities.

Risk managers should initiate proactive measures now, if they haven’t already. Before deploying generative AI technologies, conduct data privacy impact assessments to identify and mitigate potential privacy risks associated with the processing of personal data and training AI models. This assessment should consider the types of data the AI will process, how data is collected, stored and used, and the potential impacts on individual privacy.

4. D4. Data Security

The perennial “cat and mouse” game between threat actors and their targets promises to be more intense than ever with the advent of GenAI. Security risks have increased because there are a number of attack vectors that can be aided by the technology, including:

• the rapid generation of malware and automated attacks
• data poisoning
• model tampering
• data theft

Cybercriminals have taken advantage of this by creating their own large language models focused on fraud, such as WormGPT and FraudGPT, for malicious use.

At the same time, cybersecurity organizations are embracing GenAI to become smarter and faster at identifying threat actors and preventing attacks. A multi-layered security approach is required to address these threats, including robust data handling practices, continuous monitoring of machine learning systems and the implementation of advanced cybersecurity measures tailored to the specific vulnerabilities of generative AI technologies. AI third-party risk management programs can also safeguard organizations from vulnerabilities that business partners may not have addressed, reducing the potential for collateral damage.

Onspring AI: The Smarter GRC Solution

Organizations need a secure yet intelligent tool to govern not only their GenAI initiatives, but also their broader GRC processes. That’s why we’re constantly innovating to give you a powerful and intuitive GRC platform. And what we’ve come up with is Onspring AI.

Conversational, context-aware and developed from Anthropic’s secure and reliable AI models, Onspring AI serves as your intelligent GRC partner. Unlike legacy business process automation (BPA) tools, Onspring AI learns from your specific application to provide truly tailored assistance. Our platform places a strong focus on governance and privacy to reduce your AI risk, all while automating mundane GRC workflows. Some of Onspring AI’s features include:

• Accelerated Content Creation: Generate documentation quickly and clearly.
  • Predictive Thought Completion: Turn blank fields into productive starting points. Onspring AI intelligently suggests the most likely next words or phrases, reducing time spent on repetitive data entry and speeding up GRC document creation.
  • Text Field Generation: Create or revise long-form content with clarity and consistency for better standardization 
  • Record Creation: Create plans, tasks, contracts and more from simple questions and prompts. Onspring AI can refine and embed relevant values directly into your data fields in real-time, reducing tedious manual processes.
• Intelligent Recommendations: Surface relevant links between controls and policy documents, or let Onspring AI recommend the best suited analyst to handle an issue. This eliminates the need for multiple searches, helping you find information faster.
• Duplicate Detection: Improve your data integrity by identifying redundancies across records and entries. Then take the appropriate actions to maintain clean, consistent data across all GRC activities. 
• Optical Character Recognition (OCR) & Summarization: Read and extract key info from documents automatically. That way, you can understand core information faster, accelerating your business cycle times.
• Prompt Workbench: Provide clear definitions, tone, terminologies and appropriate business context for all results, tailoring your tool to how you do business.

From  smarter recommendations to reduced manual effort, every feature is designed to accelerate work while strengthening security and governance.

Effective GenAI Risk Management Starts with Strong Governance

In addition to security and compliance, managing GenAI risk is about ensuring responsible use across every aspect of the business. Onspring’s AI’s capabilities support that mission. Our platform strengthens GRC processes so you can confidently innovate with AI. Since every organization has different goals, Onspring AI is available by subscription, letting you adopt AI only where it adds meaningful value.

Ready to see what Onspring AI can do for your GRC processes? Check out our guide to preparing your organization for GenAI, or request a conversation today.